Combinatorial screening for new materials in unconstrained composition space with machine learning

By Bryce Meredig, Amit Agrawal, S. Kirklin, James E. Saal, J. Doak, Alan J Thompson, Kunpeng Zhang, Alok N. Choudhary, Chris Wolverton. 2014

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Recent advances and applications of deep learning methods in materials science (2021, background): “…fractions 140,141 or of weight percentages of alloying compositions 142 – 145 , as well as more sophisticated hand-crafted descriptors or physical attributes to add known chemistry knowledge (e.g., electronegativity, valency, etc. of constituent elements) to the feature representations 146 – 151 .”
A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials (2016, unknown): “…example, there are now models to predict the melting temperatures of binary inorganic compounds, 21 the formation enthalpy crystalline compounds, 14,15,28 which crystal structure is likely to form at a certain composition, 5,16,29 – 31 band gap energies of certain classes of crystals 32,33 and the…”
Machine learning assisted design of high entropy alloys with desired property (2019, background): “Recently, the materials science community has begun to utilize machine learning (ML) tools,[27-29] such as identifying structural flow d efects in disordered solids and discovering inorganic-organic hybrid materials.”