Overview
Our lab is an AI for Chemistry research group centered at theoretical & computational chemistry, especially wavefunction learning and electronic structure theory modelling. We focus on building practical, reliable, and user-friendly tools using AI, quantum computing, and physics-driven approaches. Our goal is to create a general and efficient framework that bridges data-driven models with fundamental electronic structure theories, providing accurate and scalable solutions for chemical problems. We actively work on physics-driven multi-scale modeling strategies via highly accurate simulators, emulators, predictors and evaluators/agents to achieve a collaborative efforts of AI, computational chemistry and experimentalists.
🔬✨ Research Interest Summary
Focus on Challenges in AI4S
⚙️🔗 Our Approach: Multiscale + Physics driven modeling in AI4Chemistry
🧩 Our research pipeline follows a modular, LEGO-like design philosophy: Each module functions as an interchangeable building block🧱, allowing us to assemble tailored workflows for a wide range of tasks.
Goal: Toward a fast, accurate and general foundation model for both ground & excite state chemistry
1️⃣ Simulator 🧪: Generates a small but extremely high-quality dataset of wavefunctions using Deep QMC, AI boost quantum computing and other advanced quantum simulation methods.
2️⃣ Emulator ⚙️: Uses orbital-representation-based graph neural networks/transformers to learn the Simulator’s outputs and expand data volume efficiently.
3️⃣ Predictor 🔮: Predict molecular properties directly from structures (molecular graph, SMILES, etc), enabling fast, low-cost end-to-end applications.
4️⃣ Evaluator 📝 & Agent 🤖: Fine-tune large scientific language models as AI agent + Bayesian optimization to integrate literature data, and guide real-world experiments.
Diverse Directions
🚀🌟 Current Active Projects:
1️⃣ AI for Quantum Chemistry
• Orbital-based machine learning for highly accurate ground and excited state wavefunction theory emulation (Emulator): Selected papers 1 2
• Ab initio wavefunction learning via Deep Quantum Monte Carlo (Simulator): Selected papers 1
2️⃣ AI x Quantum Computing x Quantum Chemistry
• Quantum computing for quantum chemistry (Simulator): Selected papers 1 2
• Machine Learning for variational quantum algorithms (Simulator): Selected papers 1
3️⃣ Machine learning potential energy surface (MLPES) & force field (MLFF) (Predictor, Not a priority now): Selected papers 1 2
4️⃣ AI for Experiment Design
• Bayesian optimization for experimental design (Evaluator): Selected papers 1
5️⃣ Benchmark of LLM & Scientific Dataset Construction (Agent): Selected papers 1