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4 papers · 1 filter
Permutationally invariant polynomial regression for energies and gradients, using reverse differentiation, achieves orders of magnitude speed-up with high precision compared to other machine learning methods
Paul L. Houston, Chen Qu, Apurba Nandi +3
Permutationally invariant polynomial (PIP) regression has been used to obtain machine-learned (ML) potential energy surfaces, including analytical gradients, for many molecules and…
Enabling rapid and accurate construction of CCSD(T)-level potential energy surface of large molecules using molecular tailoring approach
Subodh S. Khire, Nalini D. Gurav, Apurba Nandi +1
The construction of the potential energy surface (PES) of even a medium-sized molecule employing correlated theory, such as CCSD(T), is an arduous task due to the high computationa…
A CCSD(T)-based permutationally invariant polynomial 4-body potential for water
Apurba Nandi, Chen Qu, Paul L. Houston +2
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/h…
Breaking the Coupled Cluster Barrier for Machine Learned Potentials of Large Molecules: The Case of 15-atom Acetylacetone
Chen Qu, Paul Houston, Riccardo Conte +2
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 function…