Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Maksim Kulichenko"'
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02b456a999b70e69a0288db18a796416
https://doi.org/10.1038/s43588-023-00406-5
https://doi.org/10.1038/s43588-023-00406-5
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a08c5524a28a27041dd5f99cef59b2e3
https://doi.org/10.1038/s41570-022-00416-3
https://doi.org/10.1038/s41570-022-00416-3
Autor:
Anton S. Pozdeev, Wei-Jia Chen, Hyun Wook Choi, Maksim Kulichenko, Dao-Fu Yuan, Alexander I. Boldyrev, Lai-Sheng Wang
Publikováno v:
The Journal of Physical Chemistry A.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6db1b9a0a1e1bdf65ba4ae4634e66db0
https://doi.org/10.1021/acs.jpca.3c02417
https://doi.org/10.1021/acs.jpca.3c02417
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,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::43d983983326430ee6c45a8fb4b9a930
https://doi.org/10.1021/acs.jctc.3c00234
https://doi.org/10.1021/acs.jctc.3c00234
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
Autor:
Alvaro Muñoz-Castro, Ivan A. Popov, Nikita Fedik, Zhong-Ming Sun, Maksim Kulichenko, Nikolay V. Tkachenko, Alexander I. Boldyrev
Publikováno v:
European Journal of Inorganic Chemistry. 2021:4239-4250
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::79e8ae378ff5c69e60120639b99403ea
https://doi.org/10.1002/ejic.202100519
https://doi.org/10.1002/ejic.202100519
Autor:
Hyun Wook Choi, Lai-Sheng Wang, Daofu Yuan, Maksim Kulichenko, Wei-Jia Chen, Alexander I. Boldyrev, Joseph Cavanagh
Publikováno v:
The Journal of Physical Chemistry A. 125:6751-6760
Because of its low toxicity, bismuth is considered to be a "green metal" and has received increasing attention in chemistry and materials science. To understand the chemical bonding of bismuth, here we report a joint experimental and theoretical stud
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0690de3c553f1bd970672b193bda082
https://doi.org/10.1021/acs.jpca.1c05846
https://doi.org/10.1021/acs.jpca.1c05846
Autor:
Maksim Kulichenko, Justin S. Smith, Kipton Barros, Nikita Fedik, Alexander I. Boldyrev, Benjamin Nebgen, Sergei Tretiak, Nicholas Lubbers, Ying Wai Li
Publikováno v:
The Journal of Physical Chemistry Letters. 12:6227-6243
Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2249775791fb7cfdda060c036042ddc
https://doi.org/10.1021/acs.jpclett.1c01357
https://doi.org/10.1021/acs.jpclett.1c01357
Publikováno v:
The Journal of Physical Chemistry A. 125:4606-4613
The strong relativistic effects result in many interesting chemical and physical properties for gold and gold compounds. One of the most surprising findings has been that small gold clusters prefer planar structures. Dopants can be used to tune the e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::75942b5c77409cc583aa858741e8c4b3
https://doi.org/10.1021/acs.jpca.1c02954
https://doi.org/10.1021/acs.jpca.1c02954
Publikováno v:
The Journal of Physical Chemistry C. 125:9564-9570
Electrides are an unusual class of compounds where electrons are localized in space distinct from atomic positions and behave like anions. This type of localization makes electron density very “fle...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f5ade6bcddd712e52cd4b08a5c252ce
https://doi.org/10.1021/acs.jpcc.1c02710
https://doi.org/10.1021/acs.jpcc.1c02710