Thank you for your interest in the AI4PhysSci Lab! We welcome questions, collaborations, and inquiries from researchers and students who share our passion for AI4Science and quantum chemistry.

We look forward to hearing from you and exploring ways to collaborate or share ideas!

As an interdisciplinary research group working at the intersection of AI and the physical sciences, we are actively seeking talented and motivated researchers from science-related fields (chemistry, physics, biology, materials science, etc.) as well as AI-focused disciplines (computer science, mathematics, statistics, etc.). Interested candidates are encouraged to reach out to Dr. Cheng directly via email or through any of our group social media channels (X, BlueSky, etc.).

⌛ Current openings:

We don't have openings for the 2026 Fall, 2027 Spring, or 2027 Fall PhD admission cycles. However, we welcome inquiries from prospective students interested in future cycles (2028 Spring and beyond). If you are highly motivated to apply for one of the closed intakes, please include a personal statement describing the specific directions you hope to pursue, why you are committed to them, and how your background fits those directions. Exceptions may be considered based on the contents of the letter of intention.

Please feel free to reach out to Dr. Cheng to discuss potential research opportunities and to learn more about our group's work.

🚀 Long-term openings:

As part of our commitment to fostering better education in the AI era, we warmly welcome younger generations to join our group. High school and undergraduate students are especially encouraged to participate in our AI for Science research initiatives.

🌱 A supportive and collaborative environment: We will work together as partners and colleagues, not just as students and supervisors.

💡 Cultivate your own scientific style and taste: Develop your own approach to AI for Science and scientific problem solving.

🧪 Develop critical scientific insights: Develop your own approach to AI for Science and scientific problem solving.

🛠️ Build a versatile AI-era skill set: Learn to bridge theory and experiment with ML and data science via industry-standard coding practices.

🔬 Tailored growth for diverse goals: Sherry returned from industry (MSR & Tencent) and cares about your career goals. We encourage students to pursue careers in either academia or industry. For different career paths, we provide research opportunities that help you the most and actively support participation in industry internships.

🌍 Opportunities to connect and collaborate: Collaborate with computer scientists, physicists, chemists, and materials scientists from both academia (e.g., Caltech, Westlake U, HKUST, NUS, etc.) and industry.

Before applying, we suggest candidates consider the following courses and related topics, which will be extremely helpful for understanding the core research objectives of the AI for Physical Sciences Lab. Please also consider checking the Mini Tests and submitting multiple test results to show your enthusiasm for AI4S.

1. CS/Math: Linear Algebra, Probability and Statistics, Data Structures, Introduction to Machine Learning/AI, Mathematical Modeling, Stochastic Processes and Markov Chains, Linear Programming, Convex Optimization, Statistical Learning Theory, Information Theory, Functional Analysis, Numerical Analysis/Linear Algebra/PDE, Stochastic DE, Differentiable Manifolds, Lie Algebra, Computational Complexity, Topics in ML (Gaussian Processes, Graph Neural Networks, Reinforcement Learning, Generative Models, Language Models). Abstract Algebra, Topology, and Computational Graphics are not required but are a plus.
2. Physics/Chemistry/Materials: Quantum Mechanics, Statistical Mechanics, Atomic Physics, Electronic Structure Theory, Computational Physics, Group Representation Theory (note: not just group theory; chemistry students may encounter this in inorganic or structural chemistry courses), Solid-State Physics, Quantum Field Theory, Polymer Chemistry/Physics, Introduction to Quantum Computing.
3. Other foundational skills: GitHub (open-sourcing spirit), Python (NumPy, SciPy, Scikit-learn, Pandas, etc.), PyTorch, JAX. Proficiency in using the Linux operating system, command line, Slurm, Docker/Conda for environment setup, and the ability to collaborate effectively with AI agents (e.g., vibe coding, automation pipeline for your Gaussian calculations, auto-matching paper formulas with code, etc.). C++ and Julia are not required but a plus. Java is not required and not a plus.