I’m a third year PhD researcher in the Materials Intelligence Research (MIR) group, advised by Boris Kozinsky. My research interests lie at the intersection of atomistic materials modelling, electronic structure and artificial intelligence. Much of my PhD work has been on deep learning interatomic potentials. I am to build robust and scalable interatomic potential models, from their underlying deep learning architectures and training methods, to GPU performance engineering, and the software systems that enable efficient computation and automated workflows.
Prior to my PhD, I was a senior research assistant in the group of Yee Sin Ang at the Singapore University of Technology and Design, where I worked on computational materials screening for nanoelectronics applications.
I completed my BA in Physics and MPhil in Materials Science at the University of Cambridge. During my time at the Materials Theory Group, I developed methods for orbital-free density functional simulations under the supervision of Chuck Witt and Chris Pickard.
Deep Learning Interatomic Potentials
Machine learning interatomic potentials (MLIPs) based on deep equivariant neural networks achieve quantum-accurate results, but realizing their full potential requires careful attention to software design and computational efficiency. I led the redesign of the NequIP software framework for deep equivariant graph neural network potentials, focusing on robustness, extensibility, and performance. The rewrite achieved 5× speedups for training and 5–18× speedups for molecular dynamics simulations.
NequIP is a code for building E(3)-equivariant interatomic potentials
Allegro is a code for building highly scalable E(3)-equivariant interatomic potentials
- C.W. Tan , M.L. Descoteaux , M. Kotak , G. Miranda Nascimento , S.R. Kavanagh , L. Zichi , M. Wang , A. Saluja , Y.R. Hu , T. Smidt , others . High-performance training and inference for deep equivariant interatomic potentials. Digital Discovery 5, 1558–1567 (2026).
Building on the NequIP software infrastructure, I support and maintain a variety of extension packages that extend the capabilities of our MLIP architectures to new modeling capabilities.
Allegro-pol extends the Allegro architecture to model the electric response of materials
NequIP-NAC extends the NequIP architecture to perform nonadiabatic molecular dynamics.
- S. Falletta , A. Cepellotti , A. Johansson , C.W. Tan , M.L. Descoteaux , A. Musaelian , C.J. Owen , B. Kozinsky . Unified differentiable learning of electric response. Nature Communications 16, 4031 (2025).
- L.A. Gomes , S. Larmore , M. Wang , C.W. Tan , B. Kozinsky , S.A. Lopez . Machine-learned nonadiabatic couplings enable reactive photochemical reaction dynamics simulations. ChemRxiv (2026).
- G.d.M. Nascimento , M.L. Descoteaux , L. Zichi , C.W. Tan , W.C. Witt , N. Molinari , S. Mantha , D. Kitchaev , M. Kornbluth , K. Gadelrab , others . Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations. arXiv preprint arXiv:2604.26143 (2026).
Computational Materials Screening
Computational materials discovery often prioritizes device performance while overlooking materials-level sustainability. To address this, I developed a sustainability-guided materials screening protocol and applied it to ultrawide bandgap layered materials. This work identified 25 low-risk, sustainable ultrawide bandgap layered candidate semiconductors through in silico materials screening for nanoelectronic device applications, such as dielectric, power-electronics, and ultraviolet-photonics.
- C.W. Tan , L. Xu , C.C. Er , S.P. Chai , B. Kozinsky , H.Y. Yang , S.A. Yang , J. Lu , Y.S. Ang . Toward sustainable ultrawide bandgap van der Waals materials: An ab initio screening effort. Advanced Functional Materials, 2308679 (2023).
Orbital-Free Density Functional Theory
Orbital-free density functional theory (OFDFT) is a promising method for faster, more large-scale atomistic simulations. My prior work involves method development for OFDFT, including a differentiable OFDFT code, PROFESS-AD.
- C.W. Tan , C.J. Pickard , W.C. Witt . Automatic differentiation for orbital-free density functional theory. The Journal of Chemical Physics 158, 124801 (2023).
- W.C. Witt , B.W. Shires , C.W. Tan , W.J. Jankowski , C.J. Pickard . Random structure searching with orbital-free density functional theory. The Journal of Physical Chemistry A 125, 1650–1660 (2021).
