18 citations · 54 across the 14 of their papers we have counts for
5 papers · 1 filter
Predictive Free Energy Simulations Through Hierarchical Distillation of Quantum Hamiltonians
Chenghan Li, Garnet Kin-Lic Chan
Obtaining the free energies of condensed phase chemical reactions remains computationally prohibitive for high-level quantum mechanical methods. We introduce a hierarchical machine…
Reactive Chemistry at Unrestricted Coupled Cluster Level: High-throughput Calculations for Training Machine Learning Potentials
Alice E. A. Allen, Rui Li, Sakib Matin +8
Accurately modeling chemical reactions at the atomistic level requires high-level electronic structure theory due to the presence of unpaired electrons and the need to properly des…
Accurate crystal field Hamiltonians of single-ion magnets at mean-field cost
Linqing Peng, Shuanglong Liu, Xing Zhang +4
The effective crystal field Hamiltonian provides the key description of the electronic properties of single-ion magnets, but obtaining its parameters from ab initio computation is…
Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework
Divya Suman, Jigyasa Nigam, Sandra Saade +5
Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities, di…
Towards Excitations and Dynamical Quantities in Correlated Lattices with Density Matrix Embedding Theory
Shuoxue Li, Chenghan Li, Huanchen Zhai +1
Density matrix embedding theory (DMET) provides a framework to describe ground-state expectation values in strongly correlated systems, but its extension to dynamical quantities is…