DEVCOM-ARL Grant for High-Throughput Materials Discovery for Extreme Conditions

**Full proposals must by submitted by the closing date of 12 November 2021. Only those white papers invited to submit a full proposal may submit.**

**An amended version of the FOA was posted on 23 August 2021. Note the revised White Paper submission date of 10 September 2021. All White Paper submissions must be submitted to the following email addresses:

usarmy.rtp.devcom-arl.mbx.baa3qa@mail.mil AND

usarmy.rtp.devcom-arl.mbx.baa3qa@army.mil**

**As of 15 July 2021, this announcement has been updated to include the official FOA, to include submission dates and instructions. Please note that White Papers must be submitted to the email address listed in the announcement by 3:00PM (local time in North Carolina, USA) on 31 August 2021. There will be an Applicant’s Day presentation conducted by the Government on 29 July 2021. To register for this event, please visit https://www.eventbrite.com/e/htmdec-applicants-day-2021-registration-162829949763. Additional information regarding this program and the Applicant’s Day can be found at https://www.arl.army.mil/htmdec/.**

***The 14 May 2021 version of this announcement is posted in a pre-release stage. The Government is currently seeking feedback from industry and the university/non-profit community on the contents of the announcement prior to releasing the official Funding Opportunity Announcement. The official announcement is expected to be released in June 2021. Until that announcement is released, the Government will be accepting feedback via email at:

usarmy.rtp.devcom-arl.mbx.baa3qa@mail.mil

We appreciate any and all feedback during this pre-release stage.***

Within the Army science and technology enterprise, DEVCOM-ARL is chartered to conduct disruptive foundational research, engage as the Army’s primary collaborative link to the scientific community, and interface to shape future fighting concepts. We crystalize these ideas and the impetus to perform these functions at the pace of innovation as ‘Operationalize Science for Transformational Overmatch’. Simply put, we seek to accelerate discovery and transition breakthroughs to the Warfighter faster than anyone else.

Artificial Intelligence (AI) (rule-based and Machine Learning (ML), together) presents powerful new tools for exploring an information landscape in discovering novel materials for applications in extreme conditions (e.g. high-strain rate, high-g loading, high temperature). Such approaches present considerable opportunity in exploring new frontiers for materials used in ballistic applications, especially when coupled with new approaches that allow larger and richer datasets, computational tools, and data infrastructure for collaboration. Broadly, AI/ML can be used to augment individual steps in the synthesis-processing-characterization pipeline, be used for scale-bridging to draw greater information from more tractable experimental approaches, and be used to guide a broader research loop.

Advances in synthesis, modeling, and characterization will greatly advance our ability to exploit monolithic materials in extreme conditions. However, there is a need to contemplate how the capabilities of additive manufacturing and other processing techniques can be used to evaluate materials that exhibit spatial variations in composition, anisotropic characteristics, and contain interfaces between multiple materials. The parameter space expands exponentially as these variables compound the system inputs, but truly advanced materials performance will likely be dependent on an integrated systems-level approach to materials design. 

Application of ML toolsets is viewed as necessary to achieve accelerated discovery of new materials for application in extreme dynamic (impact, thermal, ablative) conditions.  ML toolsets and software exist but may need to be adapted for the specific requirements of materials discovery and design. Full exploitation of the ML approach will certainly require extension and further development to focus on proof-of-concept for material classes of interest in ballistic application. This could be achieved within a generalized and scalable framework that supports rapid, robust and trusted data exchange. New tools to consolidate data, and improved high-throughput workflow will require specialized approaches to transient phenomena e.g. shocks, heating, localized deformation, and failure.  ML models that incorporate these phenomena will critically rely on physics-based models that target key mechanisms. Critical (targeted by ML approaches) physics models may require further development; ML offers opportunity to consolidate much of these physics into fast-running analytic frameworks compatible with the high-throughput approach and may be used to guide autonomous systems for high-throughput characterization of transient phenomena.

To accelerate improvements in Army armor and weapon system performance, DEVCOM-ARL wants to leverage high-throughput methods in synthesis, processing, characterization, and modeling for materials used in these applications. Machine-learning techniques are in the nascent stage of integration with materials science but may present a path towards accelerated discovery, as these tools may uncover novel links between system performance and material science that have been previously underdeveloped or overlooked. DEVCOM-ARL seeks collaboration with external investigators to leverage (and train experts on) machine-learning techniques in the discovery of materials that perform in extreme environments, but machine-learning techniques require large volumes of quantifiable data in order to best reveal links between the materials science and system performance. High throughput techniques may present a viable approach to satisfy the data volume requirements to bring machine-learning to bear.

In summary, the US Army Modernization Priorities require materials that survive and perform in extreme environments; harsh military environments of high-acceleration (e.g. projectile launch and flight), high-temperature and rapid ablation (e.g. hypersonic flight), and impacts at very high velocity (terminal ballistics). The totality of these environments and accumulating requirements on future materials drives the imperative to consider an increasingly large number of constituent elements, structure and properties. Discovery must now parse through billions of candidate materials to achieve highly specialized and transformational functions. This drives a data-driven approach; one that fuses high-throughput materials synthesis and characterization with machine learning algorithms and close-loop discovery automation.

The overarching goal of this program is to develop the necessary methodologies, models, algorithms, synthesis & processing techniques, and requisite characterization and testing to rapidly accelerate the discovery of novel materials for extreme conditions. As such, it is expected the results of this program will be novel materials exhibiting unprecedented properties that have been developed utilizing all of the aforementioned tools which will be provided to DEVCOM-ARL for further analysis and testing.   

In order to achieve this paradigm shift in materials discovery, significant advances are needed in the following thrust areas:

• Data-driven Material Design – meant to be a comprehensive term for all aspects of the material design phase of the material development cycle which are accelerated through the integration of computational methods.
• High-Throughput Synthesis & Processing – to include both modifying existing synthesis & processing methods to accommodate for high-throughput, as well as developing novel techniques.
• High-Throughput Characterization – to include implementation of automation, as well as development of surrogate tests to mimic high-strain techniques which are not amenable to automation.
• ML-augmented Physics-Based Models – the integration of physics-based models with machine learning is poised to be a tipping point in materials science. To date, nearly all ML algorithms have been developed for big data (e.g. image recognition). It is critical that we discontinue ‘repurposing’ these types of algorithms and begin developing ML algorithms specifically designed for materials discovery, and informed by physics.

HTMDEC has been developed in coordination with other related ARL-funded collaborative efforts (see descriptions of ARL collaborative alliances at https://www.arl.army.mil/www/default.cfm?page=93) and shares a common vision of highly collaborative academia-industry-government partnerships. However, HTMDEC will be executed with a program model different than previous ARL Collaborative Research/Technology Alliances. Specific components of the program are highlighted below:

• HTMDEC will be a two-step application process, consisting of a White paper stage and a Proposal stage.

• HTMDEC will be executed through two funding periods, herein referred to as “Seedling awards” and “Centers”. A Center will be an option period exercised from a seedling award. The only exception to this will be for the FY2022 Targeted Thrust Area FY2022-2 – Data Handling & Management. The Seedling selected for continuation for this particular thrust area will do so as a recurring Seedling for the duration of the HTMDEC program, and will support all of the Centers as the approved data platform for HTMDEC.

• A FOA Opportunity workshop will be held to brief interested Applicants on the long term program goals of this FOA. In FY2022, only seedling efforts will be awarded. The focus of these seedlings will be to address either one of the FY2022 targeted thrust areas, or one of the primary thrust areas. 

• White papers will address one of the particular thrust areas; thrust areas may change on an annual basis in order to reflect current interests. White papers will be evaluated. Applicants with only the most highly rated white papers will receive an invitation from the Government to submit a Proposal.

• Proposals will address one of the thrust areas. Proposals will be evaluated and funding will be provided to those Recipients selected for award of a cooperative agreement (CA) described as the “seedling” award.

• Prior to the close of the seedling award, the Recipients of a “seedling” CA are then eligible to submit a proposal for consideration of an option period under the seedling CA of up to 4 years. This option period will be a Center. Since the option proposal will need to address all four thrust areas, seedling Recipients will be encouraged to collaborate and combine during the option period of performance so as to build the strongest possible Center moving forward. Option proposals will be evaluated and funding will be provided to those Recipients selected for the exercise of their option.  

• In addition to the option proposal addressing all four thrust areas, Recipients will also have the opportunity to provide for a Graduate Student Fellowship effort for US citizens working in one of the thrust areas.

• Project review workshops will be held annually, with the intention of allowing all CA Recipients to present the results of their research, as well as interacting with the other Recipients.  

**An amended version of the FOA was posted on 23 August 2021. Note the revised White Paper submission date of 10 September 2021. All White Paper submissions must be submitted to the following email addresses:

usarmy.rtp.devcom-arl.mbx.baa3qa@mail.mil AND

usarmy.rtp.devcom-arl.mbx.baa3qa@army.mil**

**As of 15 July 2021, this announcement has been updated to include the official FOA, to include submission dates and instructions. Please note that White Papers must be submitted to the email address listed in the announcement by 3:00PM (local time in North Carolina, USA) on 31 August 2021. There will be an Applicant’s Day presentation conducted by the Government on 29 July 2021. To register for this event, please visit https://www.eventbrite.com/e/htmdec-applicants-day-2021-registration-162829949763. Additional information regarding this program and the Applicant’s Day can be found at https://www.arl.army.mil/htmdec/.**

***The 14 May 2021 version of this announcement is posted in a pre-release stage. The Government is currently seeking feedback from industry and the university/non-profit community on the contents of the announcement prior to releasing the official Funding Opportunity Announcement. The official announcement is expected to be released in June 2021. Until that announcement is released, the Government will be accepting feedback via email at:

usarmy.rtp.devcom-arl.mbx.baa3qa@mail.mil

We appreciate any and all feedback during this pre-release stage.***

Within the Army science and technology enterprise, DEVCOM-ARL is chartered to conduct disruptive foundational research, engage as the Army’s primary collaborative link to the scientific community, and interface to shape future fighting concepts. We crystalize these ideas and the impetus to perform these functions at the pace of innovation as ‘Operationalize Science for Transformational Overmatch’. Simply put, we seek to accelerate discovery and transition breakthroughs to the Warfighter faster than anyone else.

Artificial Intelligence (AI) (rule-based and Machine Learning (ML), together) presents powerful new tools for exploring an information landscape in discovering novel materials for applications in extreme conditions (e.g. high-strain rate, high-g loading, high temperature). Such approaches present considerable opportunity in exploring new frontiers for materials used in ballistic applications, especially when coupled with new approaches that allow larger and richer datasets, computational tools, and data infrastructure for collaboration. Broadly, AI/ML can be used to augment individual steps in the synthesis-processing-characterization pipeline, be used for scale-bridging to draw greater information from more tractable experimental approaches, and be used to guide a broader research loop.

Advances in synthesis, modeling, and characterization will greatly advance our ability to exploit monolithic materials in extreme conditions. However, there is a need to contemplate how the capabilities of additive manufacturing and other processing techniques can be used to evaluate materials that exhibit spatial variations in composition, anisotropic characteristics, and contain interfaces between multiple materials. The parameter space expands exponentially as these variables compound the system inputs, but truly advanced materials performance will likely be dependent on an integrated systems-level approach to materials design. 

Application of ML toolsets is viewed as necessary to achieve accelerated discovery of new materials for application in extreme dynamic (impact, thermal, ablative) conditions.  ML toolsets and software exist but may need to be adapted for the specific requirements of materials discovery and design. Full exploitation of the ML approach will certainly require extension and further development to focus on proof-of-concept for material classes of interest in ballistic application. This could be achieved within a generalized and scalable framework that supports rapid, robust and trusted data exchange. New tools to consolidate data, and improved high-throughput workflow will require specialized approaches to transient phenomena e.g. shocks, heating, localized deformation, and failure.  ML models that incorporate these phenomena will critically rely on physics-based models that target key mechanisms. Critical (targeted by ML approaches) physics models may require further development; ML offers opportunity to consolidate much of these physics into fast-running analytic frameworks compatible with the high-throughput approach and may be used to guide autonomous systems for high-throughput characterization of transient phenomena.

To accelerate improvements in Army armor and weapon system performance, DEVCOM-ARL wants to leverage high-throughput methods in synthesis, processing, characterization, and modeling for materials used in these applications. Machine-learning techniques are in the nascent stage of integration with materials science but may present a path towards accelerated discovery, as these tools may uncover novel links between system performance and material science that have been previously underdeveloped or overlooked. DEVCOM-ARL seeks collaboration with external investigators to leverage (and train experts on) machine-learning techniques in the discovery of materials that perform in extreme environments, but machine-learning techniques require large volumes of quantifiable data in order to best reveal links between the materials science and system performance. High throughput techniques may present a viable approach to satisfy the data volume requirements to bring machine-learning to bear.

In summary, the US Army Modernization Priorities require materials that survive and perform in extreme environments; harsh military environments of high-acceleration (e.g. projectile launch and flight), high-temperature and rapid ablation (e.g. hypersonic flight), and impacts at very high velocity (terminal ballistics). The totality of these environments and accumulating requirements on future materials drives the imperative to consider an increasingly large number of constituent elements, structure and properties. Discovery must now parse through billions of candidate materials to achieve highly specialized and transformational functions. This drives a data-driven approach; one that fuses high-throughput materials synthesis and characterization with machine learning algorithms and close-loop discovery automation.

The overarching goal of this program is to develop the necessary methodologies, models, algorithms, synthesis & processing techniques, and requisite characterization and testing to rapidly accelerate the discovery of novel materials for extreme conditions. As such, it is expected the results of this program will be novel materials exhibiting unprecedented properties that have been developed utilizing all of the aforementioned tools which will be provided to DEVCOM-ARL for further analysis and testing.   

In order to achieve this paradigm shift in materials discovery, significant advances are needed in the following thrust areas:

• Data-driven Material Design – meant to be a comprehensive term for all aspects of the material design phase of the material development cycle which are accelerated through the integration of computational methods.
• High-Throughput Synthesis & Processing – to include both modifying existing synthesis & processing methods to accommodate for high-throughput, as well as developing novel techniques.
• High-Throughput Characterization – to include implementation of automation, as well as development of surrogate tests to mimic high-strain techniques which are not amenable to automation.
• ML-augmented Physics-Based Models – the integration of physics-based models with machine learning is poised to be a tipping point in materials science. To date, nearly all ML algorithms have been developed for big data (e.g. image recognition). It is critical that we discontinue ‘repurposing’ these types of algorithms and begin developing ML algorithms specifically designed for materials discovery, and informed by physics.

HTMDEC has been developed in coordination with other related ARL-funded collaborative efforts (see descriptions of ARL collaborative alliances at https://www.arl.army.mil/www/default.cfm?page=93) and shares a common vision of highly collaborative academia-industry-government partnerships. However, HTMDEC will be executed with a program model different than previous ARL Collaborative Research/Technology Alliances. Specific components of the program are highlighted below:

• HTMDEC will be a two-step application process, consisting of a White paper stage and a Proposal stage.

• HTMDEC will be executed through two funding periods, herein referred to as “Seedling awards” and “Centers”. A Center will be an option period exercised from a seedling award. The only exception to this will be for the FY2022 Targeted Thrust Area FY2022-2 – Data Handling & Management. The Seedling selected for continuation for this particular thrust area will do so as a recurring Seedling for the duration of the HTMDEC program, and will support all of the Centers as the approved data platform for HTMDEC.

• A FOA Opportunity workshop will be held to brief interested Applicants on the long term program goals of this FOA. In FY2022, only seedling efforts will be awarded. The focus of these seedlings will be to address either one of the FY2022 targeted thrust areas, or one of the primary thrust areas. 

• White papers will address one of the particular thrust areas; thrust areas may change on an annual basis in order to reflect current interests. White papers will be evaluated. Applicants with only the most highly rated white papers will receive an invitation from the Government to submit a Proposal.

• Proposals will address one of the thrust areas. Proposals will be evaluated and funding will be provided to those Recipients selected for award of a cooperative agreement (CA) described as the “seedling” award.

• Prior to the close of the seedling award, the Recipients of a “seedling” CA are then eligible to submit a proposal for consideration of an option period under the seedling CA of up to 4 years. This option period will be a Center. Since the option proposal will need to address all four thrust areas, seedling Recipients will be encouraged to collaborate and combine during the option period of performance so as to build the strongest possible Center moving forward. Option proposals will be evaluated and funding will be provided to those Recipients selected for the exercise of their option.  

• In addition to the option proposal addressing all four thrust areas, Recipients will also have the opportunity to provide for a Graduate Student Fellowship effort for US citizens working in one of the thrust areas.

• Project review workshops will be held annually, with the intention of allowing all CA Recipients to present the results of their research, as well as interacting with the other Recipients.  

**As of 15 July 2021, this announcement has been updated to include the official FOA, to include submission dates and instructions.  Please note that White Papers must be submitted to the email address listed in the announcement by 3:00PM (local time in North Carolina, USA) on 31 August 2021.  There will be an Applicant’s Day presentation conducted by the Government on 29 July 2021.  To register for this event, please visit https://www.eventbrite.com/e/htmdec-applicants-day-2021-registration-162829949763.  Additional information regarding this program and the Applicant’s Day can be found at https://www.arl.army.mil/htmdec/.**

***The 14 May 2021 version of this announcement is posted in a pre-release stage. The Government is currently seeking feedback from industry and the university/non-profit community on the contents of the announcement prior to releasing the official Funding Opportunity Announcement. The official announcement is expected to be released in June 2021. Until that announcement is released, the Government will be accepting feedback via email at:

usarmy.rtp.devcom-arl.mbx.baa3qa@mail.mil

We appreciate any and all feedback during this pre-release stage.***

Within the Army science and technology enterprise, DEVCOM-ARL is chartered to conduct disruptive foundational research, engage as the Army’s primary collaborative link to the scientific community, and interface to shape future fighting concepts. We crystalize these ideas and the impetus to perform these functions at the pace of innovation as ‘Operationalize Science for Transformational Overmatch’. Simply put, we seek to accelerate discovery and transition breakthroughs to the Warfighter faster than anyone else.

Artificial Intelligence (AI) (rule-based and Machine Learning (ML), together) presents powerful new tools for exploring an information landscape in discovering novel materials for applications in extreme conditions (e.g. high-strain rate, high-g loading, high temperature). Such approaches present considerable opportunity in exploring new frontiers for materials used in ballistic applications, especially when coupled with new approaches that allow larger and richer datasets, computational tools, and data infrastructure for collaboration. Broadly, AI/ML can be used to augment individual steps in the synthesis-processing-characterization pipeline, be used for scale-bridging to draw greater information from more tractable experimental approaches, and be used to guide a broader research loop.

Advances in synthesis, modeling, and characterization will greatly advance our ability to exploit monolithic materials in extreme conditions. However, there is a need to contemplate how the capabilities of additive manufacturing and other processing techniques can be used to evaluate materials that exhibit spatial variations in composition, anisotropic characteristics, and contain interfaces between multiple materials. The parameter space expands exponentially as these variables compound the system inputs, but truly advanced materials performance will likely be dependent on an integrated systems-level approach to materials design. 

Application of ML toolsets is viewed as necessary to achieve accelerated discovery of new materials for application in extreme dynamic (impact, thermal, ablative) conditions.  ML toolsets and software exist but may need to be adapted for the specific requirements of materials discovery and design. Full exploitation of the ML approach will certainly require extension and further development to focus on proof-of-concept for material classes of interest in ballistic application. This could be achieved within a generalized and scalable framework that supports rapid, robust and trusted data exchange. New tools to consolidate data, and improved high-throughput workflow will require specialized approaches to transient phenomena e.g. shocks, heating, localized deformation, and failure.  ML models that incorporate these phenomena will critically rely on physics-based models that target key mechanisms. Critical (targeted by ML approaches) physics models may require further development; ML offers opportunity to consolidate much of these physics into fast-running analytic frameworks compatible with the high-throughput approach and may be used to guide autonomous systems for high-throughput characterization of transient phenomena.

To accelerate improvements in Army armor and weapon system performance, DEVCOM-ARL wants to leverage high-throughput methods in synthesis, processing, characterization, and modeling for materials used in these applications. Machine-learning techniques are in the nascent stage of integration with materials science but may present a path towards accelerated discovery, as these tools may uncover novel links between system performance and material science that have been previously underdeveloped or overlooked. DEVCOM-ARL seeks collaboration with external investigators to leverage (and train experts on) machine-learning techniques in the discovery of materials that perform in extreme environments, but machine-learning techniques require large volumes of quantifiable data in order to best reveal links between the materials science and system performance. High throughput techniques may present a viable approach to satisfy the data volume requirements to bring machine-learning to bear.

In summary, the US Army Modernization Priorities require materials that survive and perform in extreme environments; harsh military environments of high-acceleration (e.g. projectile launch and flight), high-temperature and rapid ablation (e.g. hypersonic flight), and impacts at very high velocity (terminal ballistics). The totality of these environments and accumulating requirements on future materials drives the imperative to consider an increasingly large number of constituent elements, structure and properties. Discovery must now parse through billions of candidate materials to achieve highly specialized and transformational functions. This drives a data-driven approach; one that fuses high-throughput materials synthesis and characterization with machine learning algorithms and close-loop discovery automation.

The overarching goal of this program is to develop the necessary methodologies, models, algorithms, synthesis & processing techniques, and requisite characterization and testing to rapidly accelerate the discovery of novel materials for extreme conditions. As such, it is expected the results of this program will be novel materials exhibiting unprecedented properties that have been developed utilizing all of the aforementioned tools which will be provided to DEVCOM-ARL for further analysis and testing.   

In order to achieve this paradigm shift in materials discovery, significant advances are needed in the following thrust areas:

• Data-driven Material Design – meant to be a comprehensive term for all aspects of the material design phase of the material development cycle which are accelerated through the integration of computational methods.
• High-Throughput Synthesis & Processing – to include both modifying existing synthesis & processing methods to accommodate for high-throughput, as well as developing novel techniques.
• High-Throughput Characterization – to include implementation of automation, as well as development of surrogate tests to mimic high-strain techniques which are not amenable to automation.
• ML-augmented Physics-Based Models – the integration of physics-based models with machine learning is poised to be a tipping point in materials science. To date, nearly all ML algorithms have been developed for big data (e.g. image recognition). It is critical that we discontinue ‘repurposing’ these types of algorithms and begin developing ML algorithms specifically designed for materials discovery, and informed by physics.

HTMDEC has been developed in coordination with other related ARL-funded collaborative efforts (see descriptions of ARL collaborative alliances at https://www.arl.army.mil/www/default.cfm?page=93) and shares a common vision of highly collaborative academia-industry-government partnerships. However, HTMDEC will be executed with a program model different than previous ARL Collaborative Research/Technology Alliances. Specific components of the program are highlighted below:

• HTMDEC will be a two-step application process, consisting of a White paper stage and a Proposal stage.

• HTMDEC will be executed through two funding periods, herein referred to as “Seedling awards” and “Centers”. A Center will be an option period exercised from a seedling award. The only exception to this will be for the FY2022 Targeted Thrust Area FY2022-2 – Data Handling & Management. The Seedling selected for continuation for this particular thrust area will do so as a recurring Seedling for the duration of the HTMDEC program, and will support all of the Centers as the approved data platform for HTMDEC.

• A FOA Opportunity workshop will be held to brief interested Applicants on the long term program goals of this FOA. In FY2022, only seedling efforts will be awarded. The focus of these seedlings will be to address either one of the FY2022 targeted thrust areas, or one of the primary thrust areas. 

• White papers will address one of the particular thrust areas; thrust areas may change on an annual basis in order to reflect current interests. White papers will be evaluated. Applicants with only the most highly rated white papers will receive an invitation from the Government to submit a Proposal.

• Proposals will address one of the thrust areas. Proposals will be evaluated and funding will be provided to those Recipients selected for award of a cooperative agreement (CA) described as the “seedling” award.

• Prior to the close of the seedling award, the Recipients of a “seedling” CA are then eligible to submit a proposal for consideration of an option period under the seedling CA of up to 4 years. This option period will be a Center. Since the option proposal will need to address all four thrust areas, seedling Recipients will be encouraged to collaborate and combine during the option period of performance so as to build the strongest possible Center moving forward. Option proposals will be evaluated and funding will be provided to those Recipients selected for the exercise of their option.  

• In addition to the option proposal addressing all four thrust areas, Recipients will also have the opportunity to provide for a Graduate Student Fellowship effort for US citizens working in one of the thrust areas.

• Project review workshops will be held annually, with the intention of allowing all CA Recipients to present the results of their research, as well as interacting with the other Recipients.  

***The 14 May 2021 version of this announcement is posted in a pre-release stage. The Government is currently seeking feedback from industry and the university/non-profit community on the contents of the announcement prior to releasing the official Funding Opportunity Announcement. The official announcement is expected to be released in June 2021. Until that announcement is released, the Government will be accepting feedback via email at:

usarmy.rtp.devcom-arl.mbx.baa3qa@mail.mil

We appreciate any and all feedback during this pre-release stage.***

Within the Army science and technology enterprise, DEVCOM-ARL is chartered to conduct disruptive foundational research, engage as the Army’s primary collaborative link to the scientific community, and interface to shape future fighting concepts. We crystalize these ideas and the impetus to perform these functions at the pace of innovation as ‘Operationalize Science for Transformational Overmatch’. Simply put, we seek to accelerate discovery and transition breakthroughs to the Warfighter faster than anyone else.

Artificial Intelligence (AI) (rule-based and Machine Learning (ML), together) presents powerful new tools for exploring an information landscape in discovering novel materials for applications in extreme conditions (e.g. high-strain rate, high-g loading, high temperature). Such approaches present considerable opportunity in exploring new frontiers for materials used in ballistic applications, especially when coupled with new approaches that allow larger and richer datasets, computational tools, and data infrastructure for collaboration. Broadly, AI/ML can be used to augment individual steps in the synthesis-processing-characterization pipeline, be used for scale-bridging to draw greater information from more tractable experimental approaches, and be used to guide a broader research loop.

Advances in synthesis, modeling, and characterization will greatly advance our ability to exploit monolithic materials in extreme conditions. However, there is a need to contemplate how the capabilities of additive manufacturing and other processing techniques can be used to evaluate materials that exhibit spatial variations in composition, anisotropic characteristics, and contain interfaces between multiple materials. The parameter space expands exponentially as these variables compound the system inputs, but truly advanced materials performance will likely be dependent on an integrated systems-level approach to materials design. 

Application of ML toolsets is viewed as necessary to achieve accelerated discovery of new materials for application in extreme dynamic (impact, thermal, ablative) conditions.  ML toolsets and software exist but may need to be adapted for the specific requirements of materials discovery and design. Full exploitation of the ML approach will certainly require extension and further development to focus on proof-of-concept for material classes of interest in ballistic application. This could be achieved within a generalized and scalable framework that supports rapid, robust and trusted data exchange. New tools to consolidate data, and improved high-throughput workflow will require specialized approaches to transient phenomena e.g. shocks, heating, localized deformation, and failure.  ML models that incorporate these phenomena will critically rely on physics-based models that target key mechanisms. Critical (targeted by ML approaches) physics models may require further development; ML offers opportunity to consolidate much of these physics into fast-running analytic frameworks compatible with the high-throughput approach and may be used to guide autonomous systems for high-throughput characterization of transient phenomena.

To accelerate improvements in Army armor and weapon system performance, DEVCOM-ARL wants to leverage high-throughput methods in synthesis, processing, characterization, and modeling for materials used in these applications. Machine-learning techniques are in the nascent stage of integration with materials science but may present a path towards accelerated discovery, as these tools may uncover novel links between system performance and material science that have been previously underdeveloped or overlooked. DEVCOM-ARL seeks collaboration with external investigators to leverage (and train experts on) machine-learning techniques in the discovery of materials that perform in extreme environments, but machine-learning techniques require large volumes of quantifiable data in order to best reveal links between the materials science and system performance. High throughput techniques may present a viable approach to satisfy the data volume requirements to bring machine-learning to bear.

In summary, the US Army Modernization Priorities require materials that survive and perform in extreme environments; harsh military environments of high-acceleration (e.g. projectile launch and flight), high-temperature and rapid ablation (e.g. hypersonic flight), and impacts at very high velocity (terminal ballistics). The totality of these environments and accumulating requirements on future materials drives the imperative to consider an increasingly large number of constituent elements, structure and properties. Discovery must now parse through billions of candidate materials to achieve highly specialized and transformational functions. This drives a data-driven approach; one that fuses high-throughput materials synthesis and characterization with machine learning algorithms and close-loop discovery automation.

The overarching goal of this program is to develop the necessary methodologies, models, algorithms, synthesis & processing techniques, and requisite characterization and testing to rapidly accelerate the discovery of novel materials for extreme conditions. As such, it is expected the results of this program will be novel materials exhibiting unprecedented properties that have been developed utilizing all of the aforementioned tools which will be provided to DEVCOM-ARL for further analysis and testing.    

In order to achieve this paradigm shift in materials discovery, significant advances are needed in the following thrust areas:

• Data-driven Material Design – meant to be a comprehensive term for all aspects of the material design phase of the material development cycle which are accelerated through the integration of computational methods.
• High-Throughput Synthesis & Processing – to include both modifying existing synthesis & processing methods to accommodate for high-throughput, as well as developing novel techniques.
• High-Throughput Characterization – to include implementation of automation, as well as development of surrogate tests to mimic high-strain techniques which are not amenable to automation.
• ML-augmented Physics-Based Models – the integration of physics-based models with machine learning is poised to be a tipping point in materials science. To date, nearly all ML algorithms have been developed for big data (e.g. image recognition).  It is critical that we discontinue ‘repurposing’ these types of algorithms and begin developing ML algorithms specifically designed for materials discovery, and informed by physics.

HTMDEC has been developed in coordination with other related ARL-funded collaborative efforts (see descriptions of ARL collaborative alliances at https://www.arl.army.mil/www/default.cfm?page=93) and shares a common vision of highly collaborative academia-industry-government partnerships. However, HTMDEC will be executed with a program model different than previous ARL Collaborative Research/Technology Alliances. Specific components of the program are highlighted below:

• HTMDEC will be a two-step application process, consisting of a White paper stage and a Proposal stage.

• HTMDEC will be executed through two funding periods, herein referred to as “Seedling awards” and “Centers”. A Center will be an option period exercised from a seedling award. The only exception to this will be for the FY2022 Targeted Thrust Area FY2022-2 – Data Handling & Management. The Seedling selected for continuation for this particular thrust area will do so as a recurring Seedling for the duration of the HTMDEC program, and will support all of the Centers as the approved data platform for HTMDEC.

• A FOA Opportunity workshop will be held to brief interested Applicants on the long term program goals of this FOA. In FY2022, only seedling efforts will be awarded. The focus of these seedlings will be to address either one of the FY2022 targeted thrust areas, or one of the primary thrust areas. 

• White papers will address one of the particular thrust areas; thrust areas may change on an annual basis in order to reflect current interests. White papers will be evaluated. Applicants with only the most highly rated white papers will receive an invitation from the Government to submit a Proposal.

• Proposals will address one of the thrust areas. Proposals will be evaluated and funding will be provided to those Recipients selected for award of a cooperative agreement (CA) described as the “seedling” award.

• Prior to the close of the seedling award, the Recipients of a “seedling” CA are then eligible to submit a proposal for consideration of an option period under the seedling CA of up to 4 years.  This option period will be a Center. Since the option proposal will need to address all four thrust areas, seedling Recipients will be encouraged to collaborate and combine during the option period of performance so as to build the strongest possible Center moving forward. Option proposals will be evaluated and funding will be provided to those Recipients selected for the exercise of their option.   

• In addition to the option proposal addressing all four thrust areas, Recipients will also have the opportunity to provide for a Graduate Student Fellowship effort for US citizens working in one of the thrust areas.

• Project review workshops will be held annually, with the intention of allowing all CA Recipients to present the results of their research, as well as interacting with the other Recipients.  

***The 14 May 2021 version of this announcement is posted in a pre-release stage. The Government is currently seeking feedback from industry and the university/non-profit community on the contents of the announcement prior to releasing the official Funding Opportunity Announcement. The official announcement is expected to be released in June 2021. Until that announcement is released, the Government will be accepting feedback via email at:

usarmy.rtp.devcom-arl.mbx.baa3qa@mail.mil

We appreciate any and all feedback during this pre-release stage.***

Within the Army science and technology enterprise, DEVCOM-ARL is chartered to conduct disruptive foundational research, engage as the Army’s primary collaborative link to the scientific community, and interface to shape future fighting concepts. We crystalize these ideas and the impetus to perform these functions at the pace of innovation as ‘Operationalize Science for Transformational Overmatch’. Simply put, we seek to accelerate discovery and transition breakthroughs to the Warfighter faster than anyone else.

Artificial Intelligence (AI) (rule-based and Machine Learning (ML), together) presents powerful new tools for exploring an information landscape in discovering novel materials for applications in extreme conditions (e.g. high-strain rate, high-g loading, high temperature). Such approaches present considerable opportunity in exploring new frontiers for materials used in ballistic applications, especially when coupled with new approaches that allow larger and richer datasets, computational tools, and data infrastructure for collaboration. Broadly, AI/ML can be used to augment individual steps in the synthesis-processing-characterization pipeline, be used for scale-bridging to draw greater information from more tractable experimental approaches, and be used to guide a broader research loop.

Advances in synthesis, modeling, and characterization will greatly advance our ability to exploit monolithic materials in extreme conditions. However, there is a need to contemplate how the capabilities of additive manufacturing and other processing techniques can be used to evaluate materials that exhibit spatial variations in composition, anisotropic characteristics, and contain interfaces between multiple materials. The parameter space expands exponentially as these variables compound the system inputs, but truly advanced materials performance will likely be dependent on an integrated systems-level approach to materials design. 

Application of ML toolsets is viewed as necessary to achieve accelerated discovery of new materials for application in extreme dynamic (impact, thermal, ablative) conditions.  ML toolsets and software exist but may need to be adapted for the specific requirements of materials discovery and design. Full exploitation of the ML approach will certainly require extension and further development to focus on proof-of-concept for material classes of interest in ballistic application. This could be achieved within a generalized and scalable framework that supports rapid, robust and trusted data exchange. New tools to consolidate data, and improved high-throughput workflow will require specialized approaches to transient phenomena e.g. shocks, heating, localized deformation, and failure.  ML models that incorporate these phenomena will critically rely on physics-based models that target key mechanisms. Critical (targeted by ML approaches) physics models may require further development; ML offers opportunity to consolidate much of these physics into fast-running analytic frameworks compatible with the high-throughput approach and may be used to guide autonomous systems for high-throughput characterization of transient phenomena.

To accelerate improvements in Army armor and weapon system performance, DEVCOM-ARL wants to leverage high-throughput methods in synthesis, processing, characterization, and modeling for materials used in these applications. Machine-learning techniques are in the nascent stage of integration with materials science but may present a path towards accelerated discovery, as these tools may uncover novel links between system performance and material science that have been previously underdeveloped or overlooked. DEVCOM-ARL seeks collaboration with external investigators to leverage (and train experts on) machine-learning techniques in the discovery of materials that perform in extreme environments, but machine-learning techniques require large volumes of quantifiable data in order to best reveal links between the materials science and system performance. High throughput techniques may present a viable approach to satisfy the data volume requirements to bring machine-learning to bear.

In summary, the US Army Modernization Priorities require materials that survive and perform in extreme environments; harsh military environments of high-acceleration (e.g. projectile launch and flight), high-temperature and rapid ablation (e.g. hypersonic flight), and impacts at very high velocity (terminal ballistics). The totality of these environments and accumulating requirements on future materials drives the imperative to consider an increasingly large number of constituent elements, structure and properties. Discovery must now parse through billions of candidate materials to achieve highly specialized and transformational functions. This drives a data-driven approach; one that fuses high-throughput materials synthesis and characterization with machine learning algorithms and close-loop discovery automation.

The overarching goal of this program is to develop the necessary methodologies, models, algorithms, synthesis & processing techniques, and requisite characterization and testing to rapidly accelerate the discovery of novel materials for extreme conditions. As such, it is expected the results of this program will be novel materials exhibiting unprecedented properties that have been developed utilizing all of the aforementioned tools which will be provided to DEVCOM-ARL for further analysis and testing.    

In order to achieve this paradigm shift in materials discovery, significant advances are needed in the following thrust areas:

• Data-driven Material Design – meant to be a comprehensive term for all aspects of the material design phase of the material development cycle which are accelerated through the integration of computational methods.
• High-Throughput Synthesis & Processing – to include both modifying existing synthesis & processing methods to accommodate for high-throughput, as well as developing novel techniques.
• High-Throughput Characterization – to include implementation of automation, as well as development of surrogate tests to mimic high-strain techniques which are not amenable to automation.
• ML-augmented Physics-Based Models – the integration of physics-based models with machine learning is poised to be a tipping point in materials science. To date, nearly all ML algorithms have been developed for big data (e.g. image recognition).  It is critical that we discontinue ‘repurposing’ these types of algorithms and begin developing ML algorithms specifically designed for materials discovery, and informed by physics.

HTMDEC has been developed in coordination with other related ARL-funded collaborative efforts (see descriptions of ARL collaborative alliances at https://www.arl.army.mil/www/default.cfm?page=93) and shares a common vision of highly collaborative academia-industry-government partnerships. However, HTMDEC will be executed with a program model different than previous ARL Collaborative Research/Technology Alliances. Specific components of the program are highlighted below:

• HTMDEC will be a two-step application process, consisting of a White paper stage and a Proposal stage.

• HTMDEC will be executed through two funding periods, herein referred to as “Seedling awards” and “Centers”. A Center will be an option period exercised from a seedling award. The only exception to this will be for the FY2022 Targeted Thrust Area FY2022-2 – Data Handling & Management. The Seedling selected for continuation for this particular thrust area will do so as a recurring Seedling for the duration of the HTMDEC program, and will support all of the Centers as the approved data platform for HTMDEC.

• A FOA Opportunity workshop will be held to brief interested Applicants on the long term program goals of this FOA. In FY2022, only seedling efforts will be awarded. The focus of these seedlings will be to address either one of the FY2022 targeted thrust areas, or one of the primary thrust areas. 

• White papers will address one of the particular thrust areas; thrust areas may change on an annual basis in order to reflect current interests. White papers will be evaluated. Applicants with only the most highly rated white papers will receive an invitation from the Government to submit a Proposal.

• Proposals will address one of the thrust areas. Proposals will be evaluated and funding will be provided to those Recipients selected for award of a cooperative agreement (CA) described as the “seedling” award.

• Prior to the close of the seedling award, the Recipients of a “seedling” CA are then eligible to submit a proposal for consideration of an option period under the seedling CA of up to 4 years.  This option period will be a Center. Since the option proposal will need to address all four thrust areas, seedling Recipients will be encouraged to collaborate and combine during the option period of performance so as to build the strongest possible Center moving forward. Option proposals will be evaluated and funding will be provided to those Recipients selected for the exercise of their option.   

• In addition to the option proposal addressing all four thrust areas, Recipients will also have the opportunity to provide for a Graduate Student Fellowship effort for US citizens working in one of the thrust areas.

• Project review workshops will be held annually, with the intention of allowing all CA Recipients to present the results of their research, as well as interacting with the other Recipients.