Zobrazeno 1 - 10
of 147
pro vyhledávání: '"James J. Shepherd"'
Autor:
Peng Dai, Jonathan K. Williams, Chi Zhang, Matthew Welborn, James J. Shepherd, Tianyu Zhu, Troy Van Voorhis, Mei Hong, Bradley L. Pentelute
Publikováno v:
Scientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
Abstract Natural enzymes use local environments to tune the reactivity of amino acid side chains. In searching for small peptides with similar properties, we discovered a four-residue π-clamp motif (Phe-Cys-Pro-Phe) for regio- and chemoselective ary
Externí odkaz:
https://doaj.org/article/65ff2ecec9fc40949a8979455dbaebf8
Autor:
Peng Dai, Chi Zhang, Matthew Welborn, James J. Shepherd, Tianyu Zhu, Troy Van Voorhis, Bradley L. Pentelute
Publikováno v:
ACS Central Science, Vol 2, Iss 9, Pp 637-646 (2016)
Externí odkaz:
https://doaj.org/article/1e3c3ac469a24ac5a4b2d39b2667a4ed
Autor:
J. S. Spencer, N. S. Blunt, W. A. Vigor, Fionn D. Malone, W. M. C. Foulkes, James J. Shepherd, A. J. W. Thom
Publikováno v:
Journal of Open Research Software, Vol 3, Iss 1, Pp e9-e9 (2015)
The HANDE quantum Monte Carlo project offers accessible stochastic algorithms for general use for scientists in the field of quantum chemistry. HANDE is an ambitious and general high-performance code developed by a geographically-dispersed team with
Externí odkaz:
https://doaj.org/article/76bb234af30741a79d5c85ac681fdcb9
Autor:
Arjun Paudel, Emily J. Campbell, William Z. Van Benschoten, Grant W. Otting, James J. Shepherd, Ned B. Bowden
Publikováno v:
Macromolecules. 56:3721-3730
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e77412c70168a30146aef5466e3d6c60
https://doi.org/10.1021/acs.macromol.2c02507
https://doi.org/10.1021/acs.macromol.2c02507
Finite size error is commonly removed from coupled cluster theory calculations by $N^{-1}$ extrapolations over correlation energy calculations of different system sizes ($N$), where the $N^{-1}$ scaling comes from the total energy. However, previous
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67260ae22e28ace92f8fb79fb46ed526
Publikováno v:
Journal of Chemical Theory and Computation
Density matrix quantum Monte Carlo (DMQMC) is a recently-developed method for stochastically sampling the $N$-particle thermal density matrix to obtain exact-on-average energies for model and \emph{ab initio} systems. We report a systematic numerical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::121f8ac837ea7bc098184855dd0010fd
https://doi.org/10.1021/acs.jctc.1c00078
https://doi.org/10.1021/acs.jctc.1c00078
Publikováno v:
Journal of Chemical Theory and Computation. 16:1029-1038
We here apply the recently developed initiator density matrix quantum Monte Carlo (i-DMQMC) to a wide range of chemical environments using atoms and molecules in vacuum. i-DMQMC samples the exact density matrix of a Hamiltonian at finite temperature
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5777e1c51991c4cb1b24f7402fe613e4
https://doi.org/10.1021/acs.jctc.9b01080
https://doi.org/10.1021/acs.jctc.9b01080
The density matrix quantum Monte Carlo (DMQMC) set of methods stochastically samples the exact N-body density matrix for interacting electrons at finite temperature. We introduce a simple modification to the interaction picture DMQMC (IP-DMQMC) metho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::58b1760174d7754ebba1b034fa9cbfbe
http://arxiv.org/abs/2108.06252
http://arxiv.org/abs/2108.06252
Autor:
Kyounghoon Lee, Javier A. Luna, Suzanne C. Bart, James J. Shepherd, Courtney M. Donahue, Austin Benson, Hayley R. Petras, Scott K. Shaw, Ezra J. Coughlin, Scott R. Daly, Amani Kibasa, Kyle D. Spielvogel, Ryan Salacinski
Publikováno v:
Inorganic Chemistry. 58:12756-12774
The continued development of redox-active ligands requires an understanding as to how ligand modifications and related factors affect the locus of redox activity and spin density in metal complexes...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::430071f5bb75618148b32ff04aea9b7e
https://doi.org/10.1021/acs.inorgchem.9b01675
https://doi.org/10.1021/acs.inorgchem.9b01675
Autor:
Eugenio Vitale, Simon D. Smart, Péter Jeszenszki, Catherine Overy, James J. Shepherd, Nick S. Blunt, Werner Dobrautz, Oskar Weser, Alexander Y. Lozovoi, Hongjun Luo, Lauretta R. Schwarz, Dongxia Ma, Giovanni Li Manni, Khaldoon Ghanem, Robert J. Anderson, Nikolay A. Bogdanov, Pradipta Kumar Samanta, Markus Rampp, Niklas Liebermann, Deidre Cleland, Kai Guther, George H. Booth, Nike Dattani, Ali Alavi, Florian Merz
Publikováno v:
The Journal of chemical physics. 153(3)
We present NECI, a state-of-the-art implementation of the Full Configuration Interaction Quantum Monte Carlo algorithm, a method based on a stochastic application of the Hamiltonian matrix on a sparse sampling of the wave function. The program utiliz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a05be9660f607801d1221c9bfab03f7
https://pubmed.ncbi.nlm.nih.gov/32716189
https://pubmed.ncbi.nlm.nih.gov/32716189