most citedTell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine

9 citations · 9 across the 3 of their papers we have counts for

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physics.chem-ph2024

Can We Learn the Energy of Sublimation of Ice from Water Clusters?

Joe Bowman, Qi Yu, Chen Qu +2

This short paper reports a study of the electronic dissociation energies, De, of water clusters from direct ab initio (mostly CCSD(T)) calculations and the q-AQUA and MB-pol potent…

physics.chem-ph2024★ 9 cited

Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine

Fuchun Ge, Ran Wang, Chen Qu +6

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…

physics.chem-ph2024

Assessing PIP and sGDML Potential Energy Surfaces for H3O2-

Priyanka Pandey, Mrinal Arandhara, Paul L. Houston +4

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)…