摘要
A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data.While prior examples have demonstrated successful models for some applications,many more applications exist where machine learning can make a strong impact.To enable faster development of machine-learning-based models for such applications,we have created a framework capable of being applied to a broad range of materials data.Our method works by using a chemically diverse list of attributes,which we demonstrate are suitable for describing a wide variety of properties,and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy.In this manuscript,we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials,such as band gap energy and glass-forming ability.
基金
supported in part by the following grants:DARPA SIMPLEX award N66001-15-C-4036
NSF awards IIS-1343639
CCF-1409601
DOE award DESC0007456
AFOSR award FA9550-12-1-0458
supported by the Department of Defense(DoD)through the National Defense Science&Engineering Graduate Fellowship(NDSEG)Program.