Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges

Abstract

In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials’ discovery or develop new understanding of materials’ behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.

Discovering Superhard B–N–O Compounds by Iterative Machine Learning and Evolutionary Structure Predictions

“…Motivated by the study of Bhat et al, here we employ first-principles calculation and machine learning (ML) simulation to search for new superhard B–N–O compounds. Data-driven approaches have proven to be powerful in materials discovery, − and several ML models have been applied to find superhard compounds. ,− The first important step in building ML models is sample data acquisition. However, although there are several online computational materials databases, − only limited information exists on B–N–O.…”

Machine learning potential for interacting dislocations in the presence of free surfaces

“…Machine Learning (ML) methods have been recently exploited in a large number of applications in materials science 1 – 4 , solid-state physics 5 , molecular chemistry 6 – 10 , and crystallography 11 . The application of ML methods in these fields are allowing for data-intensive tasks that were previously considered inaccessible, such as the compositional search for material-discovery 2 , 4 , 12 or the automated defect detection and classifications 13 , and are replacing conventional simulation techniques by allowing for better accuracy with faster computational time. In particular, the application of ML to the development of Force Fields (FFs) for Molecular Dynamics (MD) simulations 14 – 18 have been proposed as a way to overcome the limitation of the classical modeling approaches.…”

Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology

“…[ 11–22 ] It relies heavily on ML/AI‐based data‐driven approaches. [ 23–31 ] In materials informatics, sub‐areas focused on certain groups of materials also began to be formed, including hierarchical materials informatics dealing with hierarchically organized microstructures in solid materials, [ 32 ] nanomaterials informatics (nanoinformatics) focused on nanostructures, [ 33,34 ] materials informatics for mechanical deformation, [ 35 ] etc. Such a division of chemoinformatics and materials informatics into specialized sub‐areas makes sense because this allows researchers to concentrate on general aspects common to specific types of chemical objects, physicochemical processes, and materials.…”