Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

By Katja Hansen, Franziska Biegler, R. Ramakrishnan, Wiktor Pronobis, O. V. Lilienfeld, Klaus‐Robert Müller , Alexandre Tkatchenko. 2015

732 citations recorded by the source corpus.

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Physical and Theoretical Chemistry, Representation & Self-Supervised Learning, Computational Chemistry & Drug Discovery

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Citations in the corpus

Neural Message Passing for Quantum Chemistry (2017, methodology): “To date, most research applying machine learning to chemistry tasks (Hansen et al., 2015; Huang & von Lilienfeld, 2016; 1Google Brain 2Google 3Google DeepMind.”
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (2016, background): “Representations derived from the graph include chemical fingerprints, [17] convolutional networks on graphs [18] and similar graph-convolutions, [19] or the bag-of-bonds [20] approach.”
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials (2018, methodology): “…machine learning methods for predicting properties of molecules and materials have been based on hand crafted feature descriptors, such as Coulomb matrix (Rupp et al., 2012), bag-of-bonds (Hansen et al., 2015), ﬁngerprints (e.g. Rogers and Hahn 2010) and histogram of angles (Faber et al., 2017).”
Machine learning exciton dynamics (2015, unknown): “[42] Hansen, K, Biegler, F, Ramakrishnan, R, Pronobis, W, von Lilienfeld, O.”