Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials
- Hieu A. DoanHieu A. DoanMaterials Science Division and Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United StatesMore by Hieu A. Doan,
- Garvit AgarwalGarvit AgarwalMaterials Science Division and Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United StatesMore by Garvit Agarwal,
- Hai QianHai QianThe Beckman Institute for Advanced Science and Technology and Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United StatesJoint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United StatesMore by Hai Qian,
- Michael J. CounihanMichael J. CounihanThe Beckman Institute for Advanced Science and Technology and Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United StatesJoint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United StatesMore by Michael J. Counihan,
- Joaquín Rodríguez-LópezJoaquín Rodríguez-LópezThe Beckman Institute for Advanced Science and Technology and Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United StatesJoint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United StatesMore by Joaquín Rodríguez-López,
- Jeffrey S. MooreJeffrey S. MooreThe Beckman Institute for Advanced Science and Technology, Department of Chemistry and Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United StatesJoint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United StatesMore by Jeffrey S. Moore, and
- Rajeev S. Assary*Rajeev S. Assary*(R.S.A.) Email: [email protected]. Phone: 630-252-3536.Materials Science Division and Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United StatesMore by Rajeev S. Assary
Abstract
We employed density functional theory (DFT) to compute oxidation potentials of 1400 homobenzylic ether molecules to search for the ideal sustainable redoxmer design. The generated data were used to construct an active learning model based on Bayesian optimization (BO) that targets candidates with desired oxidation potentials utilizing only a minimal number of DFT calculations. The active learning model demonstrated not only significant efficiency improvement over the random selection approach but also robust capability in identifying desired candidates in an untested set of 112 000 homobenzylic ether molecules. Our findings highlight the efficacy of quantum chemistry-informed active learning to accelerate the discovery of materials with desired properties from a vast chemical space.
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