Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Richard Messerly"'
Autor:
Alice E. A. Allen, Nicholas Lubbers, Sakib Matin, Justin Smith, Richard Messerly, Sergei Tretiak, Kipton Barros
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-9 (2024)
Abstract The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable t
Externí odkaz:
https://doaj.org/article/a6d03e20793240fea387226429c6ed76
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:
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
Publikováno v:
Goldschmidt2022 abstracts.
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. 6:916-916
Publikováno v:
The Journal of Physical Chemistry B. 126:5595-5596