Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Simon Batzner"'
Autor:
Cameron J. Owen, Steven B. Torrisi, Yu Xie, Simon Batzner, Kyle Bystrom, Jennifer Coulter, Albert Musaelian, Lixin Sun, Boris Kozinsky
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-16 (2024)
Abstract This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictio
Externí odkaz:
https://doaj.org/article/a68504f7e9f24d84b4a565a76a1b91db
Autor:
Mgcini Keith Phuthi, Archie Mingze Yao, Simon Batzner, Albert Musaelian, Pinwen Guan, Boris Kozinsky, Ekin Dogus Cubuk, Venkatasubramanian Viswanathan
Publikováno v:
ACS Omega, Vol 9, Iss 9, Pp 10904-10912 (2024)
Externí odkaz:
https://doaj.org/article/24c6b8427dc549148fc5cd59d3ee89c0
Autor:
Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J. Owen, Mordechai Kornbluth, Boris Kozinsky
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-15 (2023)
The paper presents a method that allows scaling machine learning interatomic potentials to extremely large systems, while at the same time retaining the remarkable accuracy and learning efficiency of deep equivariant models. This is obtained with an
Externí odkaz:
https://doaj.org/article/c834dacf32f9492789e8747321d5d7e6
Autor:
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, Boris Kozinsky
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
An E(3)-equivariant deep learning interatomic potential is introduced for accelerating molecular dynamics simulations. The method obtains state-of-the-art accuracy, can faithfully describe dynamics of complex systems with remarkable sample efficiency
Externí odkaz:
https://doaj.org/article/00967ba51d9e4740b8ca005b21c889fc
Autor:
Simon Batzner
Publikováno v:
Nature Computational Science. 3:190-191
Autor:
Lixin Sun, Jonathan Vandermause, Simon Batzner, Yu Xie, David Clark, Wei Chen, Boris Kozinsky
Publikováno v:
Journal of chemical theory and computation. 18(4)
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics. In this work, a data-driven machine learning algorithm is devised to
Autor:
Mordechai Kornbluth, Simon Batzner, Boris Kozinsky, Jonathan P. Mailoa, Nicola Molinari, Lixin Sun, Tess Smidt
This work presents Neural Equivariant Interatomic Potentials (NequIP), a SE(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1addaf427055d7ef1e6fe91cfd80d3c1
https://doi.org/10.21203/rs.3.rs-244137/v1
https://doi.org/10.21203/rs.3.rs-244137/v1
Autor:
Jonathan Vandermause, Chris Ablitt, Jonathan P. Mailoa, Boris Kozinsky, Nicola Molinari, Mordechai Kornbluth, Georgy Samsonidze, Simon Batzner, Stephen T. Lam
Neural network force field (NNFF) is a method for performing regression on atomic structure–force relationships, bypassing the expensive quantum mechanics calculations that prevent the execution of long ab initio quality molecular dynamics (MD) sim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b6fb55daadd072608d3d7c0d76df3cb
http://arxiv.org/abs/1905.02791
http://arxiv.org/abs/1905.02791
Autor:
Jonathan Vandermause, Yu Xie, Alexie M. Kolpak, Simon Batzner, Lixin Sun, Boris Kozinsky, Steven B. Torrisi
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-11 (2020)
Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::179ef8f3576cf8ac7c08cff902e8845a