报告题目 (Title)：Accurate and Transferable Molecular-orbital-based machine learning for molecular modelling（用于分子建模的、精确且可迁移的、基于分子轨道的机器学习方法）
报告人 (Speaker)：Sherry Cheng 程立雪（美国加州理工学院CalTech）
报告时间 (Time)：2021年9月3日 (周五) 10:30 (UTC+8)
报告地点 (Place)：线上Zoom
https://us06web.zoom.us/j/82873168207?pwd=bDM0UjJUbEl1YXhYWkt4UGVjUjlhdz09
会议号：828 7316 8207
密码：210903
邀请人 (Inviter)：李永乐 副教授
摘要 (Abstract)：
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, molecular orbital based (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, and we further support this by applying PESs obtained from MOB-ML to simulate diffusion Monte Carlo accurately and efficiently for computational spectroscopy.
References:
[1] M. Welborn, L. Cheng, T. F. Miller III. J. Chem. Theory Comput.14, 4772–4779 (2018)
[2] L. Cheng, M. Welborn, A. S. Christensen, T. F. Miller III. J. Chem. Phys. 150, 131103 (2019)
[3] L. Cheng, N. B. Kovachki, M. Welborn, T. F. Miller III. J. Chem. Theory Comput. 15, 6668–6677. (2019)
[4] T. Husch, J. Sun, L. Cheng, S. J. Lee, T. F. Miller III. J. Chem. Phys. 154, 064108. (2021)
[6] J. Sun, L. Cheng, T.F. Miller III, In submission to NeurIPS AI for Science Workshop. (2021)
[7] S. J. R. Lee, T. Husch, F. Ding, and T. F. Miller III, J. Chem. Phys. 154, 124120 (2021)
[8] R. J. DiRisio*, L. Cheng,* M. A. Boyer, J. M. Finney, F. Lu, S. J. R. Lee, J. E. Deustua, Miller III, T. F.; McCoy, A. B. In submission to J. Phys. Chem. A (2021). (*co-first author)