Conspectus In chemistry and materials science, researchers and engineers discover,
design, and optimize chemical compounds or materials with their professional knowledge …

As the big data generated by the development of modern experiments and computing
technology becomes more and more accessible, the material design method based on …

The process parameters in powder manufacturing must be optimized to produce high-quality
powders with desired sizes depending on the use. Machine learning-driven optimization …

Mixing heterogeneous Li-ion conductive materials is one potential way to enhance Li-ion
conductivity more than that of the parent materials. However, the huge number of possible …

Abstract Knowledge of phase diagrams is essential for material design as it helps in
understanding microstructure evolution during processing. The determination of phase …

In this paper, we perform a systematical investigation on searching for defect γ-graphyne
nanoribbons (γ-GYNRs) with optimal thermoelectric performance by utilizing nonequilibrium …

Materials chemists develop chemical compounds to meet often conflicting demands of
industrial applications. This process may not be properly modeled by black-box optimization …

This study is the first application of Bayesian optimization to the synthesis process of
superconducting materials. As a model case, the phase purity of BaFe 2 (As, P) 2 …

To efficiently construct phase diagrams of alloy systems, a machine learning-based method
advanced by thermodynamics on phase equilibria is proposed. With the use of uncertainty …

Compared to all-atom molecular dynamics (AA-MD) simulations, coarse-grained (CG) MD
simulations can significantly reduce calculation costs. However, existing CG-MD methods …