Bio

Ruchika Mahajan is a Postdoctoral Research Fellow at the SUNCAT Center for Interface Science and Catalysis, Department of Chemical Engineering, Stanford University and SLAC National Accelerator Laboratory. Her research combines first-principles simulations, microkinetic modeling, and machine learning to understand and design functional materials, from strongly correlated transition-metal oxides to electrocatalysts for CO2 reduction.
She received her Ph.D. and M.Sc. (Integrated Ph.D.) from the Indian Institute of Technology (IIT) Mandi, India, in 2022, where she studied MnO2 polymorphs using Hubbard-corrected density functional theory (DFT+U, DFT+U+V) and computed magnetic exchange interactions with automated AiiDA workflows. Her work on intersite Hubbard interactions has been published in Physical Review Materials the Journal of Physical Chemistry C, and Physical Review B.
At SUNCAT, she develops open-source software and AI-ready data infrastructure for computational and experimental catalysis. She is the lead developer of the Experimental Catalysis Hub (CatHub), AutoCatLab (high-throughput DFT and COHP bonding analysis), and AutoCatKinetics (automated microkinetic modeling), and she builds agentic LLM tools that run computational catalysis workflows. She also combines COHP-derived electronic descriptors with graph neural networks to predict catalyst stability and activity.

Honors & Awards

  • State Merit Award (Mukhya Mantri Protsahan Yojana) Cash incentive of INR 75,000 awarded, Government of Himachal Pradesh, India (August 2015)
  • Selected Participant, Yambo School on Electronic Excitations in Novel Materials (fully funded), International Centre for Theoretical Physics (ICTP), Trieste, Italy (January 2020)
  • Student ModCon Program Participant (competitively selected, fully funded, Trillion Parameter Consortium (TPC26 Conference and Hackathon), Baltimore, MD, USA (May 2026)

Professional Education

  • Doctor of Philosophy (Ph.D.), Indian Institute of Technology (IIT) Mandi, India, Physics (2022)
  • Master of Science (M.Sc.), Indian Institute of Technology (IIT) Mandi, India, Physics (2017)
  • Bachelor of Science (B.Sc.), Govt. College Dharamshala, Himachal Pradesh University, India, Physics Hon's (2015)

Stanford Advisors

Contact

  • Academic
    mahajann@stanford.edu
    University - Scholar Department: SLAC National Accelerator Laboratory Position: Postdoctoral Scholar

Additional Info

Links

All Publications

  • Data as the Backbone of Artificial Intelligence: Insights from Heterogeneous Catalysis ACS CATALYSIS Shin, D., Mahajan, R., Lian, Z., Heinlein, J., Cargnello, M., Tassone, C. J., Winther, K. T. 2026

    View details for DOI 10.1021/acscatal.6c03456

    View details for Web of Science ID 001845329400001

  • Comparative study of magnetic exchange parameters and magnon dispersions in NiO and MnO from first principles PHYSICAL REVIEW B Dos Santos, F., Binci, L., Menichetti, G., Mahajan, R., Marzari, N., Timrov, I. 2026; 113 (2)

    View details for DOI 10.1103/gtxm-6vtg

    View details for Web of Science ID 001686742200004

  • A research database for experimental electrocatalysis: Advancing data sharing and reusability. The Journal of chemical physics Mahajan, R., Aleman, A. M., Crago, C. F., Bhasker-Ranganath, S., Kreider, M. E., Zamora Zeledon, J. A., Schröder, J., Kamat, G. A., Hubert, M. A., Nielander, A. C., Jaramillo, T. F., Stevens, M. B., Voss, J., Winther, K. T. 2025; 163 (12)

    Abstract

    The availability of high-fidelity catalysis data is essential for training machine learning models to advance catalyst discovery. Furthermore, the sharing of data is crucial to ensure the comparability of scientific results. In electrocatalysis, where complex experimental conditions and measurement uncertainties pose unique challenges, structured data collection and sharing are critical to improving reproducibility and enabling robust model development. Addressing these challenges requires standardized approaches to data collection, metadata inclusion, and accessibility. To support this effort, we have developed an extensive data infrastructure that curates and organizes multimodal data from electrocatalysis experiments, making them openly available through the catalysis-hub.org platform. Our datasets, comprising 241 experimental entries, provide detailed information on reaction conditions, material properties, and performance metrics, ensuring transparency and interoperability. By structuring electrocatalysis data in web-based as well as machine-readable formats, we aim to bridge the gap between experimental and computational research, allowing for improved benchmarking and predictive modeling. This work highlights the importance of well-structured, accessible data in overcoming reproducibility challenges and advancing machine learning applications in catalysis. The framework we present lays the foundation for future data-driven research in electrocatalysis and offers a scalable model for other experimental disciplines.

    View details for DOI 10.1063/5.0280821

    View details for PubMedID 41025581

  • Pivotal Role of Intersite Hubbard Interactions in Fe-Doped α-MnO<sub>2</sub> JOURNAL OF PHYSICAL CHEMISTRY C Mahajan, R., Kashyap, A., Timrov, I. 2022; 126 (30): 14353-14365

    View details for DOI 10.1021/acs.jpcc.2c04767

    View details for Web of Science ID 000848273000001

  • Importance of intersite Hubbard interactions in β-MnO<sub>2</sub>: A first-principles DFT plus <i>U</i> plus <i>V</i> study PHYSICAL REVIEW MATERIALS Mahajan, R., Timrov, I., Marzari, N., Kashyap, A. 2021; 5 (10)

    View details for DOI 10.1103/PhysRevMaterials.5.104402

    View details for Web of Science ID 000705598400002

https://orcid.org/0000-0001-9333-3717