2018/07/31 by Ran Adler, Chang‐Jong Kang, Chang-Jong Kang +2
Materials Science · Physics and Astronomy · #Bridging (networking) #Computer science #Electronic and Structural Properties of Oxides #Machine Learning in Materials Science #Machine learning #Material Design #Materials science #Nanotechnology #Physics #Stability (learning theory) #Statistical physics #Superconductivity in MgB2 and Alloys #Workflow #cond-mat.str-el
paper · pdf · doi:10.1088/1361-6633/aadca4
published as Reports on Progress in Physics 2018 · This is the published version
openalex publication_date 2018/08/23 · arxiv created 2019/02/04 · arxiv updated 2019/02/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The design of correlated materials challenges researchers to combine the maturing, high throughput framework of DFT-based materials design with the rapidly-developing first-principles theory for correlated electron systems. We review the field of correlated materials, distinguishing two broad classes of correlation effects, static and dynamics, and describe methodologies to take them into account. We introduce a material design workflow, and illustrate it via examples in several materials classes, including superconductors, charge ordering materials and systems near an electronically driven metal to insulator transition, highlighting the interplay between theory and experiment with a view towards finding new materials. We review the statistical formulation of the errors of currently available methods to estimate formation energies. We formulate an approach for estimating a lower-bound for the probability of a new compound to form. Correlation effects have to be considered in all the material design steps. These include bridging between structure and property, obtaining the correct structure and predicting material stability. We introduce a post-processing strategy to take them into account.