28 citations · 38 across the 10 of their papers we have counts for
9 papers · 1 filter
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…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
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…
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…
Development of a Machine Learning Potential to Study Structure and Thermodynamics of Nickel Nanoclusters
Suvo Banik, Partha Sarathi Dutta, Sukriti Manna +1
Machine Learning (ML) potentials such as Gaussian Approximation Potential (GAP) have demonstrated impressive capabilities in mapping structure to properties across diverse systems.…
A Continuous Action Space Tree search for INverse desiGn (CASTING) Framework for Materials Discovery
Suvo Banik, Troy Loefller, Sukriti Manna +5
Fast and accurate prediction of optimal crystal structure, topology, and microstructures is important for accelerating the design and discovery of new materials. A challenge lies i…
CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment
Suvo Banik, Debdas Dhabal, Henry Chan +4
Machine learning models and applications in materials design and discovery typically involve the use of feature representations or "descriptors" followed by a learning algorithm th…
Learning with Delayed Rewards -- A case study on inverse defect design in 2D materials
Suvo Banik, Troy D Loeffler, Rohit Batra +3
Defect dynamics in materials are of central importance to a broad range of technologies from catalysis to energy storage systems to microelectronics. Material functionality depends…