Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Benjamin Nebgen"'
Autor:
Justin S. Smith, Benjamin Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy Germann, Saryu Fensin, Kipton Barros
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
The accuracy of a machine-learned potential is limited by the quality and diversity of the training dataset. Here the authors propose an active learning approach to automatically construct general purpose machine-learning potentials here demonstrated
Externí odkaz:
https://doaj.org/article/f563b56affa04410a543a78699a94661
Autor:
Maksim Kulichenko, Kipton Barros, Nicholas Lubbers, Ying Wai Li, Richard Messerly, Sergei Tretiak, Justin S. Smith, Benjamin Nebgen
Publikováno v:
Nature Computational Science. 3:230-239
Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, th
Autor:
Nikita Fedik, Roman Zubatyuk, Maksim Kulichenko, Nicholas Lubbers, Justin S. Smith, Benjamin Nebgen, Richard Messerly, Ying Wai Li, Alexander I. Boldyrev, Kipton Barros, Olexandr Isayev, Sergei Tretiak
Publikováno v:
Nature Reviews Chemistry. 6:653-672
Autor:
Maksim Kulichenko, Kipton Barros, Nicholas Lubbers, Nikita Fedik, Guoqing Zhou, Sergei Tretiak, Benjamin Nebgen, Anders M. N. Niklasson
Publikováno v:
Journal of Chemical Theory and Computation.
Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented in the semiempirical PyTorch-based software PySeQM. The implementation includes finite electronic temperatures,
Autor:
Michael Chigaev, Justin S. Smith, Steven Anaya, Benjamin Nebgen, Matthew Bettencourt, Kipton Barros, Nicholas Lubbers
Publikováno v:
The Journal of Chemical Physics. 158
Atomistic machine learning focuses on the creation of models that obey fundamental symmetries of atomistic configurations, such as permutation, translation, and rotation invariances. In many of these schemes, translation and rotation invariance are a
Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, efficient collection before thermalization remains an obstacle for realizati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd840448b002be29966efbd9dc4d1667
http://arxiv.org/abs/2303.04318
http://arxiv.org/abs/2303.04318
Autor:
Manish Bhattarai, Namita kharat, Ismael Boureima, Erik Skau, Benjamin Nebgen, Hristo Djidjev, Sanjay Rajopadhye, James P. Smith, Boian Alexandrov
Publikováno v:
Journal of Parallel and Distributed Computing. 179:104709
Autor:
SHUHAO ZHANG, Małgorzata Makoś, Ryan Jadrich, Elfi Kraka, Kipton Barros, Benjamin Nebgen, Sergei Tretiak, Olexandr Isayev, Nicholas Lubbers, Richard Messerly, Justin Smith
Reactive chemistry atomistic simulation has a broad range of applications from drug design to energy to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive quantum che
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b7cc9749d27035d3b9e55569e850c7f
https://doi.org/10.26434/chemrxiv-2022-15ct6-v2
https://doi.org/10.26434/chemrxiv-2022-15ct6-v2
Autor:
Maksim Kulichenko, Kipton Barros, Nicholas Lubbers, Ying Wai Li, Richard Messerly, Sergei Tretiak, Justin Smith, Benjamin Nebgen
Machine learning (ML) models, if trained to datasets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse datasets. In this approach, the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dfd0c0bbe27494bd84d49df0541e8ea6
https://doi.org/10.21203/rs.3.rs-2109927/v1
https://doi.org/10.21203/rs.3.rs-2109927/v1