arXiv subjects

Udaykumar Gajera

Publications and source records attributed to Udaykumar Gajera.

2 recordsLinked to original sources

arXiv2026

DISCOVER: A Physics-Informed, GPU-Accelerated Symbolic Regression Framework

Symbolic Regression (SR) enables the discovery of interpretable mathematical relationships from experimental and simulation data. These relationships are often coined descriptors which are defined as a fundamental materials property that is directly correlated to a desired or undesired functional property of the material. Although established approaches such as Sure Independence Screening and Sparsifying Operator (SISSO) have successfully identified low-dimensional descriptors within large feature spaces many existing SR tools integrate poorly with modern Python workflows, offer limited control over the symbolic search space, or struggle with the computational demands of large-scale studies. This paper introduces DISCOVER (Data-Informed Symbolic Combination of Operators for Variable Equation Regression), an open-source symbolic regression package developed to address these challenges through a modular, physics-motivated design. DISCOVER allows users to guide the symbolic search using domain knowledge, constrain the feature space explicitly, and take advantage of optional GPU acceleration to improve computational efficiency in data-intensive workflows, enabling reproducible and scalable SR workflows. The software is intended for applications in computational physics, computational chemistry, and materials science, where interpretability, physical consistency, and execution time are especially important, and it complements general-purpose SR frameworks by emphasizing the discovery of physically meaningful models.

physics.comp-ph↗

arXiv2022

Towards machine learning for microscopic mechanisms: a formula search for crystal structure stability based on atomic properties

Machine Learning (ML) techniques are revolutionizing the way to perform efficient materials modeling. Nevertheless, not all the ML approaches allow for the understanding of microscopic mechanisms at play in different phenomena. To address the latter aspect, we propose a combinatorial machine-learning approach to obtain physical formulas based on simple and easily-accessible ingredients, such as atomic properties. The latter are used to build materials features that are finally employed, through Linear Regression, to predict the energetic stability of semiconducting binary compounds with respect to zincblende and rocksalt crystal structures. The adopted models are trained using dataset built from first-principles calculations. Our results show that already one-dimensional (1D) formulas well describe the energetics; a simple grid-search optimization of the automatically-obtained 1D-formulas enhances the prediction performances at a very small computational cost. In addition, our approach allows to highlight the role of the different atomic properties involved in the formulas. The computed formulas clearly indicate that "spatial" atomic properties (i.e. radii indicating maximum probability densities for $s,p,d$ electronic shells) drive the stabilization of one crystal structure with respect to the other, suggesting the major relevance of the radius associated to the $p$-shell of the cation species.

cond-mat.mtrl-sci↗