高熵催化剂机器学习：方法与应用
Machine Learning for High-Entropy Catalysts: Methods and Applications
- Southern University of Science and Technology（南方科技大学）
- Pengcheng Laboratory（鹏城实验室）
- New York University（纽约大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文综述了机器学习在高熵合金催化剂研究中的最新方法与应用，通过预测模型和大型语言模型加速催化剂设计，并展望未来方向。
AI中文摘要：
高熵合金（HEAs）因其高构型熵、协同元素效应、可调电子结构和优异的结构稳定性，在各种反应中展现出卓越的催化性能。然而，高熵合金催化剂庞大的组分空间使得传统的实验和理论设计成本高昂且效率低下。近年来，数据驱动的机器学习（ML）方法已成为研究催化中高熵合金的强大工具。通过预测模型和机器学习替代模型，研究人员可以解读这些材料的复杂组分-结构-性能关系。此外，通过利用大语言模型（LLMs）进行知识提取、假设生成与验证，并作为构建集成设计工作流的基础，机器学习方法能够显著加速新型高熵合金催化剂的设计。本综述系统总结了机器学习方法用于催化中高熵合金的最新方法论进展和应用，讨论了面临的挑战，并对未来研究方向提出了见解，以支持催化剂的合理设计和高效开发。
英文摘要：
High-entropy alloys (HEAs) exhibit exceptional catalytic performance in various reactions due to their high configurational entropy, synergistic elemental effects, tunable electronic structures, and excellent structural stability. However, the vast compositional space of HEA catalysts makes traditional experimental and theoretical design costly and inefficient. In recent years, data-driven machine learning (ML) methods have emerged as powerful tools for studying HEAs in catalysis. Through predictive models and ML surrogates, researchers can decipher the intricate composition-structure-performance relationships of these materials. In addition, by leveraging large language models (LLMs) for knowledge extraction, hypothesis generation and validation, and as a foundation to build integrated design workflows, ML approaches can significantly accelerate the design of novel HEA catalysts. This review systematically summarizes the latest methodological advances and applications of ML methods for HEAs in catalysis, discusses the challenges, and offers insights into future research directions to support the rational design and efficient development of catalysts.
