Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf−In System

Abstract

There remain 21 systems (out of over 3500 possible combinations of the elements) in which the existence of the simple binary equiatomic phases AB has not been established experimentally. Among these, the presumed binary phase HfIn is predicted to adopt the tetragonal CuAu-type structure (space group P4/ mmm) by a recently developed machine-learning model and by structure optimization through global energy minimization. To test this prediction, the Hf-In system was investigated experimentally by reacting the elements in a 1:1 stoichiometry at 1070 K. Under the conditions investigated, the bulk and surface of the sample correspond to different crystalline phases but have nearly the same equiatomic composition, as revealed by energy-dispersive X-ray analysis. The structure of the bulk sample, which was solved from powder X-ray diffraction data through simulated annealing, corresponds to the γ-brass (CuZn) type (space group I4̅3 m) with Hf and In atoms disordered over four sites. The structure of crystals selected from the surface, which was solved using single-crystal X-ray diffraction data, corresponds to the CuPt type (space group Fm3̅ m) with Hf and In atoms partially disordered over three sites. The discrepancy between the predicted CuAu-type structure and the two experimentally refined crystal structures is reconciled through close inspection of structural relationships, which reveal that the γ-brass-type structure of the bulk HfIn phase is indeed derived through small distortions and defect formation within the CuAu-type structure.

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

“…Thus, machineassisted and autonomous capabilities are needed to perform comprehensive search. More recently, much attention has been given to ML for discovering functional compounds [49,53,108,[118][119][120][121]. Some research efforts computed ground states, selected ground-state phase diagrams, and physical properties for comparatively simple (up to quaternary) classes of inorganic compounds, while other efforts computed chemical reactivity and functional response for organic materials [108,118,[122][123][124].…”

Data-Driven Studies of Li-Ion-Battery Materials

“…The use of machine learning as a tool for materials discovery is rapidly growing. Examples can be found in the fields of thermoelectrics [4][5][6], superhard materials [7], thermochemical data [8,9], electronic properties [10][11][12][13][14], structural materials [15], functional materials [16][17][18], and structure classification [19][20][21][22][23]. Given the history of success of ML methods, it is natural to want to apply them to battery materials research.…”

Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures

“…Machine learning and data mining have been successfully applied to materials problems across various domains. For example, they have been used to successfully identify new shape memory alloys, ferroelectric materials, and novel thermoelectrics, − to make property predictions for heat capacity, , the band gap of crystalline solids, and elastic moduli, to optimize solar cells, to predict new phosphor materials, and to classify crystal structures of inorganic compounds. − These methods generate predictions for unknown examples based on statistical relationships and patterns discovered using reliable data, informative descriptions of those data, and machine learning algorithms. In this work, we use a machine learning approach to build predictive models for interatomic distances.…”