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…

• NN
  National Natural Science Foundation of China

  Awards: 12192284, 52394163, 52101255, 52371223, 12474189
• CA
  China Academy of Engineering Physics

  Award: YZJJZQ2023016