Ka Un Lao

Affiliations: 
Chemistry Virginia Commonwealth University, Richmond, VA, United States 
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"Ka Lao"
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Lao KU, Villot C. (2024) Improving second-order Møller-Plesset perturbation theory for noncovalent interactions with the machine learning-corrected ab initio dispersion potential. The Journal of Chemical Physics. 160
Villot C, Lao KU. (2024) Ab initio dispersion potentials based on physics-based functional forms with machine learning. The Journal of Chemical Physics. 160
Ballesteros F, Lao KU. (2024) Analysis of two overlapping fragmentation approaches in density matrix construction: GMBE-DM ADMA. Physical Chemistry Chemical Physics : Pccp. 26: 4386-4394
Ballesteros F, Tan JA, Lao KU. (2023) An accurate and efficient fragmentation approach via the generalized many-body expansion for density matrices. The Journal of Chemical Physics. 159
Villot C, Huang T, Lao KU. (2023) Accurate prediction of global-density-dependent range-separation parameters based on machine learning. The Journal of Chemical Physics. 159
Villot C, Lao KU. (2023) Electronic structure theory on modeling short-range noncovalent interactions between amino acids. The Journal of Chemical Physics. 158: 094301
Villot C, Ballesteros F, Wang D, et al. (2022) Coupled Cluster Benchmarking of Large Noncovalent Complexes in L7 and S12L as Well as the C Dimer, DNA-Ellipticine, and HIV-Indinavir. The Journal of Physical Chemistry. A
Ballesteros F, Lao KU. (2021) Accelerating the Convergence of Self-Consistent Field Calculations Using the Many-Body Expansion. Journal of Chemical Theory and Computation. 18: 179-191
Sparrow ZM, Ernst BG, Joo PT, et al. (2021) NENCI-2021. I. A large benchmark database of non-equilibrium non-covalent interactions emphasizing close intermolecular contacts. The Journal of Chemical Physics. 155: 184303
Carter-Fenk K, Lao KU, Herbert JM. (2021) Predicting and Understanding Non-Covalent Interactions Using Novel Forms of Symmetry-Adapted Perturbation Theory. Accounts of Chemical Research
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