5 papers
Agent-E2MD: Autonomous Translation of Interatomic Potential Equations into Physically Validated Pair Styles for Molecular Dynamics in LAMMPS
Bilvin Varughese, Orcun Yildiz, Aditya Koneru +3
Interatomic potentials underpin MD and govern predictive atomistic-model fidelity for metals, semiconductors, oxides, liquids, and reactive systems. A potential has limited practic…
Symbolic Ensemble Learning Enables Discovery of Fast Accurate Physics-Based Interatomic Potentials
Bilvin Varughese, Aditya Koneru, Adil Muhammad +6
Machine learning has transformed materials simulation by delivering force fields with ab initio accuracy, yet bridging the gap between high-dimensional regression and physical inte…
AutoMOOSE: Use Case and Logical Views of Agentic Phase-Field Simulation Software
Sukriti Manna, Henry Chan, Subramanian Sankaranarayanan
AutoMOOSE is an agentic software framework that converts a natural-language request into an executed, screened, and interpreted MOOSE phase-field simulation. Here, we deploy AutoMO…
AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation
Sukriti Manna, Henry Chan, Subramanian K. R. S. Sankaranarayanan
Phase-field modeling links thermodynamics and kinetics to microstructural evolution, but multiphysics frameworks such as MOOSE require expertise to construct inputs, manage campaig…
Physics-Informed Tree Search for High-Dimensional Computational Design
Suvo Banik, Troy D. Loeffler, Henry Chan +4
High-dimensional design spaces underpin a wide range of physics-based modeling and computational design tasks in science and engineering. These problems are commonly formulated as…