| Aug 19, 2026 | Presented recent work on machine-learned interatomic potentials for hafnia at ACS Fall 2026. |
| May 20, 2026 | New preprint: TEM-Scientist: Verified Experimental Protocols for Agentic Electron Microscopy — a compile–rehearse–execute architecture that moves the language model above the instrument control loop for safe, verifiable autonomous TEM operation. |
| Mar 15, 2026 | Our preprint AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation is now on arXiv (2603.20986). The framework introduces a multi-agent LLM system that automates MOOSE input generation, execution, and post-processing. |
| Feb 10, 2026 | Paper accepted in npj Computational Materials: Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning (Varughese et al.). |
| Dec 01, 2025 | Paper published in Materials Today: Solid Lubricant Mo₂TiC₂Tₓ MXene Coatings with Prolonged Macroscale Superlubricity (Sunkara et al.). |
| Apr 15, 2025 | Invited talk at the Department of Mechanical Engineering, Indian Institute of Science, Bangalore: AI/ML-Driven Multiscale Modeling for Materials and Devices. |
| Oct 01, 2024 | Our paper Machine Learning Simple Interpretable Short Range Potential for Silica (Koneru et al., Journal of Chemical Theory and Computation) was featured on the cover of the journal. |