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pro vyhledávání: '"Meziere, Jason A."'
Autor:
Luo, Yu, Meziere, Jason A., Samolyuk, German D., Hart, Gus L. W., Daymond, Mark R, Béland, Laurent Karim
Machine learning force fields (MLFFs) are an increasingly popular choice for atomistic simulations due to their high fidelity and improvable nature. Here, we propose a hybrid small-cell approach that combines attributes of both offline and active lea
Externí odkaz:
http://arxiv.org/abs/2306.00128
While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods, such as active learning and on-the-fly learning, construct reliable
Externí odkaz:
http://arxiv.org/abs/2304.01314
Akademický článek
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Autor:
Luo Y; Department of Mechanical and Materials Engineering, Queen's University, Kingston, Ontario K7L 2N8, Canada., Meziere JA; Department of Physics, Brigham Young University, Provo, Utah 84602, United States., Samolyuk GD; Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6138, United States., Hart GLW; Department of Physics, Brigham Young University, Provo, Utah 84602, United States., Daymond MR; Department of Mechanical and Materials Engineering, Queen's University, Kingston, Ontario K7L 2N8, Canada., Béland LK; Department of Mechanical and Materials Engineering, Queen's University, Kingston, Ontario K7L 2N8, Canada.
Publikováno v:
Journal of chemical theory and computation [J Chem Theory Comput] 2023 Oct 10; Vol. 19 (19), pp. 6848-6856. Date of Electronic Publication: 2023 Sep 12.
