Zobrazeno 1 - 10
of 215
pro vyhledávání: '"E MARKLAND"'
Publikováno v:
ACS Central Science, Vol 10, Iss 11, Pp 2162-2170 (2024)
Externí odkaz:
https://doaj.org/article/bfd7b7a2ec0348068e826892ac09c5ce
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:
Rongfeng Yuan, Joseph A. Napoli, Chang Yan, Ondrej Marsalek, Thomas E. Markland, Michael D. Fayer
Publikováno v:
ACS Central Science, Vol 5, Iss 7, Pp 1269-1277 (2019)
Externí odkaz:
https://doaj.org/article/e0df519ad62a4c5bac760639694209da
Persistent Homology Metrics Reveal Quantum Fluctuations and Reactive Atoms in Path Integral Dynamics
Autor:
Yunfeng Hu, Phonemany Ounkham, Ondrej Marsalek, Thomas E. Markland, Bala Krishmoorthy, Aurora E. Clark
Publikováno v:
Frontiers in Chemistry, Vol 9 (2021)
Nuclear quantum effects (NQEs) are known to impact a number of features associated with chemical reactivity and physicochemical properties, particularly for light atoms and at low temperatures. In the imaginary time path integral formalism, each atom
Externí odkaz:
https://doaj.org/article/fccf67cf7af644fcab80c98725ecf5e8
Publikováno v:
Scientific Reports, Vol 8, Iss 1, Pp 1-9 (2018)
Abstract We derive a rigorous, quantum mechanical map of fermionic creation and annihilation operators to continuous Cartesian variables that exactly reproduces the matrix structure of the many-fermion problem. We show how our scheme can be used to m
Externí odkaz:
https://doaj.org/article/628e7a92033e4d25b5390516afd6e41e
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:
Michael S. Chen, Joonho Lee, Hong-Zhou Ye, Timothy C. Berkelbach, David R. Reichman, Thomas E. Markland
Publikováno v:
Journal of Chemical Theory and Computation.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::59f878db5d25dac548637af233b82893
https://doi.org/10.1021/acs.jctc.2c01203
https://doi.org/10.1021/acs.jctc.2c01203
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:
Matthew W. Kanan, Zhaorui Huang, Cristian P Pacheco Woroch, Michael S. Chen, Thomas E. Markland
Publikováno v:
Chemical Science
Methods to automate structure elucidation that can be applied broadly across chemical structure space have the potential to greatly accelerate chemical discovery. NMR spectroscopy is the most widely used and arguably the most powerful method for eluc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99ba57125ceab5a000f8f250d51db46a
https://doi.org/10.1039/d1sc04105c
https://doi.org/10.1039/d1sc04105c