Nice to meet you! I’m Weiliang Luo (罗伟梁) from Dalian, Liaoning, China. I’m a Ph. D. candidate at MIT Chemistry, advised by Prof. Heather J. Kulik.

My research interest is molecular modeling/simulation, rational molecular design, and AI for chemistry. I’m working on the multiscale modeling of enzymatic catalysis across electronic and atomistic structures, including:

Next-generation machine learning potential for biochemical reactive systems.
Automatic cluster model and QM/MM simulation workflow.
Mechanism study of novel enzymatic reactions.
Application of density functional theory and correlated wave function methods on biochemistry.

My undergraduate studies were finished at CCME, PKU. I conducted my undergraduate research on graph neural networks (GNN) for ADME/T property prediction at Molecular Design Lab, supervised by Prof. Luhua Lai and Dr. Jianfeng Pei.

When I was a research intern at DP Technology, I focused on cutting-edge algorithms for the free energy evaluation of small drug molecules. I contributed to the molecular dynamics (MD) simulation, chemoinformatics, and software engineering in the free energy perturbation (FEP) module of the next-generation drug design platform Hermite. My undergraduate thesis project, supported by DP, developed a pK _a prediction model for drug-like molecules with complex acid-base equilibrium, and accurate FEP calculation augmented by the thermodynamic correction from this pK _a model and charge-changing alchemical transformation algorithm.

I believe that scientific computation and machine learning will replace serendipity with certainty in traditional, labor-intensive chemical discovery. However, I’m always wary of data-driven methods when it comes to real, risk-sensitive scenarios with limited quality and quantity of available data. Therefore, I’m on my way to integrating physics and statistics, understanding the relationship between data and models, and decoding the structure of the chemical space. Hope to find ones who are also excited about this vision.

Thank you for your visiting!

If you like the template of this homepage, you can refer to Yi Ren’s GitHub Repository acad-homepage.

🔥 News

2025.10: I won a ChemPhysChem Poster Award at SMLQC 2025.
2025.10: The ML for TMC design review paper I contributed in with my labmates was published on COChE!
2025.07: My collaborative paper with Martin Head-Gordon’s lab on regularized 2nd-order perturbation theory was published!
2025.07: I became a MolSSI Software Fellow!
2025.05: I passed my PhD qualification exam and became a PhD candidate!
2025.04: Music102 has been accepted by the ICMC 2025, Special CCOM AI Paper track!
2025.03: A successful talk on ACS Spring 2025 about Uni-pKa.

📝 Publications

Exploring beyond Experiment: Generating High-Quality Datasets of Transition Metal Complexes with Quantum Chemistry and Machine Learning, Jacob W. Toney, Aaron G. Garrison, Weiliang Luo, Roland G. St. Michel, Sukrit Mukhopadhyay, Heather J. Kulik, Curr. Opin. Chem. Eng. 2025, 50, 101189.

Preprint

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling

Mouyang Cheng, Weiliang Luo, Hao Tang, Bowen Yu, Yongqiang Cheng, Weiwei Xie, Ju Li*, Heather J. Kulik*, Mingda Li*

Code

Aiming to overcome valency imbalance in material generation.

Regularized Second-Order Møller-Plesset Theory: Linear Scaling Implementation and Assessment on Large-Molecule Problems, Zhenling Wang, Tianyi Shi, Weiliang Luo, Heather J. Kulik, Yang Liu, Xiaoye S. Li, Martin Head-Gordon*, J. Chem. Theory Comput. 2025, 21, 14, 6887–6904.

ICMC 2025

Music102: An D₁₂-equivariant transformer for chord progression accompaniment

Weiliang Luo*

Code | Conference Panel

A trial of a combination of symmetry in music theory and deep learning.

JACS Au

(Cover Article) Bridging Machine Learning and Thermodynamics for Accurate pKa Prediction

Weiliang Luo, Gengmo Zhou, Zhengdan Zhu, Guolin Ke, Zhewei Wei, Zhifeng Gao*, Hang Zheng*

Preprint at Chemrxiv | Code | Datasets | Application: Ligand Protonation with Uni-pKa Free Energy Ranking | Notebook Demo

Rigorous interpretation and modeling of pKa data with thermodynamic consistency.
State-of-the-art performance among ML-based pKa prediction models.
Fast enumeration and ranking for the protonation states of molecules under various pH conditions.

🎖 Honors and Awards

2025.10, ChemPhysChem Post Award at SMLQC 2025
2025.07, MolSSI’s Software Fellow
2024.08, Department of Chemistry Award for Outstanding Teaching
2023.06, “Chemistry Star” Academic Award (Undergraduate) (Top 2%)
2023.03, Excellent Undergraduate Research Project at Peking University
2022.12, National Scholarship (Undergraduate) (Top 3%)

📖 Educations

2023.09 - 2028.06 (Expectation), Ph. D. student, Department of Chemistry, Massachusetts Institute of Technology.
- Advisor: Prof. Heather J. Kulik.
2019.09 - 2023.07, Peking University.
- B. Sc. in Chemistry, College of Chemistry and Molecular Engineering.
- B. Sc. (double degree) in Intelligence Science and Technology, School of Electrical Engineering and Computer Science.
2016.09 - 2019.06, Dalian No.24 High School.

🏫 Teaching and Service

2025.02 - Now, MIT UROP supervisor of Michelle Luo, MIT SB AI and Chemistry, expected ’28, on atomistic property prediction for transition metal complex using 3D Graph Neural Network
2024.02 - 2024.05, Teaching Assistant for MITx, Massachusetts Institute of Technology.
2023.09 - 2023.12, Teaching Assistant for Thermodynamics I (5.601) and Thermodynamics II and Kinetics (5.602), Massachusetts Institute of Technology.
2021.03 - 2022.01, Teaching Assistant for Instrumental Analysis (Honor Class) (01034390) and Comprehensive Analytical Chemistry (Honor Class) (01034610), Peking University.

💻 Internships

2022.03 - 2023.08, research intern in small-molecule algorithms, DP Technology.