Machine Learning Paves the Way for High Entropy Compounds Exploration: Challenges, Progress, and Outlook

Machine learning (ML) has emerged as a powerful tool in the research field of high entropy compounds (HECs), which have gained worldwide attention due to their vast compositional space and abundant regulatability. However, the complex structure space of HEC poses challenges to traditional experimental and computational approaches, necessitating the adoption of machine learning. Microscopically, machine learning can model the Hamiltonian of the HEC system, enabling atomic-level property investigations, while macroscopically, it can analyze macroscopic material characteristics such as hardness, melting point, and ductility. Various machine learning algorithms, both traditional methods and deep neural networks, can be employed in HEC research. Comprehensive and accurate data collection, feature engineering, and model training and selection through cross-validation are crucial for establishing excellent ML models. ML also holds promise in analyzing phase structures and stability, constructing potentials in simulations, and facilitating the design of functional materials. Although some domains, such as magnetic and device materials, still require further exploration, machine learning's potential in HEC research is substantial. Consequently, machine learning has become an indispensable tool in understanding and exploiting the capabilities of HEC, serving as the foundation for the new paradigm of Artificial-intelligence-assisted material exploration. Machine learning (ML) revolutionizes high entropy compounds (HECs) research, addressing their complex structures. It models HEC at atomic and macroscopic levels, using various algorithms. Accurate data, feature engineering, and cross-validation are key for robust models. ML's potential in HEC exploration is significant, driving Artificial-intelliegence-assisted material discovery.image

Automated summary

Machine learning plays a crucial role in the research of high entropy compounds (HECs). It enables modeling and analysis of HEC at both atomic and macroscopic levels, with a wide range of applications. However, accurate data collection, feature engineering, and model training are essential for building robust machine learning models.