Anubhav Jain

2026 National Award Finalist — Faculty

Anubhav Jain

Current Position:
Staff Scientist, Energy Storage and Distributed Resources

Institution:
Lawrence Berkeley National Laboratory

Discipline:
Theoretical Chemistry

Recognized for: Creating AI-driven computational platforms and software tools for autonomous laboratories that accelerate discovery of advanced materials for batteries, energy conversion, catalysis, and next-generation technologies. 

Areas of Research Interest and Expertise:
Computational Materials Science, Energy Materials, Agentic AI for Science

Previous Positions:

Scientific Advisor, Radical AI
Founder/Co-President, MaterialsQM Consulting
Staff Scientist, Lawrence Berkeley National Laboratory
Research Scientist/Chemist, Lawrence Berkeley National Laboratory
Postdoctoral Fellow, Lawrence Berkeley National Laboratory, USA (Advisors: Kristin A. Persson and David H. Bailey)
Ph.D., Massachusetts Institute of Technology, USA (Advisor: Gerbrand Ceder)

Research Summary:

Discovering new materials is essential for advancing clean energy, electronics, and manufacturing, but traditional trial-and-error approaches can be slow and costly. Anubhav Jain, PhD, develops computational platforms that use materials databases, artificial intelligence, automated calculations, and autonomous laboratories to predict and test promising materials more efficiently. His work has helped accelerate the discovery of materials for batteries, catalysts, energy conversion devices, and other advanced applications. By connecting simulation and machine learning to experimental tools, Jain’s research is transforming how scientists identify useful materials and move them toward real-world use, helping speed the development of next-generation technologies.

“From the Stone Age to the Silicon Age, the materials we can make and deploy have defined our technological possibilities. Today, we need better materials for higher-capacity and more cost-effective batteries, more efficient and resilient solar cells, and catalysts that can create useful fuels and chemicals. Much of my work has used supercomputers to simulate and screen hundreds of thousands of candidate compounds, feeding open databases like the Materials Project that researchers build on to design materials. My goal now is to develop AI agents that can reason about materials science, propose and optimize new materials, and run their own experiments to find new possibilities faster than ever before.”

Key Publications:

  1. V. Tshitoyan, J. Dagdelen, L. Weston, A. Dunn, Z. Rong, O. Kononova, K. A. Persson, G. Ceder, A. Jain. Unsupervised Word Embeddings Capture Latent Knowledge from Materials Science Literature. Nature, 2019.
  2. A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K. A. Persson. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Materials, 2013. 
  3. A. M. Ganose, J. Park, A. Faghaninia, R. Woods-Robinson, K. A. Persson, A. Jain. Efficient calculation of carrier scattering rates from first principles. Nature Communications, 2021. 
  4. L. Ward, A. Dunn, A. Faghaninia, N. E. R. Zimmermann, S. Bajaj, Q. Wang, J. Montoya, J. Chen, K. Bystrom, M. Dylla, K. Chard, M. Asta, K. A. Persson, G. J. Snyder, I. Foster, A. Jain. Matminer: An open source toolkit for materials data mining. Computational Materials Science, 2018. 

Other Honors:

2021–2025 Clarivate Highly Cited Researcher
2021 LBL Director’s Award, Early Scientific Career
2015 DOE Early Career Award
2014 NERSC Achievement Award: Innovative Use of High-Performance Computing
2011 Luis W. Alvarez Postdoctoral Fellowship
2006 DOE Computational Science Graduate Fellowship
2002 John McMullen Scholarship

In the Media:

Website