Pengfei Cai

I am a 4th year PhD candidate at MIT working in computational science and engineering, within the DMSE-CSE program. I work on generative flow-based models with physical constraints, differentiable physical simulations, and deep learning for accelerated discovery of materials (polymers, molecules). I'm advised by Rafael Gomez-Bombarelli.

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Research
Physics-Constrained Flow Matching: Sampling Generative Models with Hard Constraints
Utkarsh*, Pengfei Cai*, Alan Edelman, Rafael Gomez-Bombarelli, Christopher Vincent Rackauckas
NeurIPS, 2025
Paper  |  Code
Towards Long Rollout of Neural Operators with Local Attention and Flow Matching-inspired Correction: An Example in Frontal Polymerization PDEs
Pengfei Cai, Sulin Liu, Qibang Liu, Philippe Geubelle, Rafael Gomez-Bombarelli
NeurIPS 2024 Workshop on ML for Physical Sciences
  • Introduced functional flow matching for the refinement of predictions from neural operators to extend temporal roll-out.
  • Local attention-enhanced Fourier neural operators for improved long-term rollout of neural PDEs, especially for multiscale problems / reaction-diffusion PDEs with instabilities.
Learning Cure Kinetics of Frontal Polymerization PDEs using Differentiable Simulations
Pengfei Cai, Qibang Liu, Philippe Geubelle, Rafael Gomez-Bombarelli
ICML 2024 Workshop on AI for Science
NeurIPS 2024 Workshop on Data-driven and Differentiable Simulations, Surrogates, and Solvers
  • End-to-end learning of unknown physical terms in partial differential equations with differentiable PDE solvers (finite element or spectral methods). Here, by applying PDE-constrained optimization, we can learn cure kinetics terms in frontal polymerization processes.
  • Ongoing work: Learning closure terms in the PDE from multimodal experimental data (thermal capture videos and calorimetry curves).
Monte Carlo Simulation Monte Carlo Simulations of Thermoset Polymer Chain Growth and Crosslinking

Reverse Monte Carlo method to model the complex graph network of degradable crosslinked copolymers. We can now retrospectively determine network structure and reaction parameters by matching with experimental fragment spectra for the first time.
Self-Improving Photosensitizer Discovery System via Bayesian Search with First-Principle Simulations
Shidang Xu*, Jiali Li*, Pengfei Cai, Xiaoli Liu, Bin Liu, Xiaonan Wang
Journal of the American Chemical Society, 2021
  • Bayesian optimization-based active learning and graph neural networks to accelerate the discovery of photosensitizer molecules.
Accelerated Design of Near-Infrared-II Molecular Fluorophores via First-Principle Understanding and Machine Learning
Shidang Xu*, Pengfei Cai*, Jiali Li, Xianhe Zhang, Xianglong Liu, Xiaonan Wang, Bin Liu
ChemRxiv 2022 Preprint
  • Virtual screening of NIR-II fluorophores with machine learning. Experimental validation underway.

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