利用机器学习进行高熵合金催化:关注吸附能预测

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Qi Wang, Yonggang Yao
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引用次数: 0

摘要

高熵合金(HEAs)由于其固有的成分、结构和位点水平的多样性,具有高度可调的催化性能,已成为催化剂应用的有希望的候选者。然而,这些复杂性对传统的“试错”实验或计算成本高昂的“蛮力”从头计算提出了巨大的挑战。机器学习(ML)通过建立从组合、结构或现场环境到HEA属性的高效、可扩展的映射,展示了解决这些挑战的巨大潜力。在这些性质中,吸附能是表征催化中间体与表面位点结合强度的重要指标。本文综述了基于机器学习的HEAs吸附能预测方法的研究进展。介绍了两种主要策略:从非松弛结构进行“直接”预测和通过ML电位引导松弛建模进行“迭代”预测。这两种策略都可以利用手工制作的功能或端到端框架,如图神经网络。我们还讨论了如何将大规模数据库上的预训练模型扩展到域外HEA系统。除了方法论之外,我们还解决了关键挑战和未来方向,包括对机器学习策略进行基准测试,开发hea特定数据集,预训练和微调,集成链式机器学习模型,推进多目标优化,以及将机器学习预测与实验验证联系起来。通过批判性地评估现有策略和突出新兴趋势,本综述强调了ML在推进吸附能预测方面的关键作用,为加速发现和优化HEA催化剂提供了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Harnessing machine learning for high-entropy alloy catalysis: a focus on adsorption energy prediction

Harnessing machine learning for high-entropy alloy catalysis: a focus on adsorption energy prediction

High-entropy alloys (HEAs) have emerged as promising candidates for catalyst applications due to their inherent compositional, structural, and site-level diversities, which enable highly tunable catalytic properties. However, these complexities pose grand challenges for traditional “trial-and-error” experimentation or computationally expensive “brute-force” ab initio calculations. Machine learning (ML) demonstrates great potential to address these challenges by establishing efficient, scalable mappings from composition, structure or site environment to HEA properties. Among these properties, adsorption energy, which quantifies the binding strength between catalytic intermediates and surface sites, is a crucial indicator of catalytic activity. This review provides a comprehensive overview of ML-driven strategies for adsorption energy prediction in the context of HEAs. Two primary strategies are introduced: “direct” prediction from unrelaxed structure and “iterative” prediction via ML potential-guided relaxation modeling. Both strategies can leverage handcrafted features or end-to-end frameworks such as graph neural networks. We also discuss how pretrained models on large-scale databases can extend to out-of-domain HEA systems. Beyond methodology, we address key challenges and future directions, including benchmarking ML strategies, developing HEA-specific datasets, pretraining and fine-tuning, integrating chained ML models, advancing multi-objective optimization, and bridging ML predictions with experimental validation. By critically evaluating existing strategies and highlighting emerging trends, this review underscores the critical role of ML in advancing adsorption energy predictions, offering a foundation for accelerating the discovery and optimization of HEA catalysts.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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