First-principles and machine learning predictions of elasticity in severely lattice-distorted high-entropy alloys with experimental validation

Cluster-formula-embedded machine learning for design of multicomponent β-Ti alloys with low Young’s modulus

“…), to search for high-entropy alloys with high microhardness in Al-Co-Cr-Cu-Fe-Ni system, in which the predicted results are well consistent with the experiments since these selected characteristic parameters are closely related to the desired property 29 . Similarly, the combination of structural and compositional features (cohesive energy, atomic radius, and electronegativity) with the ML could well predict the elastic properties of Al-Co-Cr-Fe-Ni high-entropy alloys 30 . Xu et al embedded the physical-metallurgy parameters, the volume fraction, and driving force of second-phase precipitates that represent the microstructural features, into the ML model to design advanced ultrahighstrength stainless steels successfully 31 .…”

Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry

“…To establish the strongly correlated connection between spin and orbit in solids, further to understand the structure-activity relationship of energy materials, it is absolutely necessary to use machine learning algorithms in quantum chemistry techniques. Besides decoding the fundamental relationships behinds structure and properties, the breakthrough of technologies, including synchrotron radiation [85], neutron diffraction [86], spherical aberra- https://engine.scichina.com/doi/10.1016/j.jechem.2020.05.044 tion corrected scanning transmission electron microscope [87], and so on, also promote the research and development of energy materials.…”

High-temperature materials for structural applications: New perspectives on high-entropy alloys, bulk metallic glasses, and nanomaterials

“…Extension of the AI technology to correlate in situ advanced light-source results, traditional protocols, and high-throughput examinations is expected in future elevated-temperature materials development, including HEAs. 74…”