arXiv subjects

Suvo Banik

Publications and source records attributed to Suvo Banik.

10 recordsLinked to original sources

arXiv2026

From Stacking Disorder to Cubic Order: Ice Crystallization from Deeply Supercooled Water

Crystallization far from equilibrium can generate morphologies that defy classical crystal habits, yet the microscopic mechanisms linking atomic-scale disorder to emergent macroscopic order remain elusive. Here we use in situ cryogenic transmission electron microscopy with a membrane-encapsulated microdroplet platform to directly visualize the freezing of deeply supercooled water at molecular resolution. We show that homogeneous nucleation produces stacking-disordered ice composed of mixed hexagonal and cubic sequences, in which cubic ice initially exists only as isolated monolayers. The gradual thickening of these cubic layers constitutes the key kinetic mechanism that governs the entire crystallization pathway. As thickening proceeds, nanoscale, defect-free cubic ice germs nucleate on the basal planes of the disordered lattice. These faceted cubic germs act as facet-registered kinetic seeds that enforce cubic twinning and sequentially multiply growth branches. This kinetic pathway reproducibly generates robust eight-branched dendrites with global cubic (octahedral) symmetry, even though each branch remains highly stacking-disordered. At later stages, latent heat release drives a crossover to the thermodynamically favored hexagonal phase; remarkably, the pre-established global cubic symmetry is retained. These results reveal how strong kinetic driving forces convert microscopic disorder into emergent macroscopic symmetry, providing a general framework for understanding and controlling rapid crystallization far from equilibrium.

cond-mat.mtrl-sci↗

arXiv2026

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

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 broad set of community-developed LLM applications in an effort to identify emerging patterns in how these systems can be used across the scientific research lifecycle. We organize the projects into two complementary categories: Knowledge Infrastructure, systems that structure, retrieve, synthesize, and validate scientific information; and Action Systems, systems that execute, coordinate, or automate scientific work across computational and experimental environments. The submissions reveal a shift from single-purpose LLM tools toward integrated, multi-agent workflows that combine retrieval, reasoning, tool use, and domain-specific validation. Prominent themes include retrieval-augmented generation as grounding infrastructure, persistent structured knowledge representations, multimodal and multilingual scientific inputs, and early progress toward laboratory-integrated closed-loop systems. Together, these results suggest that LLMs are evolving from general-purpose assistants into composable infrastructure for scientific reasoning and action. This work provides a community snapshot of that transition and a practical taxonomy for understanding emerging LLM-enabled workflows in materials science and chemistry.

cond-mat.mtrl-sci↗

arXiv2026

Sub-1-Angstrom-Resolution Imaging Reveals Phase Contrast Transition in Ice Ih Caused by Basal Stacking Faults

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 single crystals. Here, we show that this pattern commonly arises from intrinsic basal stacking faults instead. A translational boundary separating domains of comparable thickness, with an in-plane offset of $(\frac{2}{3} a_{1} + \frac{1}{3} a_{2})$, produces this honeycomb-like contrast. Stacking domains translated in nonequivalent directions yields patterns resembling cubic ice (Ic) along [111] but with a 3-fold symmetry. We imaged this structure at a record-breaking line resolution of 89 picometers, finer than the O-H covalent bond length. These findings highlight the defect tolerance of ice's molecular packing and clarify the structural relationships among hexagonal, stacking-disordered, and cubic ice phases. This resolution milestone opens new avenues for characterizing subtle structural perturbations of water in the solid state.

cond-mat.mtrl-sci↗

arXiv2026

Physics-Informed Tree Search for High-Dimensional Computational Design

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 constrained black-box searches over rugged objective landscapes, where function evaluations are expensive, and gradients are unavailable or unreliable. Conventional global search engines and optimizers struggle in such settings due to the exponential scaling of design spaces, the presence of multiple local basins, and the absence of physical guidance in sampling. We present a physics-informed Monte Carlo Tree Search (MCTS) framework that extends policy-driven tree-based reinforcement concepts to continuous, high-dimensional scientific optimization. Our method integrates population-level decision trees with surrogate-guided directional sampling, reward shaping, and hierarchical switching between global exploration and local exploitation. These ingredients allow efficient traversal of non-convex, multimodal landscapes where physically meaningful optima are sparse. We benchmark our approach against standard global optimization baselines on a suite of canonical test functions, demonstrating superior or comparable performance in terms of convergence, robustness, and generalization. Beyond synthetic tests, we demonstrate physics-consistent applicability to (i) crystal structure optimization from clusters to bulk, (ii) fitting of classical interatomic potentials, and (iii) constrained engineering design problems. Across all cases, the method converges with high fidelity and evaluation efficiency while preserving physical constraints. Overall, our work establishes physics-informed tree search as a scalable and interpretable paradigm for computational design and high-dimensional scientific optimization, bridging discrete decision-making frameworks with continuous search in scientific design workflows.

cs.LG↗

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↗

arXiv2024

Molecular-Resolution Imaging of Ice Crystallized from Liquid Water

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 stabilization and angstrom-resolution electron imaging of ice Ih crystallized from liquid water by developing cryogenic liquid-cell transmission electron microscopy (CRYOLIC-TEM). We combine lattice mapping with molecular dynamics simulations to reveal that ice formation is highly tolerant to nanoscale defects such as misoriented subdomains and trapped gas bubbles, which are stabilized by molecular-scale structural motifs. Importantly, bubble surfaces adopt low-energy nanofacets and create negligible strain fields in the surrounding crystal. These bubbles can dynamically nucleate, grow, migrate, dissolve, and coalesce under electron irradiation and be monitored in situ near a steady state. This work opens the door to understanding water crystallization behaviors at an unprecedented spatial resolution.

cond-mat.mtrl-sci↗

arXiv2024

Development of a Machine Learning Potential to Study Structure and Thermodynamics of Nickel Nanoclusters

Machine Learning (ML) potentials such as Gaussian Approximation Potential (GAP) have demonstrated impressive capabilities in mapping structure to properties across diverse systems. Here, we introduce a GAP model for low-dimensional Ni nanoclusters and demonstrate its flexibility and effectiveness in capturing the energetics, structural diversity and thermodynamic properties of Ni nanoclusters across a broad size range. Through a systematic approach encompassing model development, validation, and application, we evaluate the model's efficacy in representing energetics and configurational features in low-dimensional regimes, while also examining its extrapolative nature to vastly different spatiotemporal regimes. Our analysis and discussion shed light on the data quality required to effectively train such models. Trajectories from large scale MD simulations using the GAP model analyzed with data-driven models like Graph Neural Networks (GNN) reveal intriguing insights into the size-dependent phase behavior and thermo-mechanical stability characteristics of porous Ni nanoparticles. Overall, our work underscores the potential of ML models which coupled with data-driven approaches serve as versatile tools for studying low-dimensional systems and complex material dynamics.

cond-mat.mtrl-sci↗

arXiv2022

A Continuous Action Space Tree search for INverse desiGn (CASTING) Framework for Materials Discovery

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 in the exorbitantly large structural and compositional space presented by the various elements and their combinations. Speed, accuracy, and scalability are three desirables for any inverse design tool to sample efficiently across such a vast space. While traditional global optimization approaches (e.g., evolutionary algorithm, random sampling based) have demonstrated the ability to predict new crystal structures that can be used as super-hard materials, semiconductors, and photovoltaic materials to name a few, it is highly desirable to develop approaches that converge faster to the solution, have better solution quality, and are scalable to high dimensionality. Reinforcement learning (RL) approaches are emerging as powerful design tools capable of addressing these issues but primarily operate in discrete action space. In this work, we introduce CASTING, which is an RL-based scalable framework for crystal structure, topology, and potentially microstructure prediction. CASTING employs an RL-based continuous search space decision tree (MCTS -Monte Carlo Tree Search) algorithm with three important modifications (i) a modified rewards scheme for improved search space exploration (ii) a 'windowing' or 'funneling' scheme for improved exploitation and (iii) adaptive sampling during playouts for efficient and scalable search. Using a set of representative examples ranging from metals such as Ag to covalent systems such as C and multicomponent systems (graphane, boron nitride, and complex correlated oxides), we demonstrate the accuracy, the speed of convergence, and the scalability of CASTING to discover new metastable crystal structures and phases that meet the target objective.

cond-mat.mtrl-sci↗

arXiv2022

CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment

Machine learning models and applications in materials design and discovery typically involve the use of feature representations or "descriptors" followed by a learning algorithm that maps them to a user-desired property of interest. Most popular mathematical formulation-based descriptors are not unique across atomic environments or suffer from transferability issues across different application domains and/or material classes. In this work, we introduce the Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) and diverse classes ranging from metals, oxides, non-metals and even hierarchical materials such as zeolites and semi ordered materials such as mesophases. We first demonstrate a case study where the classification is based on a global, structure-level representation such as space group and structural dimensionality (e.g., bulk, 2D, clusters etc.). Using representative materials such as polycrystals and zeolites, we next demonstrate the transferability of our network in successfully performing local atom-level classification tasks, such as grain boundary identification and other heterointerfaces. We also demonstrate classification in (thermal) noisy dynamical environments using a representative example of crystal nucleation and growth of a zeolite polymorph from an amorphous synthesis mixture. Finally, we characterize the formation of a binary mesophase and its phase transitions and the growth of ice, demonstrating the performance of CEGANN in systems with thermal noise and compositional diversity. Overall, our approach is agnostic to the material type and allows for multiscale classification of features ranging from atomic-scale crystal structures to heterointerfaces to microscale grain boundaries.

cond-mat.mtrl-sci↗

arXiv2021

Learning with Delayed Rewards -- A case study on inverse defect design in 2D materials

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 strongly on the nature and organization of defects, their arrangements often involve intermediate or transient states that present a high barrier for transformation. The lack of knowledge of these intermediate states and the presence of this energy barrier presents a serious challenge for inverse defect design, especially for gradient-based approaches. Here, we present a reinforcement learning (Monte Carlo Tree Search) based on delayed rewards that allow for efficient search of the defect configurational space and allows us to identify optimal defect arrangements in low dimensional materials. Using a representative case of 2D MoS2, we demonstrate that the use of delayed rewards allows us to efficiently sample the defect configurational space and overcome the energy barrier for a wide range of defect concentrations (from 1.5% to 8% S vacancies), the system evolves from an initial randomly distributed S vacancies to one with extended S line defects consistent with previous experimental studies. Detailed analysis in the feature space allows us to identify the optimal pathways for this defect transformation and arrangement. Comparison with other global optimization schemes like genetic algorithms suggests that the MCTS with delayed rewards takes fewer evaluations and arrives at a better quality of the solution. The implications of the various sampled defect configurations on the 2H to 1T phase transitions in MoS2 are discussed. Overall, we introduce a Reinforcement Learning (RL) strategy employing delayed rewards that can accelerate the inverse design of defects in materials for achieving targeted functionality.

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