Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Yuezhi Mao"'
Autor:
Peter Eastman, Pavan Kumar Behara, David L. Dotson, Raimondas Galvelis, John E. Herr, Josh T. Horton, Yuezhi Mao, John D. Chodera, Benjamin P. Pritchard, Yuanqing Wang, Gianni De Fabritiis, Thomas E. Markland
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-11 (2023)
Abstract Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe the SPICE dataset, a new quantum chemistry dataset for traini
Externí odkaz:
https://doaj.org/article/777e27c4ba50416380442bf4a396eb72
Autor:
Xiaoliang Pan, Snyder, Ryan, Jia-Ning Wang, Lander, Chance, Wickizer, Carly, Van, Richard, Chesney, Andrew, Yuanfei Xue, Yuezhi Mao, Ye Mei, Jingzhi Pu, Yihan Shao
Publikováno v:
Journal of Computational Chemistry; 4/15/2024, Vol. 45 Issue 10, p638-647, 10p
Autor:
Songyuan Yao, Richard Van, Xiaoliang Pan, Ji Hwan Park, Yuezhi Mao, Jingzhi Pu, Ye Mei, Yihan Shao
Publikováno v:
RSC Advances. 13:4565-4577
Here we investigated the use of machine learning (ML) techniques to “derive” an implicit solvent model based on the average solvent environment configurations from explicit solvent molecular dynamics (MD) simulations.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e2e74d633b2df5ff1e102a6cecc793a
https://doi.org/10.1039/d2ra08180f
https://doi.org/10.1039/d2ra08180f
Publikováno v:
The Journal of Physical Chemistry B. 126:5876-5886
The ability to exploit carbonyl groups to measure electric fields in enzymes and other complex reactive environments by using the vibrational Stark effect has inspired growing interest in how these fields can be measured, tuned, and ultimately design
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d1b513708ac72855e3a42fe6d3cf869
https://doi.org/10.1021/acs.jpcb.2c03095
https://doi.org/10.1021/acs.jpcb.2c03095
Autor:
Chu Zheng, Yuezhi Mao, Jacek Kozuch, Austin O. Atsango, Zhe Ji, Thomas E. Markland, Steven G. Boxer
Publikováno v:
Nature Chemistry. 14:891-897
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::255ac39f3881a435074662d7ceee09ed
https://doi.org/10.1038/s41557-022-00937-w
https://doi.org/10.1038/s41557-022-00937-w
Autor:
Abdulrahman Aldossary, Martí Gimferrer, Yuezhi Mao, Hongxia Hao, Akshaya K. Das, Pedro Salvador, Teresa Head-Gordon, Martin Head-Gordon
Computational quantum chemistry can be more than just numerical experiments when methods are specifically adapted to investigate chemical concepts. One important example is the development of energy decomposition analysis (EDA) to reveal the physical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8123c7faca50ac598af1cb03daf94b6e
https://doi.org/10.26434/chemrxiv-2022-4jk6h
https://doi.org/10.26434/chemrxiv-2022-4jk6h
Autor:
Peter Eastman, Pavan Kumar Behara, David L. Dotson, Raimondas Galvelis, John E. Herr, Josh T. Horton, Yuezhi Mao, John D. Chodera, Benjamin P. Pritchard, Yuanqing Wang, Gianni De Fabritiis, Thomas E. Markland
Publikováno v:
Scientific data. 10(1)
Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe the SPICE dataset, a new quantum chemistry dataset for training potent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3e20a406b35e2513d23bc9a04a511ff9
https://pubmed.ncbi.nlm.nih.gov/36599873
https://pubmed.ncbi.nlm.nih.gov/36599873
Autor:
Martin Head-Gordon, Yuezhi Mao, Shaama Mallikarjun Sharada, Jeffrey S. Derrick, Christopher J. Chang, Kareesa J. Kron, Matthias Loipersberger
Publikováno v:
Chemical Science. 12:1398-1414
To facilitate computational investigation of intermolecular interactions in the solution phase, we report the development of ALMO-EDA(solv), a scheme that allows the application of continuum solvent models within the framework of energy decomposition
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::01fc3ea21913444fdaac0edbd5821796
https://doi.org/10.1039/d0sc05327a
https://doi.org/10.1039/d0sc05327a
Publikováno v:
The journal of physical chemistry letters. 13(15)
Core-level spectra of 1s electrons of elements heavier than Ne show significant relativistic effects. We combine advances in orbital-optimized density functional theory (OO-DFT) with the spin-free exact two-component (X2C) model for scalar relativist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5555ec29341fcf214ddd8ce670ea4c1e
https://pubmed.ncbi.nlm.nih.gov/35412838
https://pubmed.ncbi.nlm.nih.gov/35412838
Autor:
Junjie Yang, Zheng Pei, Erick Calderon Leon, Carly Wickizer, Binbin Weng, Yuezhi Mao, Qi Ou, Yihan Shao
Following the formulation of cavity quantum-electrodynamical time-dependent density functional theory (cQED-TDDFT) models [Flick et al., ACS Photonics 6, 2757–2778 (2019) and Yang et al., J. Chem. Phys. 155, 064107 (2021)], here, we report the deri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ed4b382bf724ad946d50cf23d41fcef
https://resolver.caltech.edu/CaltechAUTHORS:20211213-518485000
https://resolver.caltech.edu/CaltechAUTHORS:20211213-518485000