Work

Accelerating sustainable glass discovery: integrating molecular dynamics, machine learning, and robotic synthesis

Year: 2026

Type: article

Abstract: Discovering sustainable glass compositions demands navigating vast chemical spaces – a challenge that conventional experimentation cannot meet efficiently. Here we introduce the Simulation-Calibrated Active Learning Estimator (SCALE), a closed-loop framework uniting high-throughput molecular dynamics (MD), machine learning (ML), and robotic synthesis to bridge the gap between simulation and experiment. Over 42,000 melt–quench MD simulations with compact cells (≈ 200–500 atoms) train ML models that predict density and elastic moduli with cross-validated R 2 values up to 0.98. Comparison with 55 robotically synthesized sodium alumino-borosilicate glasses reveals systematic density overestimation of up to 5%. Rather than naively augmenting training data, SCALE iteratively learns a composition-dependent calibration from minimal targeted measurements. The protocol converges within two to four iterations of six measurements each, significantly reducing density errors and enabling glass optimization with fewer than 20 strategically chosen experiments.

Source: npj Computational Materials

DOI: 10.1038/s41524-026-02249-w

Authors: Felix Arendt, Tina Waurischk, Stefan Reinsch, Andrea S. S. de Camargo, Marek Sierka

Institutions: Friedrich Schiller University Jena, Federal Institute For Materials Research and Testing

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,490

Funders: Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung