Publications
Publications:
[19] Cheng, L.*; Shen, J.*; Helie, J.; Sun, L. In preparation. (*co-first author)
[18] Jacobson, G.*; Cheng, L.*; Sun, J; Bhethanabotla, V; McCoy, A. B., Machine Learning Approaches for Developing Potential Surfaces: Applications to OH(H2O)n (n=1-3) Complexes. ChemRxiv, 10.26434/chemrxiv-2024-5c808 *co-first author)Link
[17] Cheng, L.*; Szabó, P.B.*; Schätzle, Z.*; Kooi, D.; Köhler, J.; Noé, F.; Gori-Giorgi, P.; Foster, A. Highly Accurate Real-space Electron Densities with Neural Networks, arXiv:2409.01306.(*co-first author)Link
[16] Sun, J.; Cheng, L.; Zhang, S.-X. Stabilizer ground state: Theory, algorithms and applications. arXiv: 2403.08441 (2024).Link
[15] Cheng, L.*; Chen, Y.-Q.*; Zhang, S.-X.; Zhang, S. Error-mitigated quantum approximate optimization via learning-based adaptive optimization. (*co-first author) Commun. Phys., 2024.Link
[14] Sun, J.; Cheng, L.; Li, W. Towards chemical accuracy with shallow quantum circuits: A Clifford-based Hamiltonian engineering approach. J. Chem. Theory Comput., 2024.Link
[13] MR AI4Science^, MA Quantum. The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4. arXiv:2311.07361 (2023).Link (^Main contributor to Chapter 4, see Authorship and contribution list)
[12] Li, W; Allcock, J.; Cheng, L.; Zhang, S.-X.; Chen, Y.-Q.; Mailoa, J.P.; Zhang, S. TenCirChem: An efficient quantum computational chemistry package for the NISQ era. J. Chem. Theory Comput., 2023.Link
[11] Cheng, L.; Sun, J.; Deustua, J. E.; Bhethanabotla, V. C.; Miller III, T. F. Molecular-orbital-based machine learning for open-shell and multi-reference systems with kernel addition Gaussian process regression. J. Chem. Phys., 2022. Link
[10] Sun, J.; Cheng, L.; Miller III, T. F. Molecular dipole moment learning via rotationally equivariant Gaussian process regression with derivatives in molecular-orbital-based machine learning. J. Chem. Phys., 2022. Link
[9] Cheng, L.*; Yang, Z.*; Liao, B.; Hsieh, C.; Zhang, S. ODBO: Bayesian Optimization with prescreening for directed protein evolution. arXiv:2205.09548 (2022). (*co-first author) Link
[8] Cheng, L., Sun, J. & Miller III, T. F. Accurate molecular-orbital-based machine learning energies via unsupervised clustering of chemical space. J. Chem. Theory Comput., 2022. Link
[7] Lu, F.*; Cheng, L.*; DiRisio, R. J.*; Finney, J. M.; Boyer, M. A.; Moonkaen, P.; Sun, J.; Lee, S. J. R.; Deustua, J. E.; Miller III, T. F.; McCoy, A. B. Fast near ab initio potential energy surfaces using machine learning. J. Phys. Chem. A, 2022. (*co-first author) Link
[6] Sun, J.; Cheng, L.; Miller III, T. F. Molecular energy learning using alternative blackbox matrix-matrix multiplication (AltBBMM) algorithm for exact Gaussian process. arXiv:2109.09817 (2021). (Accepted for presentation at the NeurIPS 2021 AI for Science Workshop) Link
[5] Husch, T.; Sun, J.; Cheng, L.; Lee, S. J. R.; Miller III, T. F. Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states. J. Chem. Phys., 2021. Link
[4] Cheng, L.; Kovachki, N; Welborn, M.; Miller III, T. F. Regression clustering for improved accuracy and training costs with molecular-orbital-based machine learning. J. Chem. Theory Comput., 2019. Link
[3] Cheng, L.; Welborn, M.; Miller III, T. F. A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules. J. Chem. Phys., 2019. Link
[2] Welborn, M.; Cheng, L.; Miller III, T. F. Transferability in machine learning for electronic structure via the molecular orbital basis. J. Chem. Theory Comput., 2018. Link (Highlighted with commentary in C&EN and Caltech News; #5 of 6595 JCTC papers in Altmetric attention score)
[1] Knowles, D. B.; Shkel, I. A.; Phan, N. M.; Sternke, M.; Lingeman, E.; Cheng, X.; Cheng, L.; O’Connor, K.; Record, M. T. Chemical interactions of polyethylene glycols (PEGs) and glycerol with protein functional groups: Applications to effects of PEG and glycerol on protein processes. Biochemistry, 2015, 54 (22), 3528–3542. Link
Patent:
Miller III, T. F.; Welborn, M.; Cheng, L.; Husch, T.; Song, J.; Kovachiki, N.; Burov, D.; Teh, Y.S.; Anandkumar, A.; Ding, F.; Lee, S.J.R.; Qiao, Z.; Lale, A.S. Systems and methods for determining molecular structures with molecular-orbital-based features. U.S. Patent 16817489, 2020 Link