24 citations · 53 across the 5 of their papers we have counts for
5 papers
The quantum nature of ubiquitous vibrational features revealed for ethylene glycol
Apurba Nandi, Riccardo Conte, Priyanka Pandey +4
Vibrational properties of molecules are of widespread interest and importance in chemistry and biochemistry. The reliability of widely employed approximate computational methods is…
Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials
Qi Yu, Ruitao Ma, Chen Qu +6
Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN)…
-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol
Apurba Nandi, Priyanka Pandey, Paul L. Houston +5
Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation…
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)…
No Headache for PIPs: A PIP Potential for Aspirin Outperforms Other Machine-Learned Potentials
Paul L. Houston, Chen Qu, Qi Yu +4
Assessments of machine-learned (ML) potentials are an important aspect of the rapid development of this field. We recently reported an assessment of the linear-regression permutati…