Projection-based wavefunction-in-DFT embedding SJR Lee, M Welborn, FR Manby, TF Miller III Accounts of chemical research 52 (5), 1359-1368, 2019 | 111 | 2019 |
Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states T Husch, J Sun, L Cheng, SJR Lee, TF Miller The Journal of Chemical Physics 154 (6), 2021 | 49 | 2021 |
Entos: A quantum molecular simulation package F Manby, T Miller, P Bygrave, F Ding, T Dresselhaus, F Batista-Romero, ... | 38 | 2019 |
Analytical gradients for projection-based wavefunction-in-DFT embedding SJR Lee, F Ding, FR Manby, TF Miller The Journal of Chemical Physics 151 (6), 2019 | 32 | 2019 |
Quantum chemistry common driver and databases (QCDB) and quantum chemistry engine (QCEngine): Automation and interoperability among computational chemistry programs DGA Smith, AT Lolinco, ZL Glick, J Lee, A Alenaizan, TA Barnes, ... The Journal of chemical physics 155 (20), 2021 | 26 | 2021 |
Analytical gradients for molecular-orbital-based machine learning SJR Lee, T Husch, F Ding, TF Miller The Journal of Chemical Physics 154 (12), 2021 | 16 | 2021 |
Density-based errors in mixed-basis mean-field electronic structure, with implications for embedding and QM/MM methods SJR Lee, K Miyamoto, F Ding, FR Manby, TF Miller III Chemical Physics Letters 683, 375-382, 2017 | 5 | 2017 |
Intermolecular interactions and proton transfer in the hydrogen halide–superoxide anion complexes SJR Lee, JW Mullinax, HF Schaefer Physical Chemistry Chemical Physics 18 (8), 6201-6208, 2016 | 1 | 2016 |
Combining high-and low-level electronic structure theories for the efficient exploration of potential energy surfaces SJR Lee California Institute of Technology, 2021 | | 2021 |