9 citations · 9 across the 3 of their papers we have counts for
3 papers
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…
Impact of spin-entropy on the thermoelectric properties of a 2D magnet
Alessandra Canetta, Serhii Volosheniuk, Sayooj Satheesh +11
Heat-to-charge conversion efficiency of thermoelectric materials is closely linked to the entropy per charge carrier. Thus, magnetic materials are promising building blocks for hig…
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)…