Department of Energy’s ARPA-E Offers CATALCHEM-E Grant to Accelerate Catalytic Chemistry R&D via AI and High-Throughput Experimentation

• Clarified the meaning of the Program Policy Factors in Section V.C.
• The CP due date for this NOFO has already closed and is not being extended with this modification

NOFO Number: DE-FOA-0003505 – CATALYTIC APPLICATION TESTING FOR ACCELERATED LEARNING CHEMISTRIES VIA HIGH-THROUGHPUT EXPERIMENTATION AND MODELING EFFICIENTLY (CATALCHEM-E)
To obtain a copy of the Notice of Funding Opportunity (NOFO) please go to ARPA-E eXCHANGE at https://arpa-e-foa.energy.gov. To apply to this NOFO, Applicants must register with and submit application materials through ARPA-E eXCHANGE (https://arpa-e-foa.energy.gov/Registration.aspx). For detailed guidance on using ARPA-E eXCHANGE, please refer to the ARPA-E eXCHANGE User Guide (https://arpa-e-foa.energy.gov/Manuals.aspx). ARPA-E will not review or consider application materials submitted through other means. For problems with ARPA-E eXCHANGE, email ExchangeHelp@hq.doe.gov (with NOFO name and number in the subject line). Questions about this NOFO? Check the Frequently Asked Questions available at http://arpa-e.energy.gov/faq. For questions that have not already been answered, email ARPA-E-CO@hq.doe.gov.
Agency Overview:
The Advanced Research Projects Agency – Energy (ARPA-E), an organization within the Department of Energy (DOE), is chartered by Congress in the America COMPETES Act of 2007 (P.L. 110-69), as amended by the America COMPETES Reauthorization Act of 2010 (P.L. 111-358), as further amended by the Energy Act of 2020 (P.L. 116-260):
“(A) to enhance the economic and energy security of the United States through the development of energy technologies that—
(i) reduce imports of energy from foreign sources;
(ii) reduce energy-related emissions, including greenhouse gases;
(iii) improve the energy efficiency of all economic sectors;
(iv) provide transformative solutions to improve the management, clean-up, and disposal of radioactive waste and spent nuclear fuel; and
(v) improve the resilience, reliability, and security of infrastructure to produce, deliver, and store energy; and
(B) to ensure that the United States maintains a technological lead in developing and deploying advanced energy technologies.”
ARPA-E issues this Notice of Funding Opportunity (NOFO) under its authorizing statute codified at 42 U.S.C. § 16538. The NOFO and any cooperative agreements or grants made under this NOFO are subject to 2 C.F.R. Part 200 as supplemented by 2 C.F.R. Part 910.
ARPA-E funds research on, and the development of, transformative science and technology solutions to address the energy and environmental missions of the Department. The agency focuses on technologies that can be meaningfully advanced with a modest investment over a defined period of time in order to catalyze the translation from scientific discovery to early-stage technology. For the latest news and information about ARPA-E, its programs and the research projects currently supported, see: http://arpa-e.energy.gov/.

ARPA-E funds transformational research. Existing energy technologies generally progress on established “learning curves” where refinements to a technology and the economies of scale that accrue as manufacturing and distribution develop drive improvements to the cost/performance metric in a gradual fashion. This continual improvement of a technology is important to its increased commercial deployment and is appropriately the focus of the private sector or the applied technology offices within DOE. In contrast, ARPA-E supports transformative research that has the potential to create fundamentally new learning curves. ARPA-E technology projects typically start with cost/performance estimates well above the level of an incumbent technology. Given the high risk inherent in these projects, many will fail to progress, but some may succeed in generating a new learning curve with a projected cost/performance metric that is significantly better than that of the incumbent technology. ARPA-E will provide support at the highest funding level only for submissions with significant technology risk, aggressive timetables, and careful management and mitigation of the associated risks.
ARPA-E funds technology with the potential to be disruptive in the marketplace. The mere creation of a new learning curve does not ensure market penetration. Rather, the ultimate value of a technology is determined by the marketplace, and impactful technologies ultimately become disruptive – that is, they are widely adopted and displace existing technologies from the marketplace or create entirely new markets. ARPA-E understands that definitive proof of market disruption takes time, particularly for energy technologies. Therefore, ARPA-E funds the development of technologies that, if technically successful, have clear disruptive potential, e.g., by demonstrating capability for manufacturing at competitive cost and deployment at scale.
ARPA-E funds applied research and development (R&D). The Office of Management and Budget defines “applied research” as an “original investigation undertaken in order to acquire new knowledge…directed primarily towards a specific practical aim or objective” and defines “experimental development” as “creative and systematic work, drawing on knowledge gained from research and practical experience, which is directed at producing new products or processes or improving existing products or processes.”
Applicants interested in receiving financial assistance for basic research (defined by the Office of Management and Budget as “experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts”) should contact the DOE’s Office of Science (http://science.energy.gov/). Office of Science national scientific user facilities (http://science.energy.gov/user-facilities/) are open to all researchers, including ARPA-E Applicants and awardees. These facilities provide advanced tools of modern science including accelerators, colliders, supercomputers, light sources and neutron sources, as well as facilities for studying the nanoworld, the environment, and the atmosphere. Projects focused on early-stage R&D for the improvement of technology along defined roadmaps may be more appropriate for support through the DOE applied energy offices including: the Office of Energy Efficiency and Renewable Energy (http://www.eere.energy.gov/), the Office of Fossil Energy and Carbon Management (https://www.energy.gov/fecm/office-fossil-energy-and-carbon-management), the Office of Nuclear Energy (http://www.energy.gov/ne/office-nuclear-energy), and the Office of Electricity (https://www.energy.gov/oe/office-electricity).

ARPA-E encourages submissions stemming from ideas that still require proof-of-concept R&D efforts as well as those for which some proof-of-concept demonstration already exists. Submissions can propose a project with the end deliverable being an extremely creative, but partial solution.
Program Overview:
1. SUMMARY
The Catalytic Application Testing for Accelerated Learning Chemistries via High-throughput Experimentation and Modeling Efficiently (CATALCHEM-E) program aims to disrupt and accelerate the design and development cycle for heterogeneous catalyst R&D workflows. The program will span from rational material discovery to synthesis and final reactor testing. These novel workflows will be developed by coupling the latest advancements in artificial intelligence (AI) and machine learning (ML) with high-throughput experimentation (HTE) to verifiably complete 10–15 years of traditional catalysis R&D work within 12–18 months, thus achieving more than a ten-time acceleration in the catalyst development cycle. The program will then use these new tools to discover and optimize catalytic chemistries relevant to ARPA-E’s goals. These new chemistries will ultimately help advance the objective of net-zero carbon emissions by 2050.
Innovations developed under the CATALCHEM-E program will involve:
• Future refinery relevant or other next-generation feedstocks such as hydrogen (H2), nitrogen (N2), oxygen (O2), water (H2O), carbon dioxide (CO2), methane (CH4), ammonia (NH3), methanol (MeOH), ethanol (EtOH), bio-intermediates (CxHyOz), waste plastics, and triglycerides (TAGs); and
• Products like ethylene (C2=) and propylene (C3=) as low carbon monomers, and sustainable aviation fuel (SAF), diesel, and syngas as distillate range hydrocarbons.
Key program elements include, but are not limited to:
• Novel workflow topologies: A workflow topology is a diagram designating key task nodes involved in the development of a technical catalyst from conception through reactor-scale testing. An example of a traditional, non-automated closed-loop workflow spanning from hypothesis to technical catalyst performance testing is illustrated in Figure 1. The CATALCHEM-E program envisions creating novel closed-loop workflow topologies that strategically remove bottlenecks and time-consuming tasks ultimately resulting in significant acceleration when compared to the traditional workflow.
• Enhanced data integrity and benchmarking through reference chemistries: The program will use reference chemistries for workflow validation. These reference chemistries have been proven at the commercial scale and are relevant to both ARPA-E’s goals and the U.S. goal of net-zero carbon emissions by 2050. Specifically, the reference chemistries recommended in this program have been selected to provide applicants with feedstock phase flexibility across thermochemical and electrochemical reaction classes. Here, commercially available catalysts operating in real-world industrial-scale reactor units will serve as controls to ensure data integrity within each task node in the workflow, and as benchmarks when assessing the performance of AI/ML models.
• High-quality data generation via HTE: Autonomous or automated HTE methods are necessary to generate high-quality experimental data in large quantities to train and validate AI/ML models. HTE techniques operating over complex, wide parameter spaces can increase the efficiency of experimentation across all stages of the catalyst development cycle, from research catalyst (synthesis, characterization, and validation) to technical catalyst (formulation, characterization, and validation). In addition, the quality of data and ability to identify the most optimal experimental conditions is expected to be enhanced dramatically with these approaches.
• AI/ML-ready catalysis databases and informatics: As a result of creating effective CATALCHEM-E workflows, projects must create a set of robust databases in tandem populated with high-quality, multi-scale, multi-modal data as generated and gathered from synthesis, characterization, and performance testing tasks at the ab initio and research and technical catalyst levels. Further, these novel workflows will take advantage of the tools for automation and database management to streamline the storage, access, and processing of collected data that is findable, accessible, interoperable, and reusable (FAIR) to accomplish AI/ML tasks.
• Transformational multi-scale, multi-modal modeling using AI/ML: As shown in Figure 2 in Section I.C, there are several ways to leverage the AI/ML tools and techniques to understand heterogenous catalyst surfaces starting from known theoretical predictions or prior knowledge in literature coupled with CATALCHEM-E workflow data (including language) from synthesis, advanced characterization, and reactor-scale testing activities.
• Surrogate AI/ML assisted computational modeling and simulations: ML-based surrogate models can be used in two ways in the program. First, these models can accelerate the parametric testing space for the reactor-scale performance. Projects may accelerate the simulation of technical catalyst performance at engineering-scale by training surrogate models on data generated using various computational fluid dynamics and multi-physics approaches (e.g., COMSOL, Ansys, and OpenFOAM). , , These simulations can be used to expand the training set of the CATALCHEM-E learning model. Second, these models can accelerate the elucidation of fundamental reaction mechanisms and networks by including atomic scale, ab initio approximations along with microkinetic modeling for more rigorous calculations involving surface transition states and adsorption energetics.

To view the NOFO in its entirety, please visit https://arpa-e-foa.energy.gov.

Program Overview

The Catalytic Application Testing for Accelerated Learning Chemistries via High-throughput Experimentation and Modeling Efficiently (CATALCHEM-E) program aims to disrupt and accelerate the design and development cycle for heterogeneous catalyst R&D workflows. The program will span from rational material discovery to synthesis and final reactor testing. These novel workflows will be developed by coupling the latest advancements in artificial intelligence (AI) and machine learning (ML) with high-throughput experimentation (HTE) to verifiably complete 10–15 years of traditional catalysis R&D work within 12–18 months, thus achieving more than a ten-time acceleration in the catalyst development cycle.[1] The program will then use these new tools to discover and optimize catalytic chemistries relevant to ARPA-E’s goals. These new chemistries will ultimately help advance the objective of net-zero carbon emissions by 2050.[2]

Innovations developed under the CATALCHEM-E program will involve:

·      Future refinery relevant or other next-generation feedstocks such as hydrogen (H2), nitrogen (N2), oxygen (O2), water (H2O), carbon dioxide (CO2), methane (CH4), ammonia (NH3), methanol (MeOH), ethanol (EtOH), bio-intermediates (CxHyOz), waste plastics, and triglycerides (TAGs); and

·      Products like ethylene (C2=) and propylene (C3=) as low carbon monomers, and sustainable aviation fuel (SAF), diesel, and syngas as distillate range hydrocarbons.

[1] Sharon Mitchell, Nina-Luisa Michels, and Javier Pérez-Ramírez, “From Powder to Technical Body: The Undervalued Science of Catalyst Scale Up,” Chemical Society Reviews 42, no. 14 (June 21, 2013): 6094–6112, https://doi.org/10.1039/C3CS60076A.

[2] U.S. Department of Energy. “How we’re moving to net-zero by 2050,” (April 2021). https://www.energy.gov/articles/how-were-moving-net-zero-2050.

Program Overview

The Catalytic Application Testing for Accelerated Learning Chemistries via High-throughput Experimentation and Modeling Efficiently (CATALCHEM-E) program aims to disrupt and accelerate the design and development cycle for heterogeneous catalyst R&D workflows. The program will span from rational material discovery to synthesis and final reactor testing. These novel workflows will be developed by coupling the latest advancements in artificial intelligence (AI) and machine learning (ML) with high-throughput experimentation (HTE) to verifiably complete 10–15 years of traditional catalysis R&D work within 12–18 months, thus achieving more than a ten-time acceleration in the catalyst development cycle.[1] The program will then use these new tools to discover and optimize catalytic chemistries relevant to ARPA-E’s goals. These new chemistries will ultimately help advance the objective of net-zero carbon emissions by 2050.[2]

Innovations developed under the CATALCHEM-E program will involve:

·      Future refinery relevant or other next-generation feedstocks such as hydrogen (H2), nitrogen (N2), oxygen (O2), water (H2O), carbon dioxide (CO2), methane (CH4), ammonia (NH3), methanol (MeOH), ethanol (EtOH), bio-intermediates (CxHyOz), waste plastics, and triglycerides (TAGs); and

·      Products like ethylene (C2=) and propylene (C3=) as low carbon monomers, and sustainable aviation fuel (SAF), diesel, and syngas as distillate range hydrocarbons.

[1] Sharon Mitchell, Nina-Luisa Michels, and Javier Pérez-Ramírez, “From Powder to Technical Body: The Undervalued Science of Catalyst Scale Up,” Chemical Society Reviews 42, no. 14 (June 21, 2013): 6094–6112, https://doi.org/10.1039/C3CS60076A.

[2] U.S. Department of Energy. “How we’re moving to net-zero by 2050,” (April 2021). https://www.energy.gov/articles/how-were-moving-net-zero-2050.