
doi: 10.20944/preprints202109.0176.v1 , 10.3390/ma14195764 , 10.5281/zenodo.14787622 , 10.5281/zenodo.14787621
pmid: 34640157
pmc: PMC8510221
handle: 10281/450819
doi: 10.20944/preprints202109.0176.v1 , 10.3390/ma14195764 , 10.5281/zenodo.14787622 , 10.5281/zenodo.14787621
pmid: 34640157
pmc: PMC8510221
handle: 10281/450819
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.
Data science; Defects; Dislocations; Informatics; Machine learning; Mechanical deformation; Metal alloys; Ontology;, metallurgy, Review
Data science; Defects; Dislocations; Informatics; Machine learning; Mechanical deformation; Metal alloys; Ontology;, metallurgy, Review
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 51 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
