Bias-Free Chemically Diverse Test Sets from Machine Learning
Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal analysis and K-means clustering have previously been used to summarize large sets of nanoparticles however molecules are more diverse and not as easily characterized by descriptors. In this work, we compare three sets of descriptors based on the one-,...[Show more]
|Collections||ANU Research Publications|
|Source:||ACS combinatorial science|
|Access Rights:||Open Access|
|ProtoArch_CCCBDB new.pdf||565.05 kB||Adobe PDF|
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