Sukriti Manna

I build autonomous AI workflows for materials discovery, design, and simulation — integrating quantum mechanics, atomistic modeling, and continuum-scale simulation with reinforcement learning, graph neural networks, and most recently multi-agent LLM systems.

My current research centers on agentic AI for scientific instruments and simulation: AI systems that autonomously specify, execute, verify, and analyze physics-based experiments and simulations. The flagship projects are AutoMOOSE — a multi-agent framework for phase-field simulation — and TEM-Scientist, a compile–rehearse–execute architecture for autonomous transmission electron microscopy. Both build on a longer arc of reinforcement-learning methods I have developed for atomistic potential construction (BLAST), and on data-driven infrastructure such as the Quantum Cluster Database — the largest open repository of atomically precise nanoclusters, with 70,000+ DFT-computed structures across 55 elements.

I apply these workflows to autonomous discovery of neuromorphic computing materials, thermal management for high-performance computing hardware, data-driven additive-manufacturing optimization, and next-generation energy and electronic materials. Selected outcomes have appeared in Science, Nature Communications, Science Advances, Advanced Materials, ACS Nano, and Nano Letters.

I am a Visiting Research Assistant Professor in the Department of Mechanical and Industrial Engineering at the University of Illinois Chicago, and a Resident Associate in the Theory and Modeling Group at Argonne National Laboratory’s Center for Nanoscale Materials, working with Prof. Subramanian Sankaranarayanan. I received my Ph.D. in Mechanical Engineering from Colorado School of Mines (2018) with Prof. Cristian V. Ciobanu and Prof. Vladan Stevanović, and held a postdoctoral position at Johns Hopkins University with Prof. Tim Mueller before joining Argonne in 2019.

Current preprints

AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation — S. Manna, H. Chan, S. K. R. S. Sankaranarayanan. arXiv, under review (2026).
Verified Experimental Protocols for Agentic Electron Microscopy (TEM-Scientist) — S. Manna, Y. Huang, R. Vasudevan, Y. Liu, S. K. R. S. Sankaranarayanan. arXiv, under review (2026).

selected publications

arXiv

AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation

Sukriti Manna, Henry Chan, and Subramanian K. R. S. Sankaranarayanan

2026

Under review

Bib

@misc{manna2026automoose,
  category = {preprint},
  title = {{AutoMOOSE}: An Agentic AI for Autonomous Phase-Field Simulation},
  author = {Manna, Sukriti and Chan, Henry and Sankaranarayanan, Subramanian K. R. S.},
  year = {2026},
  archiveprefix = {arXiv},
  url = {https://arxiv.org/abs/2603.20986},
  note = {Under review},
}

Sci. Data

A Database to Enable the Design and Discovery of Atomically Precise Nanoclusters

Sukriti Manna, Yunzhe Wang, Alberto Hernandez, and 3 more authors

Scientific Data, 2023

DOI Bib

@article{manna2023qcd,
  category = {journal},
  title = {A Database to Enable the Design and Discovery of Atomically Precise Nanoclusters},
  author = {Manna, Sukriti and Wang, Yunzhe and Hernandez, Alberto and Lile, Peter and Liu, Shuai and Mueller, Tim},
  journal = {Scientific Data},
  volume = {10},
  pages = {308},
  year = {2023},
  doi = {10.1038/s41597-023-02200-4},
}

Nat. Commun.

Learning in Continuous Action Space for Determination of High Dimensional Potential Energy Surfaces

Sukriti Manna, Troy D. Loeffler, Rohit Batra, and 10 more authors

Nature Communications(Featured in Nature Communications Collection on Machine Learning for Chemistry) , 2022

DOI Bib

@article{manna2022blast,
  category = {journal},
  title = {Learning in Continuous Action Space for Determination of High Dimensional Potential Energy Surfaces},
  author = {Manna, Sukriti and Loeffler, Troy D. and Batra, Rohit and Banik, Suvo and Chan, Henry and Varughese, Bilvin and Sasikumar, Kiran and Sternberg, Michael and Peterka, Tom and Cherukara, Mathew and Gray, Stephen and Sumpter, Bobby G. and Sankaranarayanan, Subramanian K. R. S.},
  journal = {Nature Communications},
  volume = {13},
  number = {1},
  pages = {368},
  year = {2022},
  doi = {10.1038/s41467-021-27849-6},
}

Science

Reconfigurable Perovskite Electronics for Artificial Intelligence

Hai-Tian Zhang, Tae Joon Park, ANM Nafiul Islam, and 17 more authors

Science, 2022

DOI Bib

@article{zhang2022perovskite,
  category = {journal},
  title = {Reconfigurable Perovskite Electronics for Artificial Intelligence},
  author = {Zhang, Hai-Tian and Park, Tae Joon and Islam, ANM Nafiul and Tran, Dat S. J. and Manna, Sukriti and Wang, Qi and Mondal, Sukirti and Yu, Heshan and Banik, Suvo and Cheng, Shaobo and Zhou, Hua and Gamage, Sampath and Mahapatra, Soumyabrata and Zhu, Yimei and Abate, Yohannes and Jiang, Nan and Sankaranarayanan, Subramanian K. R. S. and Sengupta, Abhronil and Teuscher, Christof and Ramanathan, Shriram},
  journal = {Science},
  volume = {375},
  number = {6580},
  pages = {533--539},
  year = {2022},
  doi = {10.1126/science.abn6196},
}

Phys. Rev. Appl.

Enhanced Piezoelectric Response of AlN via CrN Alloying

Sukriti Manna, Keith R. Talley, Prashun Gorai, and 5 more authors

Physical Review Applied, 2018

DOI Bib

@article{manna2018alncrn,
  category = {journal},
  title = {Enhanced Piezoelectric Response of {AlN} via {CrN} Alloying},
  author = {Manna, Sukriti and Talley, Keith R. and Gorai, Prashun and Mangum, John and Zakutayev, Andriy and Brennecka, Geoff L. and Stevanovi{\'c}, Vladan and Ciobanu, Cristian V.},
  journal = {Physical Review Applied},
  volume = {9},
  number = {3},
  pages = {034026--034036},
  year = {2018},
  doi = {10.1103/PhysRevApplied.9.034026},
}