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Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy.

, , , and . CoRR, (2022)

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Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis., , , , and . CoRR, (2022)M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery., , , , , , , , and . CoRR, (2023)Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles., , , , and . CoRR, (2021)Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery., , , , , and . CoRR, (2021)Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy., , , and . CoRR, (2022)MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks., , , , , and . CoRR, (2021)Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials., , , , , , , , , and 3 other author(s). CoRR, (2023)Rapid Exploration of a 32.5M Compound Chemical Space with Active Learning to Discover Density Functional Approximation Insensitive and Synthetically Accessible Transitional Metal Chromophores., , , , and . CoRR, (2022)Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model., , , and . Nat. Comput. Sci., 3 (12): 1045-1055 (2023)Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties., , , and . CoRR, (2022)