Can Machine Learning Find Extraordinary Materials
2019
One of the most common criticisms of machine learning is an
assumed inability for models to extrapolate, i.e. to identify extraordinary
materials with properties beyond those present in the training data set. To
investigate whether this is indeed the case, this work takes advantage of
density functional theory calculated properties (bulk modulus, shear modulus,
thermal conductivity, thermal expansion, band gap and Debye temperature) to
investigate whether machine learning is truly capable of predicting materials
with properties that extend beyond previously seen values. We refer to these
materials as extraordinary, meaning they represent the top 1% of values in the
available data set. Interestingly, we show that even when machine learning is
trained on a fraction of the bottom 99% we can consistently identify 3/4 of the
highest performing compositions for all considered properties with a precision
that is typically above 0.5. Moreover, we investigate a few different modeling
choices and demonstrate how a classification approach can identify an
equivalent amount of extraordinary compounds but with significantly fewer false
positives than a regression approach. Finally, we discuss cautions and
potential limitations in implementing such an approach to discover new
record-breaking materials.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
0
References
2
Citations
NaN
KQI