如何加速无机材料合成:从计算指南到数据驱动方法?
Yilei Wu1, Xiaoyan Li1, Rong Guo1,2
1Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing 211189, China.
National science review
|April 2, 2025
概括
机器学习 (ML) 通过指导实验和预测结果来加速无机材料合成. 本综述涵盖了计算机材料科学中的ML应用,方法和挑战.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 新型功能材料对于应对全球挑战至关重要.
- 实验合成是材料开发中的一个关键瓶.
- 计算能力和机器学习 (ML) 为优化合成提供了解决方案.
研究的目的:
- 审查计算机引导和ML辅助无机材料合成的最新进展.
- 提供物理模型,数据驱动方法和ML技术的全面概述.
- 突出该领域的挑战和机遇.
主要方法:
- 介绍常见的无机材料合成方法.
- 对合成可行性的热力学和动力学物理模型的讨论.
- 探索数据采集,材料描述符和ML技术.
- 基于实物数据源的ML应用的分类.
主要成果:
- 计算和ML方法显著加速和优化材料合成.
- 物理模型有助于理解合成的可行性.
- 在不同的材料数据源中,应用了各种ML技术.
- 该审查巩固了ML辅助无机材料合成目前的进展.
结论:
- 机器学习辅助合成是开发新型功能材料的强大工具.
- 需要进一步的研究来解决挑战,并解锁机遇在ML引导合成.
- 本综述为该领域未来的进步提供了科学指导.
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