Zobrazeno 1 - 10
of 331
pro vyhledávání: '"Markland, Thomas E."'
We describe version 2 of the SPICE dataset, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original dataset by adding much more sampling of chemical space and more data on non-covalent inter
Externí odkaz:
http://arxiv.org/abs/2406.13112
Autor:
Pelaez, Raul P., Simeon, Guillem, Galvelis, Raimondas, Mirarchi, Antonio, Eastman, Peter, Doerr, Stefan, Thölke, Philipp, Markland, Thomas E., De Fabritiis, Gianni
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 step forwa
Externí odkaz:
http://arxiv.org/abs/2402.17660
Autor:
Zariquiey, Francesc Sabanes, Galvelis, Raimondas, Gallicchio, Emilio, Chodera, John D., Markland, Thomas E., de Fabritiis, Gianni
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). We compu
Externí odkaz:
http://arxiv.org/abs/2401.16062
Autor:
Eastman, Peter, Galvelis, Raimondas, Peláez, Raúl P., Abreu, Charlles R. A., Farr, Stephen E., Gallicchio, Emilio, Gorenko, Anton, Henry, Michael M., Hu, Frank, Huang, Jing, Krämer, Andreas, Michel, Julien, Mitchell, Joshua A., Pande, Vijay S., Rodrigues, João PGLM, Rodriguez-Guerra, Jaime, Simmonett, Andrew C., Swails, Jason, Zhang, Ivy, Chodera, John D., De Fabritiis, Gianni, Markland, Thomas E.
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 potentials. Arbitrary PyTorch models can be added
Externí odkaz:
http://arxiv.org/abs/2310.03121
The transport of excess protons and hydroxide ions in water underlies numerous important chemical and biological processes. Accurately simulating the associated transport mechanisms ideally requires utilizing ab initio molecular dynamics simulations
Externí odkaz:
http://arxiv.org/abs/2308.06348
Autor:
Chen, Michael S., Mao, Yuezhi, Snider, Andrew, Gupta, Prachi, Montoya-Castillo, Andrés, Zuehlsdorff, Tim J., Isborn, Christine M., Markland, Thomas E.
Hydrogen bonding interactions with chromophores in chemical and biological environments play a key role in determining their electronic absorption and relaxation processes, which are manifested in their linear and multidimensional optical spectra. Fo
Externí odkaz:
http://arxiv.org/abs/2305.16981
Electron transfer at electrode interfaces to molecules in solution or at the electrode surface plays a vital role in numerous technological processes. However, treating these processes requires a unified and accurate treatment of the fermionic states
Externí odkaz:
http://arxiv.org/abs/2305.01027
The dynamics of many-body fermionic systems are important in problems ranging from catalytic reactions at electrochemical surfaces, to transport through nanojunctions, and offer a prime target for quantum computing applications. Here we derive the se
Externí odkaz:
http://arxiv.org/abs/2212.07003
Linear and nonlinear electronic spectra provide an important tool to probe the absorption and transfer of electronic energy. Here we introduce a pure state Ehrenfest approach to obtain accurate linear and nonlinear spectra that is applicable to syste
Externí odkaz:
http://arxiv.org/abs/2212.06973
Autor:
Chen, Michael S., Lee, Joonho, Ye, Hong-Zhou, Berkelbach, Timothy C., Reichman, David R., Markland, Thomas E.
Obtaining the atomistic structure and dynamics of disordered condensed phase systems from first principles remains one of the forefront challenges of chemical theory. Here we exploit recent advances in periodic electronic structure to show that, by l
Externí odkaz:
http://arxiv.org/abs/2211.16619