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通过机器学习转变催化:新兴工具和下一代战略
Pengxin Pu1, Haisong Feng1, Xin Song1
1State Key Laboratory of Chemical Resource Engineering, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, P. R. China.
机器学习 (ML) 通过加速开发,彻底改变了催化剂的发现. 本综述涵盖了催化剂中的ML,从传统方法到深度学习 (DL),突出了有效的催化剂设计的应用和未来挑战.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 催化剂对于化学工业至关重要,但发现新催化剂的传统方法很慢.
- 机器学习 (ML) 提供了一种强大,高效的方法来加速催化剂的开发.
研究的目的:
- 提供ML在催化中的应用的全面概述.
- 讨论催化剂设计和反应预测中的ML的挑战和未来方向.
主要方法:
- 审查传统的机器学习和深度学习 (DL) 技术.
- 对建模策略,算法框架和催化剂设计,反应预测和表面吸附中的应用进行分析.
- 讨论数据挑战,可解释性和与实验工作流程的整合.
主要成果:
- ML,特别是DL,在加速催化剂的发现和开发方面显示出显著的前景.
- 关键应用包括催化剂设计,反应预测和模拟表面吸附现象.
- 当前的挑战涉及数据质量,模型解释性和实验集成.
结论:
- ML正在改变催化化学,在催化剂开发中提供前所未有的效率.
- 解决数据碎片化,可解释性和工作流集成是推动ML在催化中的关键.
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