arXiv subjects

Gautam Anand

Publications and source records attributed to Gautam Anand.

4 recordsLinked to original sources

arXiv2026

Information Entropy Based Crystal Structure Prediction of Chemically Disordered Alloys via Graph Convolutional Neural Networks

The phase prediction of chemically disordered alloys poses a significant computational challenge due to the combinatorial complexity of such materials. The high-throughput compositional exploration of chemically disordered alloys, including high-entropy alloys, requires an approach to efficiently explore the potential energy landscape of such complex materials. Additionally, a metric to quantify the potential energy landscape explored for phase prediction of the compositions needs to be defined. We propose an information-theoretic approach to phase prediction in chemically disordered alloys in the present work. We demonstrate the applicability of alchemical Monte Carlo sampling using an efficient Graph Convolutional Neural Network-Based machine learning model. We additionally demonstrate the applicability and limitations of the Bond Disproportion Vector (BDV) as a low-computational-cost descriptor and benchmark it against the state-of-the-art Smooth Overlap of Atomic Positions (SOAP) descriptor. We show the applicability of an information entropy-based metric for the phase prediction of binary (CoNi, MoW, FeNi and TaW), ternary (CoCrNi, CrFeNi), quaternary (CoCrFeNi) and quinary ($\mathrm{Al_x(CoCrFeNi)_{1-x}}$) alloys. Information entropy-based phase prediction can be applicable in challenging cases where conventional approaches are not feasible.

cond-mat.mtrl-sci↗

arXiv2026

Edge Dislocation Mediated Anomalous Charge Transfer in Face Centered Cubic High Entropy Alloys

Charge transfer in concentrated alloys governs their structural stability and functional response, and can be strongly perturbed by lattice defects. In high-entropy alloys, the interaction between edge dislocations and volume misfit plays a central role in solid-solution strengthening models; however, the influence of dislocations on the local charge transfer has not been explicitly investigated. In this work, large-scale ab initio calculations are employed to examine the dislocation-mediated charge transfer in CoNi, CoCrNi and CoCrFeMnNi alloys. The calculations reveal an anomalous charge redistribution near edge dislocation cores, including deviation from the conventional electronegativity trend. The observed behavior is shown to originate from collective electronegativity equalisation effects rather than simple pairwise atomic interactions. Furthermore, the asymmetric atomic volume response within the compressive and tensile regions of the dislocation field is rationalised in terms of anomalous magneto-volume fluctuations. These results establish a direct coupling between dislocation-induced electronic redistribution and local volumetric response in chemically complex alloys. The demonstrated coupling between dislocation-mediated charge transfer and atomic volume fluctuations provides a pathway toward electronically informed solid-solutions strengthening models and defect-aware alloy design strategy for chemically complex alloys. These findings further suggest that local electronic redistribution near dislocation-cores can play a critical role in governing the deformation behavior and defect-enbergetics in high-entropy alloys.

cond-mat.mtrl-sci↗

arXiv2026

SWE Atlas: Benchmarking Coding Agents Beyond Issue Resolution

We introduce SWE Atlas, a benchmark suite for coding agents spanning three professional software engineering workflows: Codebase Q&A (124 tasks), Test Writing (90 tasks), and Refactoring (70 tasks). SWE Atlas differs from prior SWE benchmarks in three key ways: it targets underrepresented but practically important task categories, uses comprehensive category-specific evaluation protocols, and adopts under-specified, agentic task formulations that better reflect real-world usage. Its evaluation framework combines programmatic checks with rubric-based assessment. This goes beyond functional correctness, evaluating software engineering quality, including test and refactor completeness, maintainability, reusable abstractions, and codebase hygiene. We evaluate a range of frontier and open-weight models on SWE Atlas and find that GPT-5.4 and Opus 4.7 achieve the strongest overall performance, while even the best open-weight models score poorly. Our analysis suggests that top models rely on extensive codebase exploration and runtime-driven reasoning. However, even top models consistently struggle with subtle edge cases, complex runtime analysis, and adherence to software engineering best practices. Overall, SWE Atlas provides a complementary evaluation suite for measuring both correctness and engineering quality in coding agents.

cs.LG↗

arXiv2022

Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models

We propose a scheme to construct predictive models for Hamiltonian matrices in atomic orbital representation from ab initio data as a function of atomic and bond environments. The scheme goes beyond conventional tight binding descriptions as it represents the ab initio model to full order, rather than in two-centre or three-centre approximations. We achieve this by introducing an extension to the Atomic Cluster Expansion (ACE) descriptor that represents Hamiltonian matrix blocks that transform equivariantly with respect to the full rotation group. The approach produces analytical linear models for the Hamiltonian and overlap matrices. Through an application to aluminium, we demonstrate that it is possible to train models from a handful of structures computed with density functional theory, and apply them to produce accurate predictions for the electronic structure. The model generalises well and is able to predict defects accurately from only bulk training data.

cond-mat.mtrl-sci↗