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Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles., , , , и . CoRR, (2021)Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery., , , , , и . CoRR, (2021)Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy., , , и . CoRR, (2022)Density Functional Theory in Transition-Metal Chemistry: A Self-Consistent Hubbard $U$ Approach, , , и . Phys. Rev. Lett., 97 (10): 103001 (сентября 2006)A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery., , , , и . CoRR, (2022)Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks., , и . CoRR, (2021)Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets., , , и . J. Cheminformatics, 15 (1): 121 (декабря 2023)Reply to "Comment on 'Evaluating Unexpectedly Short Non-covalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis'"., и . J. Chem. Inf. Model., 59 (9): 3609-3610 (2019)SESAMI APP: An Accessible Interface for Surface Area Calculation of Materials from Adsorption Isotherms., , , , , и . J. Open Source Softw., 8 (87): 5429 (июля 2023)Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis., , , , и . CoRR, (2022)