About
Research Engineer at Meta, FAIR Chemistry team
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Previously at Google X, PhD @ Columbia, UGrad @ UC Berkeley
I am a Research Engineer at Meta, Fundamental AI Research [FAIR] Chemistry team (previously known as the Open Catalyst Project), working on new materials and molecular design and discovery with machine learning and computational simulations.
Previously, I obtained my PhD in 2024 with Urban group at Columbia University, working on accelerating high-temperature thermodynamics with ML, materials representation learning and discovery through generative models and DFT/MD simulations.
I was also previously at Google X (the moonshot factory) as an AI Resident working on generative models for molecular and materials discovery and computer vision on the circularity moonshot.
I received my BS from the University of California, Berkeley in 2019, where I performed undergraduate research with Asta group, working on materials defect thermodynamics with quantum simulations.
Besides work, you can usually find me playing chess, traveling, learning my fourth language (Can you guess the three languages that I know?) and spending time with people I love.
Resume
Below is a preview of my resume; access my full CV here. [updated July 2025]
Education
Columbia University
Ph.D. Materials Science and Engineering
Fu Foundation School of Engineering and Applied Science
Department of Applied Physics and Applied Mathematics
2019 - 2024 | New York, NY
- Research in Alexander Urban's group on high-temperature thermodynamics and ML and quantum-guided inverse design of materials with target properties.
- Coursework in Machine Learning, Computational Mathematics, Numerical Methods, Group Theory, Atomistic Simulations, DFT, Phonons.
University of California, Berkeley
B.S. Materials Science and Engineering
B.S. Chemical Engineering
Minor in Electrical Engineering and Computer Sciences
2015 - 2019 | Berkeley, CA
- Undergraduate research in Mark Asta's group on thermodynamics of charged defects with DFT.
- Coursework in Data Science, Circuit Design, Optics, Control Theory, Materials Science, Chemical Engineering.
Selected Papers
Wood, B.M., Dzamba, M., Fu, X., Gao, M., Shuaibi, M., Barroso‑Luque, L., Abdelmaqsoud, K., Gharakhanyan, V., Kitchin, J.R., Levine, D.S., Michel, K., Sriram, A., Cohen, T., Das, A., Rizvi, A., Sahoo, S.J., Ulissi, Z.W., and Zitnick, C.L. (2025). UMA: A Family of Universal Models for Atoms. arXiv preprint arXiv:2506.23971 [in press ‑ 1, 2, and 3]
Levine, D.S., Shuaibi, M., Spotte‑Smith, E.W.C., Taylor, M.G., Hasyim, M.R., Michel, K., Batatia, I., Csányi, G., Dzamba, M., Eastman, P., Frey, N.C., Fu, X., Gharakhanyan, V., Krishnapriyan, A.S., Rackers, J.A., Raja, S., Rizvi, A., Rosen, A.S., Ulissi, Z., Vargas, S., Zitnick, C.L., Blau, S.M., and Wood, B.M. (2025). The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models. arXiv preprint arXiv:2505.08762 [in press ‑ 1, 2, and 3]
Zhou, J., Huang, Y., Boromand, A., Noori, K, Purvis, L., Oh, C., Lu, L., Ulissi, Z.W., Gharakhanyan, V.*, and Zhang, X.* [* joint last author] (2025). Genetic algorithm‑accelerated computational discovery of liquid crystal polymers with enhanced optical properties. arXiv preprint arXiv:2505.13477
Joshi, C.K., Fu, X., Liao, Y.L., Gharakhanyan, V., Miller, B.K., Sriram, A., and Ulissi, Z.W. (2025) All‑atom dif‑ fusion transformers: Unified generative modelling of molecules and materials. [Spotlight at AI for Materials (AI4Mat) workshop at The 13th International Conference on Learning Representations (ICLR 2024) and accepted to The 42nd International Conference on Machine Learning (ICML 2025)] openreview.net
Abed, J., Kim, J., Shuaibi. M., Wander, B., Duijf, B., Mahesh, S., Lee, H., Gharakhanyan, V., Hoogland, S., Irtem, E., Lan, J., Schouten, N., Vijayakumar, A.U., Hattrick‑Simpers, J., Kitchin, J. R., Ulissi, Z.W., van Vugt, A., Sargent, E.H., Sinton, D., and Zitnick, C.L. (2024). Open Catalyst Experiments 2024 (OCx24): Bridging experiments and computational models. arXiv preprint arXiv:2411.11783 [in press ‑ 1, 2, and 3]
Gharakhanyan, V. (2024). Advancing computational high‑temperature materials thermodynamics with machine learning. Doctoral dissertation, Columbia University. doi.org/10.7916/1qnv-6142
Gharakhanyan, V., Wirth, L., Garrido Torres, J. A., Eisenberg, E., Wang, T., Trinkle, D. R., Chatterjee, S., and Urban, A. (2024). Discovering melting temperature prediction models of inorganic solids by combining supervised andunsupervised learning. The Journal of Chemical Physics, 160(20). doi.org/10.1063/5.0207033
Gharakhanyan, V., Aalto, M. S., Alsoulah, A., Artrith N., and Urban, A. (2023). Constructing and compressing global moment descriptors from local atomic environments. (2023). Machine Learning for Materials (ML4Materials) workshop at The 11th International Conference on Learning Representations (ICLR 2023).. openreview.net
Gadhiya, T., Shah, F., Vyas, N., Gharakhanyan, V., Yang, J. H., and Holiday, A. (2022). Directional Variational Transformers for continuous molecular embedding. Machine Learning for Molecules (ML4Molecules) workshop at ELLIS 2022. ml.jku.at
Garrido Torres, J. A., Gharakhanyan, V., Artrith, N., Eegholm, T. H., and Urban, A. (2021). Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures. Nature communications, 12(1). doi.org/10.1038/s41467-021-27154-2 [in press]
Honors and Awards
GDS IMPACT Award for Excellence in Graduate Research
FIP Distinguished Student Award
DMP Ovshinsky Student Prize
GERA Energy Workshop Award
American Physical Society [Mar 2023]
NSF Conference Fellowship
Mechanistic ML and Digital Twins (MMLDT-CSET) 2021 Conference [Jul 2021]
Data Science/Medical Research Program Fellowship
TechFoundation & Harvard Medical School [Jul 2020]
President’s Special Award - Paper on Mathematical Modeling of Viruses
President of Armenia - Armen Sarkissian [Jun 2020] [in press]
2nd place - Design Competition: The Energy Transition Challenge
Chevron Corporation & UC Berkeley [May 2018]
President’s Annual Award for the Best Student in Information Technology
Synopsys & President of Armenia - Serzh Sargsyan [Oct 2013] [in press]
Two Bronze medals - 44th and 45th International Chemistry Olympiads
Washington D.C., USA [2012 U.S. Senate resolution 491] and Moscow, Russia [Jul 2012 and Jul 2013]
Industry Experience
Meta Platforms, Inc.
Research Engineer @ FAIR Chemistry
Nov 2023 - Present | San Francisco, CA
- Materials and molecular design and discovery with machine learning and computational chemistry tools.
- Areas include display and optical materials, energy storage materials, catalysis, direct air capture, etc.
Google X (the moonshot factory)
AI Resident
May 2021 - Dec 2021 | Mountain View, CA
June 2022 - April 2023 | Mountain View, CA
- X is Alphabet’s moonshot factory. I was part of a circularity moonshot working in the area of ML for materials recycling.
- Co‑developed 6 patents and 1 workshop paper (see below) for the applications related to generative models for materials and molecular discovery, and computer vision.
The Quant Edge
Quantitative Research Intern
Aug 2020 - Feb 2021 | New York, NY
- ML for sports betting: Monte Carlo simulations and statistical methods for soccer match prediction, and a ranking algorithm for horse racing predictions.
Patents
Gharakhanyan, V., Yang, J. H., Gadhiya, T., and Holiday, A. (X Development LLC, 2023). Search for candidate molecules using quantum or thermodynamical simulations and autoencoder. U.S. Patent 12,291,608, issued May 6, 2025.
Holiday, A., Gharakhanyan, V., Gadhiya, T., Vyas, N., and Shah, F. (X Development LLC, 2023). Machine learning platform for finding solid catalysts for depolymerization reactions. U.S. Patent Application 18/435,957, Aug 8, 2024.
Yang, J. H., Gharakhanyan, V., Gadhiya, T., and Holiday, A. (X Development LLC, 2023). Ionic liquid-based depolymerization optimization. U.S. Patent Application 17/967,711, June 1, 2023.
Gadhiya, T., Shah, F., Vyas, N., Yang, J. H., Gharakhanyan, V., and Holiday, A. (X Development LLC, 2023). Molecular structure transformers for property prediction. U.S. Patent Application 17/967,685, June 1, 2023.
Gadhiya, T., Shah, F., Vyas, N., Yang, J. H., Gharakhanyan, V., and Holiday, A. (X Development LLC, 2023). Molecular structure transformers for property prediction. U.S. Patent Application 17/967,723, June 1, 2023.
Holiday, A., Gharakhanyan, V., Gadhiya, T., Vyas, N., and Shah, F. (X Development LLC, 2023). Machine learning platform for generating solid catalysts for depolymerization reactions. U.S. Provisional Patent Application 63/509,220, filed June 20, 2023.
Gharakhanyan, V., Purvis, L., Zhang, X., Boromand, A., Oh, C., Ulissi, Z.W., and Noori, K. (Meta Platforms, Inc., 2025). Genetic algorithm‑accelerated computational discovery of liquid crystal polymers. U.S. Provisional Patent Application 63/814,363, filed May 29, 2025.
Teaching Experience
Teaching at Columbia University
- TA - Analysis of Engineering Problems course (CHEN 3020) [Sp2023]
- TA - Atomistic Simulations course (CHEN 4880) [Sp2021]
- TA - Computational Math: Numerical Methods course (APMA 4300) [Fa2019-Sp2020]
- Instructor - Academic Success Program - Introduction to Statistics course [Su2020, Su2021]
Teaching at UC Berkeley
- TA - Quantum Mechanics course (Chem 120A) [Fa2018-Sp2019]
- TA - General Chemistry course (Chem 1A) [Su2018]
- Head Tutor - College of Chemistry [Fa2017-Sp2019]
Teaching elsewhere
- Instructor - Workshop on Chemical Process Control and Dynamics at Tumo Center for Creative Technologies in Yerevan, Armenia [Wi2018]