28 citations · 38 across the 10 of their papers we have counts for
10 papers
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…
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…
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…