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.
Machine-learning-accelerated materials discovery has yielded large numbers of computationally stable compounds, yet many remain experimentally unrealized, underscoring a persistent gap between prediction and synthesis. Here, we introduce a hierarchical screening framework that combines PBE-based thermodynamic stability, efficient dynamical-stability screening enabled by universal machine-learning interatomic potentials, and SCAN-based thermodynamic refinement. Applying this protocol to the 894 stable materials previously reported in Sci. Data 9, 302 (2022), we first curate 603 unique structures, of which only 298 remain thermodynamically stable on the complete PBE phase diagrams, demonstrating the critical role of competing phases in stability assessment. Dynamical screening then identifies 166 materials stable under both harmonic-phonon and finite-temperature molecular dynamics criteria, and SCAN phase diagrams further narrow this set to 109. Finally, by combining decomposition enthalpy with chemical-space completeness, we prioritize 25 candidates as high-confidence targets for experimental synthesis. This work provides a practical protocol for translating stability predictions into experimentally actionable synthesis targets, closing a key gap in machine-learning-driven materials discovery.
Yuqi An, Sihong Zhu, Joseph H. Montoya et al.· 0 citations
The integration of machine learning (ML) into materials science offers a transformative pathway toward fully autonomous synthesis workflows. For precise thin-film deposition techniques like molecular beam epitaxy (MBE), this automation is critical to overcome the time-consuming, manual navigation of high-dimensional thermodynamic phase spaces. Existing approaches for ML-assisted thin film growth predominantly rely on continuous Bayesian optimization (BO) models that assume smooth parameter landscapes. Consequently, they struggle to capture the abrupt crystallographic phase boundaries and narrow growth windows inherent to binary quantum materials. Here, we demonstrate an active learning protocol based on Sequential Model-Based Optimization (SMBO) designed specifically for the closed-loop MBE of such compounds. To overcome the limitations of continuous models while retaining the efficient exploration-exploitation logic of traditional BO, we combine a random forest surrogate model capable of capturing highly non-linear phase transitions and thermodynamics constraints of the growth process with an expected improvement function to predict optimum growth parameters. We apply this combined SMBO framework to the MBE of the topological Weyl ferromagnet Fe$_3$Sn, which exists as a metastable line compound. Using a small initial training set of fewer than twenty growth iterations, our active learning loop rapidly navigates a complex optimization landscape to identify an optimum growth window bounded by sharp transitions. Within only four active learning iterations, the absolute predictive error is halved to $\approx10\%$. This data-efficient framework paves the way for the autonomous discovery and thin-film synthesis of functional quantum materials.
Raghutheja Bollampally, Soumya Sankar, Yuqi Qin et al.· 0 citations
Molecular dynamics (MD) is essential for investigating atomic‐scale processes in materials and molecular systems, but the cost of high‐accuracy machine learning force field simulations still limits accessible system sizes and timescales. Here, we propose a practical model‐switching strategy for Deep Potential (DP)‐based MD simulations that alternates between independently trained DP models with different cutoff radii: a standard 6 Å model for higher accuracy and a reduced‐cutoff 4 Å model for faster inference. The method was implemented in LAMMPS/DeePMD and evaluated using solid‐phase anatase TiO
2
and liquid‐phase polyethylene glycol (PEG). For anatase TiO
2
, the 1:3 4–6 Å switching scheme preserved radial distribution function (RDF) correlations of 0.996 or higher relative to the 6 Å baseline while achieving a 1.24‐fold speedup. For PEG, the switching scheme maintained RDF correlations of 0.996 or higher with a 1.18‐fold speedup. Additional optimization using network‐size reduction and mixed‐precision inference achieved a 2.53‐fold speedup with RDF correlations of 0.975–0.988. Constant particle‐number, pressure, and temperature (NPT) simulations remained stable, whereas constant particle‐number, volume, and energy (NVE) simulations revealed system‐dependent energy‐drift behavior, particularly for aggressively optimized models. These results demonstrate that DP model switching provides a simple and practical route for accelerating structural MD simulations while highlighting the need for validation when strict energy conservation is required.
Ryuya Kanda, Megumu Yamazaki, Yuta Yoshimoto et al.· Advanced Intelligent Discove...· 0 citations
The viscosity and density of organic mixtures are essential properties for designing lubricants, solvents, and heat transfer fluids. In engineering practice, formulating a functional fluid requires understanding how these properties change with composition and temperature. However, exhaustive experimental characterization across the full parameter space is impractical due to the vast number of possible species and combinations. Here we introduce a mixture-aware 3D molecular representation learning strategy, built upon a pre-trained molecular encoder, that jointly encodes component structures, mole fractions, and temperature to achieve accurate predictions for organic mixtures. Fine-tuning on publicly available datasets covering a wide range of binary organic mixtures yields test-set R2 values of 0.973 for dynamic viscosity and 0.996 for density, significantly outperforming traditional machine learning baselines. Beyond this overall accuracy, the model captures non-monotonic viscosity changes upon mixing, surpassing simple linear or logarithmic mixing rules. The architecture is extendable to ternary and multicomponent mixtures, as verified via preliminary experiments. Using this model, we quantitatively analyze how molecular structure-branching, cycloalkane, and aromatic rings-affects viscosity-temperature behavior, which benefits the design of lubricants with superior viscosity-temperature performance. Altogether, this work provides a practical, data-driven tool for mixture property prediction, accelerating the rational formulation of functional fluids in chemical engineering.
Most computationally predicted materials are never synthesized because conventional synthesis optimization is slow, expertise-dependent, and iterative. Here we present a closed-loop framework that automates this expert workflow by placing human tacit knowledge in the loop through a large language model (LLM) that distills synthesis knowledge from the literature, high-throughput hyperspectral imaging for rapid film evaluation, and multi-objective Bayesian optimization guided by experimental feedback. In a paired optimization campaign, LLM-assisted initialization produced more Pareto-optimal samples and higher hypervolume than a Latin hypercube sampling baseline at matched trial counts, and this advantage persisted throughout iterative optimization. We demonstrate the framework by synthesizing the previously unreported perovskite-inspired compound Rb3BiI6 as thin films and validating the optimized films by optical bandgap analysis and X-ray diffraction. The framework transforms synthesis prediction from single-shot recommendation to iterative learning, providing a generalizable strategy to accelerate automated and fully autonomous experimental materials discovery.
Fang Sheng, Steven B. Torrisi, Amanda A. Volk et al.· 0 citations
Atomistic simulations have become indispensable tools for understanding glass structure, dynamics, and properties, yet persistent challenges limit their predictive power. This perspective examines three interconnected issues, namely, glass formation procedures, interatomic potential development, and machine learning (ML) applications, which emerged from the 5th International Workshop on Challenges of Atomistic Simulations of Glasses and Amorphous Materials. We identify convergent community priorities for standardized validation protocols and curated benchmark datasets with complete metadata. A systematic pathway forward is provided by a hierarchical validation framework for assessing the structural fidelity, property prediction, and behavioral realism of simulation techniques. Looking ahead, transformative advances are promised by the fusion of classical techniques with ML‐based approaches. Examples include the integration of swap Monte Carlo with ML potentials, leveraging foundation models through transfer or active learning, and fine‐tuning ML potentials with experimental data. Progress depends on community awareness and efforts committed to validating models, creating reproducible protocols, and sustained data sharing.
N. M. A. Krishnan, A. Pedone, Xiaonan Lu et al.· Journal of The American Cera...· 0 citations