Accelerated Science: Difference between revisions

Machine learning can help accelerate materials science across a variety of climate change applications by learning patterns in experimental or operational data in order to guide future experiments/operations. Examples include:<ref>{{Cite webjournal|last=Rolnick|first=David|last2=Donti|first2=Priya L.|last3=Kaack|first3=Lynn H.|last4=Kochanski|first4=Kelly|last5=Lacoste|first5=Alexandre|last6=Sankaran|first6=Kris|last7=Ross|first7=Andrew Slavin|last8=Milojevic-Dupont|first8=Nikola|last9=Jaques|first9=Natasha|last10=Waldman-Brown|first10=Anna|last11=Luccioni|first11=Alexandra|date=2019-11-05|title=Tackling climateClimate changeChange inwith theMachine EULearning|url=http://dx.doiarxiv.org/10.1163abs/9789004322714_cclc_2017-0189-0051906.05433|websitejournal=Climate Change andarXiv:1906.05433 Law[cs, Collectionstat]}}</ref>