The "DNA" of chemistry: Scalable quantum machine learning with "amons"

By Bing Huang, O. V. Lilienfeld. 2017

38 citations recorded by the source corpus.

Given sufficient examples, recently introduced machine learning models enable rapid, yet accurate, predictions of properties of new molecules. Extrapolation to larger molecules with differing composition is prohibitive due to all the specific chemistries which would be required for training. We address this problem by exploiting redundancies due to chemical similarity of repeating building blocks each represented by an effective {\underline a}tom in {\underline m}olecule: The "am-on". In analogy to the DNA sequence in a gene encoding its function, constituting amons encode a query molecule's properties. The use of amons affords highly accurate machine learning predictions of quantum properties of arbitrary query molecules in real time. We investigate this approach for predicting energies of various covalently and non-covalently bonded systems. After training on the few amons detected, very low prediction errors can be reached, on par with experimental uncertainty. Systems studied include two dozen large biomolecules, eleven thousand medium sized organic molecules, large common polymers, water clusters, doped $h$BN sheets, bulk silicon, and Watson-Crick DNA base pairs. Conceptually, the amons extend Mendeleev's table to account for the chemical environments of elements. They represent an important stepping stone to machine learning based virtual chemical space exploration campaigns.

Subfields: Materials Chemistry, Physical and Theoretical Chemistry, Spectroscopy

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Citations in the corpus

Exploring chemical compound space with quantum-based machine learning (2019, background): “Amon stands for an Atom in a Molecule, and the suffix on indicates that it can be used as a building-block dictionary entry which is selected whenever relevant in the ensemble of constituting fragments of any larger query molecule [12].”
Inverse molecular design using machine learning: Generative models for matter engineering (2018, background): “Other representations are better suited to prediction and could be rendered invertible via lookup tables: bag of bonds (61), amons (62), fingerprints (63,64), electronic density (51), symmetry functions (65), and chemical environments (50).”