Thomas E Markland
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Explore the profile of Thomas E Markland including associated specialties, affiliations and a list of published articles.
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71
Citations
786
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Recent Articles
1.
Zheng C, Mao Y, Markland T, Boxer S
J Am Chem Soc
. 2025 Feb;
147(7):6227-6235.
PMID: 39930554
C-H···O hydrogen bonds are formed in systems ranging from biomolecular complexes to small-molecule structures. Previous work has focused on the blueshifts in the C-H stretching frequency () induced by these...
2.
Hu F, Chen M, Rotskoff G, Kanan M, Markland T
ACS Cent Sci
. 2024 Dec;
10(11):2162-2170.
PMID: 39634219
Rapid determination of molecular structures can greatly accelerate workflows across many chemical disciplines. However, elucidating structure using only one-dimensional (1D) NMR spectra, the most readily accessible data, remains an extremely...
3.
Eastman P, Pritchard B, Chodera J, Markland T
J Chem Theory Comput
. 2024 Sep;
20(19):8583-8593.
PMID: 39318326
We describe version 2 of the SPICE data set, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original data set by adding much...
4.
Pelaez R, Simeon G, Galvelis R, Mirarchi A, Eastman P, Doerr S, et al.
J Chem Theory Comput
. 2024 May;
20(10):4076-4087.
PMID: 38743033
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in TorchMD-Net software, a pivotal step...
5.
Pelaez R, Simeon G, Galvelis R, Mirarchi A, Eastman P, Doerr S, et al.
ArXiv
. 2024 Mar;
PMID: 38463504
Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in the TorchMD-Net software, a pivotal...
6.
Sabanes Zariquiey F, Galvelis R, Gallicchio E, Chodera J, Markland T, De Fabritiis G
J Chem Inf Model
. 2024 Feb;
64(5):1481-1485.
PMID: 38376463
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM)....
7.
Sabanes Zariquiey F, Galvelis R, Gallicchio E, Chodera J, Markland T, De Fabritiis G
ArXiv
. 2024 Feb;
PMID: 38351937
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural network potential and molecular mechanics methodology (NNP/MM)....
8.
Eastman P, Galvelis R, Pelaez R, Abreu C, Farr S, Gallicchio E, et al.
J Phys Chem B
. 2023 Dec;
128(1):109-116.
PMID: 38154096
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning...
9.
Eastman P, Galvelis R, Pelaez R, Abreu C, Farr S, Gallicchio E, et al.
ArXiv
. 2023 Nov;
PMID: 37986730
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning...
10.
Galvelis R, Varela-Rial A, Doerr S, Fino R, Eastman P, Markland T, et al.
J Chem Inf Model
. 2023 Sep;
63(18):5701-5708.
PMID: 37694852
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number...