MADE: Benchmark Environments for Closed-Loop Materials Discovery

TL;DR

MADE introduces a comprehensive benchmarking framework for autonomous, end-to-end materials discovery that simulates iterative, resource-constrained workflows, enabling evaluation and comparison of diverse discovery strategies.

Contribution

The paper presents MADE, a flexible, simulation-based framework for benchmarking closed-loop materials discovery pipelines, capturing the iterative decision-making process in resource-limited settings.

Findings

MADE effectively evaluates discovery algorithms in simulated environments.

Component ablation reveals impact on discovery efficiency.

Framework scales with system complexity, enabling diverse workflow analysis.

Abstract

Existing benchmarks for computational materials discovery primarily evaluate static predictive tasks or isolated computational sub-tasks. While valuable, these evaluations neglect the inherently iterative and adaptive nature of scientific discovery. We introduce MAterials Discovery Environments (MADE), a novel framework for benchmarking end-to-end autonomous materials discovery pipelines. MADE simulates closed-loop discovery campaigns in which an agent or algorithm proposes, evaluates, and refines candidate materials under a constrained oracle budget, capturing the sequential and resource-limited nature of real discovery workflows. We formalize discovery as a search for thermodynamically stable compounds relative to a given convex hull, and evaluate efficacy and efficiency via comparison to baseline algorithms. The framework is flexible; users can compose discovery agents from interchangeable components such as generative models, filters, and planners, enabling the study of arbitrary workflows ranging from fixed pipelines to fully agentic systems with tool use and adaptive decision making. We demonstrate this by conducting systematic experiments across a family of systems, enabling ablation of components in discovery pipelines, and comparison of how methods scale with system complexity.