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Structure prediction – Recommendation engines have been developed in the literature, ranging in strategy from recommending compounds that are chemically similar to existing compounds, to deep learning of formation energies. We conduct a systematic comparison of the performance of previously developed recommendation engines in recovering stable hypothetical compounds in the Open Quantum Materials Database (OQMD), and develop workflows to execute these methods in a highly efficient manner. In addition, we apply a variety of machine-learning-based tools to automate the prediction of new inorganic compounds. Leveraging these tools, we perform high throughput predictions of thousands of new stable, previously-undiscovered inorganic compounds, and significantly enhance the number of energetically stable compounds in the OQMD.
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Structure prediction – Recommendation engines have been developed in the literature, ranging in strategy from recommending compounds that are chemically similar to existing compounds, to deep learning of formation energies. We conduct a systematic comparison of the performance of previously developed recommendation engines in recovering stable hypothetical compounds in the Open Quantum Materials Database (OQMD), and develop workflows to execute these methods in a highly efficient manner. In addition, we apply a variety of machine-learning-based tools to automate the prediction of new inorganic compounds. Leveraging these tools, we perform high throughput predictions of thousands of new stable, previously-undiscovered inorganic compounds, and significantly enhance the number of energetically stable compounds in the OQMD.
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## Biography |
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## Biography
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Chris Wolverton is the Jerome B. Cohen Professor of Materials Science and Engineering at Northwestern University. Before joining the faculty, he worked at the Research and Innovation Center at Ford Motor Company, where he was group leader for the Hydrogen Storage and Nanoscale Modeling Group. He received his BS degree in Physics from the University of Texas at Austin, his PhD degree in Physics from the University of California at Berkeley, and performed postdoctoral work at the National Renewable Energy Laboratory (NREL). His research interests include computational studies of a variety of energy-efficient and environmentally friendly materials via first-principles atomistic calculations, high-throughput and machine learning tools to accelerate materials discovery, and “multiscale” methodologies for linking atomistic and microstructural scales. He is a Fellow of the American Physical Society and the American Society for Metals, and is an ISI Highly Cited Researcher. He has published more than 400 papers, with ~45,000 citations, and an h-index of 106. |
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