160 citations
- Atlanta University CenterUS24 papers
- Emory UniversityUS24 papers
- Cornell UniversityUS13 papers
- University of MilanIT12 papers
- Georgia Institute of TechnologyUS11 papers
- Yale UniversityUS6 papers
- University of LuxembourgLU4 papers
- University of Maryland, College ParkUS4 papers
- Fudan UniversityCN3 papers
- Rice UniversityUS2 papers
- Chinese Academy of SciencesCN1 paper
- Dalian Institute of Chemical PhysicsCN1 paper
25 papers
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…
Towards accurate spin-orbit splittings from relativistic multireference electronic structure theory
Zijun Zhao, Francesco A. Evangelista
Most nonrelativistic electron correlation methods can be adapted to account for relativistic effects, as long as the relativistic molecular spinor integrals are available, from eit…
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…