👋 About Me
Hi! I’m Dr. Bijun Tang, a Presidential Postdoctoral Fellow at the School of Materials Science and Engineering, Nanyang Technological University (NTU), Singapore, and a former Visiting Scientist at Rice University, USA.
My work sits at the exciting intersection of materials science and artificial intelligence (AI). I’m passionate about developing AI-driven methodologies and autonomous research platforms that accelerate the entire materials innovation cycle—from materials design and simulation to synthesis optimization and experimental validation.
My team works on building end-to-end AI systems for materials research. We combine machine learning, large language models (LLMs), scientific databases, knowledge extraction, inverse design algorithms, and autonomous experimentation to create intelligent platforms capable of discovering and developing advanced materials with minimal human intervention.
While our work spans a broad range of materials systems—including 0D nanoparticles, 1D nanowires, 2D materials, 3D alloys, and multifunctional composites—the central theme is the development of AI technologies that transform how materials are designed, synthesized, and understood.
I’ve published over 40 peer-reviewed articles in leading journals like Nature, Nature Materials, Nature Electronics, Nature Communications, Advanced Materials, and Materials Today. My work has received more than 2,700 citations (H-index: 25), and I have led or co-led multiple AI for Materials Science (AI4MS) initiatives with total research funding exceeding S$7 million.
🔬 Research Interests
My research focuses on developing next-generation AI technologies for scientific discovery and materials innovation. Current interests include:
🤖 AI for Materials Discovery
- Machine learning and foundation models for materials property prediction
- Inverse design and multi-objective optimization of advanced materials
- Uncertainty-aware and physics-informed AI models
📚 Scientific Knowledge Intelligence
- Automated extraction of scientific knowledge from literature
- Materials databases, knowledge graphs, and scientific reasoning systems
- Large language models (LLMs) for scientific discovery and hypothesis generation
⚙️ Autonomous Materials Research
- AI agents for materials design and synthesis planning
- Closed-loop AI–simulation–experiment frameworks
- Autonomous laboratories for accelerated materials development
🧪 Advanced Materials Systems
- Two-dimensional materials and heterostructures
- Quantum dots and nanomaterials
- High-entropy alloys and multifunctional composites
- Catalysts for sustainable energy applications
My long-term goal is to develop autonomous scientific systems capable of discovering, designing, and realizing novel materials at unprecedented speed and scale.
✨ Recent Highlights
🎤 Invited Talk at 2026 MRS Fall Meeting & Exhibit (2026)
I’ll be giving a presentation for the symposium: “AI-Driven Workflows and Autonomous Platforms for Functional Material Design and Catalysis” in Boston
Conference details →🎤 ICML 2026 AI4Science Oral Presentation (2026) Our paper “MATAI: A Unified Interactive Platform for AI-Driven Alloy Discovery” was accepted as an Oral presentation at the ICML 2026 AI4Science Workshop. Paper details →
📄 New Preprint: AutoDFT: Closed-Loop LLM Agents for Autonomous Density Functional Theory Calculations (2026)
An autonomous LLM-agent framework for closed-loop density functional theory (DFT) calculations and materials simulations published on arXiv (arXiv:2605.26179)
Paper details →📄 New Preprint: A Self-Evolving Agent for Explainable Diagnosis of DFT–Experiment Band-Gap Mismatch (2026)
A self-evolving AI agent for diagnosing and explaining DFT–experiment band-gap mismatches in materials research published on arXiv (arXiv.2604.26703)
Paper details →📄 New Publication: MatSeek: An Automated Knowledge-Driven Framework for Materials Research (2026)
An LLM-based framework that integrates literature-driven knowledge extraction with structured materials data to enable interpretable and efficient inverse alloy design, published at the AI4Mat Workshop at ICLR 2026
Paper details →🎤 Symposium Co-chair, AI for Materials Discovery Symposium (2026)
Co-chairing a major symposium on AI for Materials Discovery, NTU Singapore
Symposium details →📄 New Preprint: MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys (2025)
A generalist machine learning framework, integrating a curated alloy database, deep neural network-based property predictors, a constraint-aware optimization engine, and an iterative AI-experiment feedback loop, for alloys development published on arXiv: MATAI (arXiv:2511.10108)
Paper details →🎤 Invited Talk at 18th Pujiang Innovation Forum (2025)
Gave a keynote titled “Accelerating Materials Synthesis through AI: From Feasibility to Autonomy” at the AI for Materials Science Forum in Shanghai
Forum details →🥇 AI2050 Early Career Fellow, Schmidt Sciences (2024)
Only Asia-based recipient among 15 global fellows, first from Singapore
AI2050 Fellowship profile →🥇 Forbes 30 Under 30 Asia, Healthcare & Science (2023)
For pioneering the use of machine learning in materials discovery
Forbes profile →