Accelerating sustainable glass discovery: integrating molecular dynamics, machine learning, and robotic synthesis
TL;DR
The Simulation-Calibrated Active Learning Estimator (SCALE), a closed-loop framework uniting high-throughput molecular dynamics, machine learning, and robotic synthesis to bridge the gap between simulation and experiment, is introduced.
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 R2 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 substantially reduces density errors over three experimental iterations of six measurements each and demonstrates the potential for glass optimization with a small number of strategically chosen experiments.