My research interests are ML for materials science and chemistry, particularly materials synthesis, generative models, LLMs and RL. Beyond AI for science, I had the privilege of working as an AI Researcher at Meta and Google Research on diffusion models and transformers. I was also incredibly lucky to receive the MIT Presidential Fellowship for my PhD, and for my research to be featured on MIT News.

	Massachusetts Institute of Technology PhD Researcher | Cambridge, MA Sep 2021 – Present Advisor: Elsa A. Olivetti Collaborators: Rafael Gomez-Bombarelli, Yuriy Roman-Leshkov, Manuel Moliner, Jennifer Rupp MIT Presidential Fellowship Young NUS Fellow
	Meta AI Research Scientist | Sunnyvale, CA June 2025 – Aug 2025 Foundational AI and Codesign team (internship): Self-feedback for transformers
	Google Research Research Scientist | Mountain View, CA + Cambridge, MA June 2024 – Jan 2025 SciML team (internship): Hierarchical controllable diffusion models
	Ro5 ML Engineer | London, UK Apr 2021 – Jul 2021 GNNs + RL for molecular generation
	Institute of High Performance Computing Research Engineer | Singapore Jul 2020 – Jul 2021 Classical ML for catalysis
	Imperial College London Research Assistant | London, UK Mar 2020 – Mar 2021 Constrained RL for process optimization

1. [Featured on MIT News] Generative diffusion models for materials synthesis planning using molecular and crystalline materials datasets (Nature Computational Science (accepted, under proof), 2025 | NeurIPS (Oral Spotlight) AI for Materials, 2024 | Code) Elton Pan, Soonhyoung Kwon, Sulin Liu, Mingrou Xie, Alexander J Hoffman, Yifei Duan, Thorben Prein, Killian Sheriff, Yuriy Roman-Leshkov, Manuel Moliner, Rafael Gomez-Bombarelli, Elsa Olivetti

2. LLMs for materials synthesis planning for inorganic materials (ACS Applied Materials & Interfaces | NeurIPS (Oral Spotlight) AI for Materials, 2025 | Code) Thorben Prein, Elton Pan, Janik Jehkul, Steffen Weinmann, Elsa A Olivetti, Jennifer LM Rupp

3. LLM-enabled Bayesian optimization for molecular / protein optimization (NeurIPS (Spotlight) AI for Science, 2025 | Code) Mattias Akke, Soojung Yang, Jurgis Ruza, Jinyeop Song, Elton Pan, Rafael Gomez-Bombarelli

4. Transformer-based ranker for synthesis precursor recommendation for inorganic materials (Paper | Code in progress) Thorben Prein, Elton Pan, Sami Haddouti, Marco Lorenz, Janik Jehkul, Tymoteusz Wilk, Cansu Moran, Menelaos Panagiotis Fotiadis, Artur P Toshev, Elsa Olivetti, Jennifer LM Rupp

5. Synthesis condition prediction for inorganic materials (Chemistry of Materials, 2023 | Code) Christopher Karpovich, Elton Pan, Zach Jensen, Elsa Olivetti

6. Model explainability/interpretability (Aggregated SHAP) for materials synthesis (ACS Central Science, 2024 | Code) Elton Pan, Soonhyoung Kwon, Zach Jensen, Mingrou Xie, Rafael Gómez-Bombarelli, Manuel Moliner, Yuriy Román-Leshkov, Elsa Olivetti

7. Reinforcement learning (deep Q-learning, policy gradient) for inverse design of inorganic materials (NeurIPS AI for Materials, 2022 | npj Computational Materials, 2024 | Code) Elton Pan^*, Christopher Karpovich^*, Elsa Olivetti

8. Reaction Graph Networks for modeling precursor-target interactions to predict materials synthesis routes (NeurIPS AI for Materials, 2024 | Code in progress) Thorben Prein, Fuzhan Rahmanian, Kesava Prasad Arul, Jasmin El-Wafi, Menelaos Panagiotis Fotiadis, Jan Heimann, Paul Weinmann, Yifei Duan, Elton Pan, Elsa Olivetti, Jennifer LM Rupp

9. Materials representation learning (multi-task transformer pretraining) for inorganic materials property/synthesis prediction (NeurIPS AI for Materials, 2023 | Code in progress) Thorben Prein^*, Elton Pan^*, Tom Doerr, Elsa Olivetti, Jennifer Rupp

10. Constrained RL for process optimization (Computers & Chemical Engineering, 2021 | Code) Elton Pan, Panagiotis Petsagkourakis, Max Mowbray, Dongda Zhang, Ehecatl Antonio del Rio-Chanona

11. Bayesian optimization for chemistry/materials (Code for Acceleration Consortium Bayesian Optimization Hackathon) Elton Pan, Jurgis Ruza, Pengfei Cai