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

TL;DR

DISCOVER is a GPU-accelerated, physics-informed symbolic regression framework that enhances interpretability and scalability for materials science applications, addressing limitations of existing tools in control, efficiency, and integration.

Contribution

It introduces a modular, physics-motivated, open-source SR package with GPU support, enabling guided, constrained, and scalable symbolic regression workflows.

Findings

Supports domain-guided symbolic search

Offers GPU acceleration for large-scale data

Facilitates discovery of physically meaningful models

Abstract

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.