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pro vyhledávání: '"Bowman, Joel M."'
Autor:
Ge, Fuchun, Wang, Ran, Qu, Chen, Zheng, Peikun, Nandi, Apurba, Conte, Riccardo, Houston, Paul L., Bowman, Joel M., Dral, Pavlo O.
Machine learning potentials (MLPs) are widely applied as an efficient alternative way to represent potential energy surfaces (PES) in many chemical simulations. The MLPs are often evaluated with the root-mean-square errors on the test set drawn from
Externí odkaz:
http://arxiv.org/abs/2403.11216
Autor:
Pandey, Priyanka, Arandhara, Mrinal, Houston, Paul L., Qu, Chen, Conte, Riccardo, Bowman, Joel M., Ramesh, Sai G.
Here we assess two machine-learned potentials, one using the symmetric gradient domain machine learning (sGDML) method and one based on permutationally invariant polynomials (PIPs). These are successors to a PIP potential energy surface (PES) reporte
Externí odkaz:
http://arxiv.org/abs/2402.11158
Publikováno v:
J. Phys. Chem. A. 2022
The temperature dependence of the thermal rate constant for the reaction Cl($^2$P) + CH$_4$ $\rightarrow$ CH$_3$ + HCl is calculated using a Gaussian Process machine learning (ML) approach to train on and predict thermal rate constants over a large t
Externí odkaz:
http://arxiv.org/abs/2206.12443
$\Delta$-Machine Learning ($\Delta$-ML) has been shown to effectively and efficiently bring a low-level ML potential energy surface to CCSD(T) quality. Here we propose extending this approach to general force fields, which implicitly or explicitly co
Externí odkaz:
http://arxiv.org/abs/2206.04254
Publikováno v:
J. Chem. Theory Comput. 2022
Ethanol is a molecule of fundamental interest in combustion, astrochemistry, and condensed phase as a solvent. It is characterized by two methyl rotors and $trans$ ($anti$) and $gauche$ conformers, which are known to be very close in energy. Here we
Externí odkaz:
http://arxiv.org/abs/2206.02297
Publikováno v:
J. Chem. Phys. 156, (2022) 240901
There has been great progress in developing methods for machine-learned potential energy surfaces. There have also been important assessments of these methods by comparing so-called learning curves on datasets of electronic energies and forces, notab
Externí odkaz:
http://arxiv.org/abs/2205.11663
Publikováno v:
J. Phys. Chem. Lett. 2022, 13, 5068-5074
Many model potential energy surfaces (PESs) have been reported for water; however, none are strictly from "first principles". Here we report such a potential, based on a many-body representation at the CCSD(T) level of theory up to the ultimate 4-bod
Externí odkaz:
http://arxiv.org/abs/2204.01804
Permutationally invariant polynomial (PIP) regression has been used to obtain machine-learned (ML) potential energy surfaces, including analytical gradients, for many molecules and chemical reactions. Recently, the approach has been extended to moder
Externí odkaz:
http://arxiv.org/abs/2112.01734
Publikováno v:
J. Phys. Chem. Lett. 2021, 12, 10318-10324
We report a permutationally invariant polynomial (PIP) potential energy surface for the water 4-body interaction. This 12-atom PES is a fit to 2119, symmetry-unique, CCSD(T)-F12a/haTZ (aug-cc-pVTZ basis for 'O' atom and cc-pVTZ basis for 'H' atom) 4-
Externí odkaz:
http://arxiv.org/abs/2107.05881
Publikováno v:
J. Phys. Chem. Lett. 12 (2021) 4902-4909
Machine-learned potential energy surfaces (PESs) for molecules with more than 10 atoms are typically forced to use lower-level electronic structure methods such as density functional theory and second-order Moller-Plesset perturbation theory (MP2). W
Externí odkaz:
http://arxiv.org/abs/2103.12333