Weiliang Luo

Weiliang Luo

MIT Chemistry Kulik's Group

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.

Google Scholar citations: 135

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

  • Neural network potentials, enhanced sampling, and generative modeling for reactions in condensed phases. (Enerzyme)
  • Mechanistic studies, design and control of enzymatic and prebiotic reactions. (Enerzymette)
  • Agentic systems for GPU acceleration and method development in quantum chemistry. (Electrolites)

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

  • 2026.10: The first paper with my Enerzyme open-source initiative was accepted by JCTC!

📝 Publications

Main contributions

JCTC
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Enerzyme: An Efficient Reactive Neural Network Potential Training Framework for Enzyme Cluster Models with Application to Methyltransferases.

Weiliang Luo*, and Heather J. Kulik

Preprint at Arxiv | Code for Enerzyme | Code for Enerzymette | Citations: 0

  • The first release of Enerzyme, a reactive neural network potential design and training framework for enzyme cluster models.
  • The first release of Enerzymette, a reactive neural network potential harness and evaluation framework for enzyme mechanistic studies.
JCIM
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QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures

David W. Kastner, Weiliang Luo, Wilson Ho, Clorice R. Reinhardt, Allison Keys, and Heather J. Kulik

Preprint at Chemrxiv | Code | Citations: 4

  • Automatic quantum chemistry cluster construction for diverse enzyme systems.
Nat. Comput. Sci.
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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*

Preprint at arXiv | Code | Citations: 6

  • A solution to the ubiquitous valency imbalance in generative modeling of materials.
  • Efficient stability screening with foundational MLIP.
  • High-performance thermal-conductive and optical materials discovery with conditional generation.
ICMC 2025
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Music102: An D12-equivariant transformer for chord progression accompaniment

Weiliang Luo*

Code | Conference Panel | Citations: 2

  • A trial of a combination of symmetry and group representations in music theory and deep learning.
JACS Au
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(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 | Citations: 59

  • 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.

Other collaborations

🎖 Honors and Awards

  • 2025.10, ChemPhysChem Post Award at SMLQC 2025
  • 2025.07, Sigma Xi
  • 2025.05, 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.
  • 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 Mentorship

  • 2026.07 - Now, MIT UROP supervisor of Amelia England, Georgia Tech Chemistry, expected ’27, on adaptive QM/MM embedding for enzyme catalysis modeling
  • 2025.02 - 2025.05, 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.

📣 Leadership and Service

💻 Internships

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