Research

I’m working at Microsoft Research as a researcher on DeepQMC for electronic structure. I graduated with Prof. Thomas F. Miller III @ CCE, Caltech in 2022 on AI for quantum chemistry (MOB-ML). Before joining Microsoft Research AI4Science lab, I was a researcher working on the interdisplinary studies between quantum sciences & machine learning in Tencent Quantum Lab for around 9 months. The topics/projects I am working/worked on are listed as below.

Microsoft Research work:

AI for chemistry

Deep Quantum Monte Carlo for Electronic Structure
[1] 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

LLMs for Scientific Discovery: Evaluate and explore the ability of LLMs in many scientific tasks.
[1] 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)

Other independent work:

AI for quantum computing & Quantum machine learning:

The goal is to apply the state-of-the-art ML tools to facilitate more efficient quantum algorithm realizations and quantum resource usages.

[1] Cheng, L.^*; Chen, Y.-Q.^*; Zhang, S.-X.; Zhang, S. Error-mitigated quantum approximate optimization via learning-based adaptive optimization. (*co-first author) arXiv:2303.14877 (2023).Link

AI for biology: Bayesian Optimization for experimental design

[1] Cheng, L.^*; Yang, Z^*; Liao, B; Hsieh, C; Zhang, S. ODBO: Bayesian optimization with search space prescreening for directed protein evolution. Link

Quantum computing for quantum chemistry

[1] 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

[2] 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

Caltech work:

AI for electronic structure: MOB-ML

Quantum simulation is a is a powerful tool for chemists to understand the chemical processes and discover their nature accurately by expensive wavefunction theory (WFT) or approximately by cheap density function theory (DFT). However, the cost-accuracy trade-offs in electronic structure methods limit the application of quantum simulation to large chemical and biological systems. An accurate, transferable, and physical-driven molecular modelling framework, i.e., molecular orbital based machine learning (MOB-ML), is introduced to provide accurate wavefunction-quality molecular descriptions with at most DFT level computational cost. Preserving all the physical constraints, MOB features represent the chemical space faithfully in both supervised learning for molecular property by scalable exact Gaussian processes and unsupervised learning for chemical space explorations. MOB-ML is not only the most accurate method in the low data regime, but also scalable to big data modelling to provide a universal density matrix functional. As an exciting and general new tool to tackle various problems in chemistry, MOB-ML offers great accuracies of predicting total energies of organic and transition-metal containing molecules, non-covalent interactions in the protein backbone-backbone, and transition-state energies. The availability of analytical gradient of MOB-ML opens an avenue of applying MOB-ML to provide accurate potential energy surfaces (PESs) for molecular dynamics simulations.

Selected publications:

[1] 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

[2] Lu, F.^*; Cheng, L.^*; DiRisio, R. J.^*; Finney, J. M.; Boyer, M. A.; Lu, F.; 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

[3] 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

[4] 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

[5] 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)

AI for biology: INSPIRE

Predict the thermaldynamics properties of nucleic acids using their secondary structures.