从可塑性到带隙:机器学习加速了矿材料的发现和应用
Shiyan Wang1, Chaopeng Liu1, Weiyao Hao1
1College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), State Key Laboratory of Flexible Electronics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
ACS nano
|August 6, 2025
概括
机器学习加速了矿材料的发现,克服了太阳能电池等应用的传统限制. 本综述详细介绍了ML工作流程,属性预测以及先进矿研究的未来方向.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 矿材料具有独特的特性,适合用于太阳能电池,光探测器,催化剂和LED.
- 对于矿的传统研究方法往往是缓慢和计算要求苛刻的.
- 机器学习 (ML) 提供了一种强大的方法来加速矿的发现和优化.
研究的目的:
- 审查机器学习在矿材料科学中的影响和方法.
- 分析ML应用在矿研究中的趋势,工作流程和算法.
- 突出ML在预测矿性质和指导实际应用中的作用.
主要方法:
- 在矿研究中对最近的机器学习应用进行文献审查和分析.
- 对矿材料的机器学习工作流程和基本算法的划分.
- 对矿结构组成关系的分析,以预测可形成性和带隙等属性.
主要成果:
- 机器学习显著加快了矿材料的发现和优化.
- ML准确地预测了矿的关键性质,包括可形成性和带隙.
- 介绍了ML在矿技术中的四个实际应用,以及挑战和未来前景.
结论:
- 机器学习正在通过实现更快的发现和属性预测来彻底改变矿研究.
- 本综述为利用ML在矿开发中提供了洞察力和指导.
- 进一步整合ML有望增强矿材料的实际应用.
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