Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Ying Wai, Li"'
Autor:
Duane C. Harris, Apoorv Shanker, Makaela M. Montoya, Trent R. Llewellyn, Anna R. Matuszak, Aditi Lohar, Jessica Z. Kubicek-Sutherland, Ying Wai Li, Kristen Wilding, Ben Mcmahon, Sandrasegaram Gnanakaran, Ruy M. Ribeiro, Alan S. Perelson, Carmen Molina-París
Publikováno v:
Frontiers in Immunology, Vol 15 (2024)
Vaccines have historically played a pivotal role in controlling epidemics. Effective vaccines for viruses causing significant human disease, e.g., Ebola, Lassa fever, or Crimean Congo hemorrhagic fever virus, would be invaluable to public health stra
Externí odkaz:
https://doaj.org/article/ffe0469cf0d941c6bf7e57eae0e04be7
Autor:
Anjana M. Samarakoon, Kipton Barros, Ying Wai Li, Markus Eisenbach, Qiang Zhang, Feng Ye, V. Sharma, Z. L. Dun, Haidong Zhou, Santiago A. Grigera, Cristian D. Batista, D. Alan Tennant
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
Developing an understanding of a material’s magnetic behaviour based on neutron scattering measurements often relies on extracting an effective spin model. Samarakoon et al. demonstrate an automated machine learning approach to this problem, leadin
Externí odkaz:
https://doaj.org/article/5964d7ee7a0c4c278294454fdb52db06
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:
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:
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:
Journal of chemical information and modeling. 62(10)
Using a realistic molecular catalyst system, we conduct scaling studies of ab initio molecular dynamics simulations using the popular CP2K code on both Intel Xeon CPU and NVIDIA V100 GPU architectures. Additional performance improvements were gained
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fd4bb7acf2ca62d554c41a903397051
https://pubmed.ncbi.nlm.nih.gov/35451847
https://pubmed.ncbi.nlm.nih.gov/35451847
We identify configurational phases and structural transitions in a polymer nanotube composite by means of machine learning. We employ various unsupervised dimensionality reduction methods, conventional neural networks, as well as the confusion method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd400d20cb7ab1dd1ec4c00b8aab8589
http://arxiv.org/abs/2203.11861
http://arxiv.org/abs/2203.11861
Autor:
Ying Wai Li, Peter W. Doak, Giovanni Balduzzi, Wael Elwasif, Ed F. D’Azevedo, Thomas A. Maier
Publikováno v:
Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation ISBN: 9783030964979
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68a87c7a21b4c6fb557fe6980668249a
https://doi.org/10.1007/978-3-030-96498-6_11
https://doi.org/10.1007/978-3-030-96498-6_11
Autor:
Timothy Ferreira, Shree Ram Acharya, Ying Wai Li, David S. Parker, Athena S. Sefat, Valentino R. Cooper
Publikováno v:
Physical Review Materials. 5
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89c9dda4ce95c5d68ea052e29da8e81a
https://doi.org/10.1103/physrevmaterials.5.123401
https://doi.org/10.1103/physrevmaterials.5.123401