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27 papers · 1 filter
Equation-of-motion internally contracted multireference unitary coupled-cluster theory
Shuhang Li, Zijun Zhao, Francesco A. Evangelista
The accurate computation of excited states remains a challenge in electronic structure theory, especially for systems with a ground state that requires a multireference treatment.…
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)…
Polaritonic control of blackbody infrared radiative dissociation
Enes Suyabatmaz, Gustavo J. R. Aroeira, Raphael F. Ribeiro
Vibrational strong light-matter coupling offers a promising approach for controlling chemical reactivity with infrared microcavities. While recent research has examined potential m…
-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…
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…