Machine learning for glass science and engineering: A review

Glassy dynamics of sticky hard spheres beyond the mode-coupling regime

“…5 More recently, machine learning has also been used to search for the link between structure and dynamics in dense disordered systems. [6][7][8][9] However, there is still no complete and formally exact theory of glassy dynamics that is founded entirely on first principles.…”

Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century

“…Some of the areas where reinforcement learning can have applications include the design of smart robots (robotics), which allow automated high‐throughput synthesis, characterization, selection, and design of novel materials 30 . A review of some of the algorithms used in materials science and glass science can be found elsewhere 15,34‐36 …”

“…30 A review of some of the algorithms used in materials science and glass science can be found elsewhere. 15,[34][35][36]…”

Quantitative calculation and prediction of the optical and spectroscopic properties of Er<sup>3+</sup>-doped tellurite glass

“…稀土离 子掺杂的激光玻璃光纤是光纤激光器的核心增益介 质, 然而, 目前在探索新型激光玻璃及性质时, 主要 依赖传统-炒菜式‖的经验试错法, 效率低下, 实验周 期长, 缺乏科学的理论指导, 制约了新型激光玻璃的 快速低成本研发. 而现有的物理与经验模型方法如 加和法、统计法、机器学习等侧重于玻璃结构与物理 性质的预测和研究, 对光学光谱和发光性质预测的 研究较少 [2,3] . 对稀土激光玻璃而言, 光学光谱特性 决定激光性能, 对实际应用至关重要.…”