51 citations · 51 across the 6 of their papers we have counts for
6 papers
Universal Thermodynamic Interatomic Potentials for Crystalline Materials
Juno Nam, Bowen Deng, Xiaochen Du +3
Free energies govern solid-state phase stability, yet computational materials discovery still relies largely on ground-state energies because free energy calculations require ensem…
MetaDNS: Enhancing Exploration in Discrete Neural Samplers via Well-Tempered Metadynamics
Xiaochen Du, Juno Nam, Jaemoo Choi +7
Sampling from discrete distributions with multiple modes and energy barriers is fundamental to machine learning and computational physics. Recent discrete neural samplers like MDNS…
Harnessing AtomisticSkills for Agentic Atomistic Research
Bowen Deng, Bohan Li, Matthew Cox +20
Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) have demonstrated research capabi…
Scaling Autoregressive Models for Lattice Thermodynamics
Xiaochen Du, Juno Nam, Sulin Liu +1
Predicting how materials behave under realistic conditions requires understanding the statistical distribution of atomic configurations on crystal lattices, a problem central to al…
Accelerating and enhancing thermodynamic simulations of electrochemical interfaces
Xiaochen Du, Mengren Liu, Jiayu Peng +6
Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsor…
Machine-learning-accelerated simulations to enable automatic surface reconstruction
Xiaochen Du, James K. Damewood, Jaclyn R. Lunger +4
Understanding material surfaces and interfaces is vital in applications like catalysis or electronics. By combining energies from electronic structure with statistical mechanics, a…