Bias-Free Chemically Diverse Test Sets from Machine Learning

Ellen T. Swann, Michael Fernandez, Michelle L. Coote, Amanda S. Barnard

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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-, two-, and three-dimensional structure of a molecule. Using data from the NIST Computational Chemistry Comparison and Benchmark Database and machine learning techniques, we demonstrate the functional relationship between these structural descriptors and the electronic energy of molecules. Archetypes and prototypes found with topological or Coulomb matrix descriptors can be used to identify smaller, statistically significant test sets that better capture the diversity of chemical space. We apply this same method to find a diverse subset of organic molecules to demonstrate how the methods can easily be reapplied to individual research projects. Finally, we use our bias-free test sets to assess the performance of density functional theory and quantum Monte Carlo methods.

Original languageEnglish
Pages (from-to)544-554
Number of pages11
JournalACS Combinatorial Science
Issue number8
Publication statusPublished - 14 Aug 2017
Externally publishedYes


  • Benchmarking
  • Bias-free test sets
  • Machine learning
  • Quantum chemistry


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