5 citations · 10 across the 6 of their papers we have counts for
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From Stacking Disorder to Cubic Order: Ice Crystallization from Deeply Supercooled Water
Yulin Lin, Weimin Guo, Suvo Banik +9
Crystallization far from equilibrium can generate morphologies that defy classical crystal habits, yet the microscopic mechanisms linking atomic-scale disorder to emergent macrosco…
Sub-1-Angstrom-Resolution Imaging Reveals Phase Contrast Transition in Ice Ih Caused by Basal Stacking Faults
Jingshan S. Du, Suvo Banik, Lehan Yao +3
Phase-contrast transmission electron microscopy (TEM) of hexagonal ice (Ih) along [0001] sometimes shows a honeycomb-like pattern, often interpreted as individual oxygen columns in…
Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning
Bilvin Varughese, Troy D. Loeffler, Suvo Banik +9
The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture mul…
Harnessing Machine Learning for Quantum-Accurate Predictions of Non-Equilibrium Behavior in 2D Materials
Yue Zhang, Robert J. Appleton, Kui Lin +5
Accurately predicting the non-equilibrium mechanical properties of two-dimensional (2D) materials is essential for understanding their deformation, thermo-mechanical properties, an…
Molecular-Resolution Imaging of Ice Crystallized from Liquid Water
Jingshan S. Du, Suvo Banik, Henry Chan +6
Despite the ubiquity of ice, a molecular-resolution image of ice crystallized from liquid water or the resulting defect structure has never been obtained. Here, we report the stabi…
Machine Learning the Metastable Phase Diagram of Materials
Srilok Srinivasan, Rohit Batra, Duan Luo +8
Phase diagrams are an invaluable tool for material synthesis and provide information on the phases of the material at any given thermodynamic condition. Conventional phase diagram…