Scholar
Bowen Deng
Google Scholar ID: PRPXA0QAAAAJ
Postdoc at MIT | PhD at UC Berkeley
Citations & Impact
Citations
1,233
H-index
12
i10-index
12
Publications
20
Co-authors
10
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Spin-informed universal graph neural networks for simulating magnetic ordering, W. Xu, R. Y. Sanspeur*, A. Kolluru, B. Deng, P. Harrington, S. Farrell, K. Reuter, J. R. Kitchin* (2025), Proc. Natl. Acad. Sci.
- Modeling phase transformations in Mn-rich disordered rocksalt cathodes with charge-informed machine-learning interatomic potentials, P. Zhong*, B. Deng, S. Anand, T. Mishra, G. Ceder* (2025), arXiv
- A framework to evaluate machine learning crystal stability predictions, J. Riebesell*, R. E. A. Goodall, P. Benner, Y. Chiang, B. Deng, G. Ceder, M. Asta, A. A. Lee, A. Jain, K. A. Persson* (2025), Nat. Mach. Intell. 7, 836–847
- DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials, K. Han, B. Deng*, A. B. Farimani, G. Ceder (2025), arXiv
- Crystal structure prediction with host-guided inpainting generation and foundation potentials, P. Zhong*, X. Dai, B. Deng, G. Ceder, K. A. Persson* (2025), Mater. Horiz.
- Cross-functional transferability in universal machine learning interatomic potentials, X. Huang, B. Deng*, P. Zhong, A. D. Kaplan, K. A. Persson, G. Ceder* (2025), arXiv
- A Foundational Potential Energy Surface Dataset for Materials, A. D. Kaplan, R. Liu, J. Qi, T. W. Ko, B. Deng, J. Riebesell, G. Ceder, K. A. Persson, S. P. Ong* (2025), arXiv
- Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry, T. W. Ko, B. Deng, M. Nassar, L. Barroso-Luque, R. Liu, J. Qi, E. Liu, G. Ceder, S. Miret, S. P. Ong* (2025), arXiv
- A practical guide to machine learning interatomic potentials – Status and future, R. Jacobs*, D. Morgan*, S. Attarian, J. Meng, C. Shen, Z. Wu, C. Y. Xie, J. H. Yang, N. Artrith, B. Blaiszik, G. Ceder, K. Choudhary, G. Csanyi, E. D. Cubuk, B. Deng, R. Drautz, X. Fu, J. Godwin, V. Honavar, O. Isayev, A. Johansson, S. Martiniani, S. P. Ong, I. Poltavsky, K. Schmidt, S. Takamoto, A. P. Thompson, J. Westermayr, B. M. Wood, B. Kozinsky, (2025), Curr. Opin. Solid State Mater. Sci. 35, 101214
- Oxygen Dimerization-Driven Cation Migration Induces Voltage Hysteresis in Disordered Rocksalt Cathodes, B. Kim, P. Zhong, Y. Choi, S. Anand, H.-M. Hau, B. Deng, G. Ceder* (2025), J. Am. Chem. Soc. 147, 223–233
- Systematic softening in universal machine learning interatomic potentials, B. Deng, Y. Choi, P. Zhong, J. Riebesell, S. Anand, Z. Li, K. Jun, K. A. Persson, G. Ceder* (2025), npj Comput. Mater. 11, 9
- Effect of Cation Disorder on Lithium Transport in Halide Superionic Conductors, P. Zhong, S. Gupta, B. Deng, K. Jun, G. Ceder* (2024), ACS Energy Lett. 9, 2775–2781
- Inpainting crystal structure generations with score-based denoising, X. Dai‡, P. Zhong‡, B. Deng, Y. Chen, G. Ceder* (2024), ICML 2024 AI for Science Workshop
Research Experience
- Learning Matter Group at MIT: 09/2025-Current, Cambridge, MA, AI for materials
- CEDER Group at Lawrence Berkeley National Lab: 02/2021-08/2025, Berkeley, CA, Developing and benchmarking of machine learning interatomic potentials (MLIPs), Deep learning, first-principles simulation, thermodynamics, and statistical mechanics, Modeling of Li-ion battery cathodes and solid-state Li-ion conductors
- Google DeepMind, Science Team: 05/2024-11/2024, Mountain View, CA, Materials AI and thermodynamics
- Microsoft Research Asia: 09/2020 – 12/2020, Beijing, China, Materials deep learning with graph neural networks
- Transport for Energy Application Lab at UCSB: 01/2019 – 02/2020, Santa Barbara, CA, First-principles study of topological Dirac semimetals for thermal-electric applications
Background
- Postdoc researcher at MIT, working at the intersection of materials science and artificial intelligence, especially on designing reliable AI for material discoveries at scales.
