Seรกn Kavanagh (@kavanagh_sean_) 's Twitter Profile
Seรกn Kavanagh

@kavanagh_sean_

Theoretically a material scientist at Harvard (@HUCEnvironment) ๐Ÿงช๐Ÿ‘จโ€๐Ÿ”ฌ via @CDT_ACM @ImpMaterials @UCLChemistry, @tcddublin ๐Ÿ‡ฎ๐Ÿ‡ช
Figuring it out as we go... โ™Ÿ

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linkhttp://seankavanagh.com calendar_today30-06-2019 11:52:11

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Seรกn Kavanagh
@kavanagh_sean_

7 months ago

Machine learning can be powerful for understanding defects, but currently sufficient only in select cases. MLFFs (& geometric/electrostatic tools in doped) allow screening for challenging 'non-local' defect reconstructions (split vacancies) in all ICSD/MP solids, w/caveats ๐Ÿ”—๐Ÿ‘‡

Machine learning can be powerful for understanding defects, but currently sufficient only in select cases. MLFFs (& geometric/electrostatic tools in doped) allow screening for challenging 'non-local' defect reconstructions (split vacancies) in all ICSD/MP solids, w/caveats ๐Ÿ”—๐Ÿ‘‡