arXiv subjects

Bilvin Varughese

Publications and source records attributed to Bilvin Varughese.

3 recordsLinked to original sources

arXiv2026

Agent-E2MD: Autonomous Translation of Interatomic Potential Equations into Physically Validated Pair Styles for Molecular Dynamics in LAMMPS

Interatomic potentials underpin MD and govern predictive atomistic-model fidelity for metals, semiconductors, oxides, liquids, and reactive systems. A potential has limited practical value until reliably implemented in production MD code. Slow, expertise-intensive implementation requires more than equation-to-C++ translation: it must select the neighbor-list architecture, evaluate and distribute many-body derivatives, manage interprocessor communication, and preserve host-code energy, force, and virial conventions. We introduce Agent-E2MD, a knowledge-guided agentic workflow that translates user-specified interatomic models into executable LAMMPS pair styles. It combines architectural classification, code generation, autonomous build-test-fix cycles, simulation execution, and hierarchical physical validation. We test Agent-E2MD on seven material-potential pairs: Lennard--Jones Ar, EAM/FS Ag, MEAM Bi, Tersoff Si, GAP Ni, ReaxFF C/H/N/O, and a recently developed Symbolic Regression EAM (Symb EAM) model for Al, currently unavailable in LAMMPS. Single-point results match reference energies and forces for all seven models. Five crystalline many-body benchmarks recover reference relaxed lattice properties, vacancy formation energies, and elastic constants. Ag, Bi, Si, and Ni are stable at finite temperature in nanosecond-scale simulations; ReaxFF extends validation to reactive dynamics with evolving bond order and charge equilibration. Results demonstrate that the software architecture for an interatomic potential can be inferred from its physical and mathematical structure. Agent-E2MD does not replace scientific judgment; it shifts users' effort from routine implementation to model definition, rigorous validation, and physical interpretation. The framework provides a practical, traceable bridge between emerging AI-driven potential discovery methods and production-scale atomistic simulations.

cond-mat.mtrl-sci↗

arXiv2026

Symbolic Ensemble Learning Enables Discovery of Fast Accurate Physics-Based Interatomic Potentials

Machine learning has transformed materials simulation by delivering force fields with ab initio accuracy, yet bridging the gap between high-dimensional regression and physical interpretability remains a grand challenge. Conventional analytical potentials offer transparency but often fail to capture the complexity of far-from-ground state regimes. Here, we introduce a hybrid symbolic-neural framework that unifies the interpretability of the Embedded Atom Method (EAM) with the adaptability of data-driven learning. Using Equation Learner Neural Networks (EqNNs) trained on density functional theory (DFT) data, we obtain interpretable models for aluminum through three distinct training protocols: random initialization trained via Monte Carlo Tree Search (MCTS) and gradient descent, and two transfer-learning strategies initialized from a copper potential - one employing MCTS followed by gradient descent, and the other using gradient descent only. We find that while all three resulting symbolic models achieve sub-10 meV/atom accuracy, they occupy distinct local minima in the functional landscape, exhibiting complementary trade-offs across phonon dispersion, surface energetics, and elastic response. By integrating these diverse functional forms through a weighted symbolic ensemble, we derive a composite potential that surpasses the fidelity of its constituent models. The resulting ensemble effectively mitigates individual biases, delivering superior consistency with DFT benchmarks across equation-of-state curvature, phonon spectra, and melting dynamics. This approach demonstrates that combining transfer learning with ensemble symbolic regression yields compact, transparent potentials capable of robust prediction across equilibrium and non-equilibrium states.

cond-mat.mtrl-sci↗

arXiv2025

Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning

The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture multiscale physics. Here, we demonstrate such an approach using symbolic regression (SR) with equation learner networks and a reinforcement learning search engine to derive interpretable equations for interatomic interactions. Training data were generated through nested ensemble sampling with density functional theory (DFT) energetics, spanning crystalline to highly disordered states. The optimization of the learner network employed continuous-action Monte Carlo Tree Search (MCTS) combined with gradient descent, enabling efficient exploration of function space. For copper as a representative transition metal, an unconstrained search produced models that outperformed fixed-form Sutton-Chen EAM potentials. The SR-derived models (SR1 and SR2) reproduced key material properties - lattice constants, cohesive energies, equations of state, elastic constants, phonon dispersion, defect formation energies, surface/bulk energetics, and phase transformation with significantly improved accuracy. Furthermore, stringent melting simulations using two-phase solid-amorphous interfaces confirmed that SR models accurately capture the interplay of vibrational entropy, cohesive energy, and structural dynamics, surpassing SC-EAM in both qualitative and quantitative predictions. This highlights the potential of SR to deliver fast, accurate, flexible, and physically meaningful potentials, advancing predictive modeling across scales.

cond-mat.mtrl-sci↗