Editing Materials science (section)

===Computational materials science===
{{Main|Computational materials science}}
With continuing increases in computing power, simulating the behavior of materials has become possible. This enables materials scientists to understand behavior and mechanisms, design new materials, and explain properties formerly poorly understood. Efforts surrounding [[integrated computational materials engineering]] are now focusing on combining computational methods with experiments to drastically reduce the time and effort to optimize materials properties for a given application. This involves simulating materials at all length scales, using methods such as [[density functional theory]], [[molecular dynamics]], [[Monte Carlo algorithm|Monte Carlo]], dislocation dynamics, [[Phase field models|phase field]], [[Finite element method|finite element]], and many more.<ref>{{Cite journal |last1=Schmidt |first1=Jonathan |last2=Marques |first2=Mário R. G. |last3=Botti |first3=Silvana |last4=Marques |first4=Miguel A. L. |date=2019-08-08 |title=Recent advances and applications of machine learning in solid-state materials science |journal=npj Computational Materials |volume=5 |issue=1 |page=83 |doi=10.1038/s41524-019-0221-0 |bibcode=2019npjCM...5...83S |s2cid=199492241 |issn=2057-3960|doi-access=free }}</ref>