Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Xingyi Guan"'
Autor:
Eric C.-Y. Yuan, Anup Kumar, Xingyi Guan, Eric D. Hermes, Andrew S. Rosen, Judit Zádor, Teresa Head-Gordon, Samuel M. Blau
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-9 (2024)
Abstract Identifying transition states—saddle points on the potential energy surface connecting reactant and product minima—is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train a fully d
Externí odkaz:
https://doaj.org/article/f4975b34f01c443e85ecb32c6c0675d8
Autor:
Xingyi Guan, Akshaya Das, Christopher J. Stein, Farnaz Heidar-Zadeh, Luke Bertels, Meili Liu, Mojtaba Haghighatlari, Jie Li, Oufan Zhang, Hongxia Hao, Itai Leven, Martin Head-Gordon, Teresa Head-Gordon
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-7 (2022)
Measurement(s) ab initio energies and forces of hydrogen combustion Technology Type(s) density functional theory • ab initio molecular dynamics • normal modes Factor Type(s) cartesian coordinates
Externí odkaz:
https://doaj.org/article/55f05403903645dfa2269feb1f613dfd
Recent Advances for Improving the Accuracy, Transferability, and Efficiency of Reactive Force Fields
Autor:
Adri C. T. van Duin, Jamil Hossain, Songchen Tan, Hongxia Hao, Benjamin Evangelisti, Jason Koski, Xingyi Guan, Katheryn A. Penrod, Hasan Metin Aktulga, Itai Leven, Teresa Head-Gordon, Stan Gerald Moore, Mahbubul Islam, Dooman Akbarian
Publikováno v:
Journal of Chemical Theory and Computation. 17:3237-3251
Reactive force fields provide an affordable model for simulating chemical reactions at a fraction of the cost of quantum mechanical approaches. However, classically accounting for chemical reactivity often comes at the expense of accuracy and transfe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ebc72cb3f21b11e8219517e31ec4738b
https://doi.org/10.1021/acs.jctc.1c00118
https://doi.org/10.1021/acs.jctc.1c00118
Publikováno v:
J Am Chem Soc
Journal of the American Chemical Society, vol 144, iss 11
Journal of the American Chemical Society, vol 144, iss 11
The biosynthesis of pyrroindomycins A and B features a complexity-building [4 + 2] cycloaddition cascade, which generates the spirotetramate core under the catalytic effects of monofunctional Diels-Alderases PyrE3 and PyrI4. We recently showed that t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54419aec231f5107060f090911191e7b
https://europepmc.org/articles/PMC9292823/
https://europepmc.org/articles/PMC9292823/
Autor:
Jagna Witek, Joseph P. Heindel, Xingyi Guan, Itai Leven, Hongxia Hao, Pavithra Naullage, Allen LaCour, Selim Sami, M. F. S. J. Menger, D. Vale Cofer-Shabica, Eric Berquist, Shirin Faraji, Evgeny Epifanovsky, Teresa Head-Gordon
We present a new software package called M-Chem that is designed from scratch in C++ and parallelised on shared-memory multi-core architectures to facilitate efficient molecular simulations. Currently, M-Chem is a fast molecular dynamics (MD) engine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::41f852605010334275852288c8810c57
Publikováno v:
Journal of chemical information and modeling. 61(9)
The electrostatic potential (ESP) is a powerful property for understanding and predicting electrostatic charge distributions that drive interactions between molecules. In this study, we compare various charge partitioning schemes including fitted cha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab41fd5ed7412fc1983cb8760d730f33
https://pubmed.ncbi.nlm.nih.gov/34490776
https://pubmed.ncbi.nlm.nih.gov/34490776
Autor:
Mojtaba Haghighatlari, Jie Li, Xingyi Guan, Oufan Zhang, Akshaya Das, Christopher J. Stein, Farnaz Heidar-Zadeh, Meili Liu, Martin Head-Gordon, Luke Bertels, Hongxia Hao, Itai Leven, Teresa Head-Gordon
We report a new deep learning message passing network that takes inspiration from Newton's equations of motion to learn interatomic potentials and forces. With the advantage of directional information from trainable latent force vectors, and physics-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d68aa9379bbb34a8b31537146696e765
http://arxiv.org/abs/2108.02913
http://arxiv.org/abs/2108.02913
Autor:
Xingyi Guan, Mojtaba Haghighatlari, Yuchen Liu, Jie Li, Teresa Head-Gordon, Farnaz Heidar-Zadeh
Publikováno v:
Chem
Chem, vol 6, iss 7
Chem, vol 6, iss 7
Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we focus on the interplay of machine learning method with the chemically
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d65eb64757ec8625bdf5785d894f5c1d
https://europepmc.org/articles/PMC7373218/
https://europepmc.org/articles/PMC7373218/