7 papers · 1 filter
"Gold-Standard" -Machine Learned and Transferable Potential for Linear Alkanes
Chen Qu, Thomas C. Allison, Apurba Nandi +4
The conformational properties of linear alkanes, CH, have been of intense interest for many years. Experiments and corresponding electronic structure calculations were…
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)…
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…
-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…
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…