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Chen C, Nguyen DT, Lee SJ, Baker NA, Karakoti AS, Lauw L, Owen C, Mueller KT, Bilodeau BA, Murugesan V, Troyer M. Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation. J Am Chem Soc 2024; 146:20009-20018. [PMID: 38980280 DOI: 10.1021/jacs.4c03849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
High-throughput computational materials discovery has promised significant acceleration of the design and discovery of new materials for many years. Despite a surge in interest and activity, the constraints imposed by large-scale computational resources present a significant bottleneck. Furthermore, examples of very large-scale computational discovery carried out through experimental validation remain scarce, especially for materials with product applicability. Here, we demonstrate how this vision became reality by combining state-of-the-art machine learning (ML) models and traditional physics-based models on cloud high-performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. By focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade's worth of collective knowledge in the field as a byproduct. We then synthesized and experimentally characterized the structures and conductivities of our top candidates, the NaxLi3-xYCl6 (0≤ x≤ 3) series, demonstrating the potential of these compounds to serve as solid electrolytes. Additional candidate materials that are currently under experimental investigation could offer more examples of the computational discovery of new phases of Li- and Na-conducting solid electrolytes. The showcased screening of millions of materials candidates highlights the transformative potential of advanced ML and HPC methodologies, propelling materials discovery into a new era of efficiency and innovation.
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Affiliation(s)
- Chi Chen
- Azure Quantum, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
| | - Dan Thien Nguyen
- Pacific Northwest National Laboratory, Physical and Computational Sciences Directorate, 902 Battelle Blvd., Richland, Washington 99352, United States
| | - Shannon J Lee
- Pacific Northwest National Laboratory, Physical and Computational Sciences Directorate, 902 Battelle Blvd., Richland, Washington 99352, United States
| | - Nathan A Baker
- Azure Quantum, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
| | - Ajay S Karakoti
- Pacific Northwest National Laboratory, Physical and Computational Sciences Directorate, 902 Battelle Blvd., Richland, Washington 99352, United States
| | - Linda Lauw
- Azure Quantum, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
| | - Craig Owen
- Microsoft Surface, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
| | - Karl T Mueller
- Pacific Northwest National Laboratory, Physical and Computational Sciences Directorate, 902 Battelle Blvd., Richland, Washington 99352, United States
| | - Brian A Bilodeau
- Azure Quantum, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
| | - Vijayakumar Murugesan
- Pacific Northwest National Laboratory, Physical and Computational Sciences Directorate, 902 Battelle Blvd., Richland, Washington 99352, United States
| | - Matthias Troyer
- Azure Quantum, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
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Kim S, Jung Y, Schrier J. Large Language Models for Inorganic Synthesis Predictions. J Am Chem Soc 2024; 146:19654-19659. [PMID: 38991051 DOI: 10.1021/jacs.4c05840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis. The predictions of fine-tuned LLMs are comparable to─and sometimes better than─recent bespoke machine learning models for these tasks but require only minimal user expertise, cost, and time to develop. Therefore, this strategy can serve both as an effective and strong baseline for future machine learning studies of various chemical applications and as a practical tool for experimental chemists.
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Affiliation(s)
- Seongmin Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
| | - Yousung Jung
- Department of Chemical and Biological Engineering (BK21 four), Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
- Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
- Institute of Engineering Research, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
| | - Joshua Schrier
- Department of Chemistry and Biochemistry, Fordham University, 441 East Fordham Road, The Bronx, New York 10458, United States
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Kim S, Noh J, Gu GH, Chen S, Jung Y. Predicting synthesis recipes of inorganic crystal materials using elementwise template formulation. Chem Sci 2024; 15:1039-1045. [PMID: 38239693 PMCID: PMC10793203 DOI: 10.1039/d3sc03538g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 12/05/2023] [Indexed: 01/22/2024] Open
Abstract
While advances in computational techniques have accelerated virtual materials design, the actual synthesis of predicted candidate materials is still an expensive and slow process. While a few initial studies attempted to predict the synthesis routes for inorganic crystals, the existing models do not yield the priority of predictions and could produce thermodynamically unrealistic precursor chemicals. Here, we propose an element-wise graph neural network to predict inorganic synthesis recipes. The trained model outperforms the popularity-based statistical baseline model for the top-k exact match accuracy test, showing the validity of our approach for inorganic solid-state synthesis. We further validate our model by the publication-year-split test, where the model trained based on the materials data until the year 2016 is shown to successfully predict synthetic precursors for the materials synthesized after 2016. The high correlation between the probability score and prediction accuracy suggests that the probability score can be interpreted as a measure of confidence levels, which can offer the priority of the predictions.
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Affiliation(s)
- Seongmin Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST) 291, Daehak-ro, Yuseong-gu Daejeon 34141 South Korea
| | - Juhwan Noh
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST) 291, Daehak-ro, Yuseong-gu Daejeon 34141 South Korea
| | - Geun Ho Gu
- School of Energy Technology, Korea Institute of Energy Technology 200 Hyuksin-ro Naju 58330 South Korea
| | - Shuan Chen
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST) 291, Daehak-ro, Yuseong-gu Daejeon 34141 South Korea
| | - Yousung Jung
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST) 291, Daehak-ro, Yuseong-gu Daejeon 34141 South Korea
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University 1, Gwanak-ro, Gwanak-gu Seoul 08826 South Korea
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Schmidt J, Hoffmann N, Wang HC, Borlido P, Carriço PJMA, Cerqueira TFT, Botti S, Marques MAL. Machine-Learning-Assisted Determination of the Global Zero-Temperature Phase Diagram of Materials. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2210788. [PMID: 36949007 DOI: 10.1002/adma.202210788] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 02/28/2023] [Indexed: 06/02/2023]
Abstract
Crystal-graph attention neural networks have emerged recently as remarkable tools for the prediction of thermodynamic stability. The efficacy of their learning capabilities and their reliability is however subject to the quantity and quality of the data they are fed. Previous networks exhibit strong biases due to the inhomogeneity of the training data. Here a high-quality dataset is engineered to provide a better balance across chemical and crystal-symmetry space. Crystal-graph neural networks trained with this dataset show unprecedented generalization accuracy. Such networks are applied to perform machine-learning-assisted high-throughput searches of stable materials, spanning 1 billion candidates. In this way, the number of vertices of the global T = 0 K phase diagram is increased by 30% and find more than ≈150 000 compounds with a distance to the convex hull of stability of less than 50 meV atom-1 . The discovered materials are then accessed for applications, identifying compounds with extreme values of a few properties, such as superconductivity, superhardness, and giant gap-deformation potentials.
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Affiliation(s)
- Jonathan Schmidt
- Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, D-06099, Halle, Germany
| | - Noah Hoffmann
- Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, D-06099, Halle, Germany
| | - Hai-Chen Wang
- Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, D-06099, Halle, Germany
| | - Pedro Borlido
- CFisUC, Department of Physics, University of Coimbra, Rua Larga, 3004-516, Coimbra, Portugal
| | - Pedro J M A Carriço
- CFisUC, Department of Physics, University of Coimbra, Rua Larga, 3004-516, Coimbra, Portugal
| | - Tiago F T Cerqueira
- CFisUC, Department of Physics, University of Coimbra, Rua Larga, 3004-516, Coimbra, Portugal
| | - Silvana Botti
- Institut für Festkörpertheorie und -optik, Friedrich-Schiller-Universität Jena, Max-Wien-Platz 1, 07743, Jena, Germany
| | - Miguel A L Marques
- Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, D-06099, Halle, Germany
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