Improved decision making with similarity based machine learning: applications in chemistry

• Published in: Machine learning: science and technology Vol. 4; no. 4; pp. 45043 - 45056
• Main Authors: Lemm, Dominik, Falk von Rudorff, Guido, Anatole von Lilienfeld, O
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.12.2023
• Subjects: Chemical compounds; Chemical synthesis; Decision making; Harmonic oscillators; local learning; Machine learning; Model accuracy; Quantum mechanics; Similarity
• ISSN: 2632-2153; 2632-2153
• DOI: 10.1088/2632-2153/ad0fa3

Abstract Despite the fundamental progress in autonomous molecular and materials discovery, data scarcity throughout chemical compound space still severely hampers the use of modern ready-made machine learning models as they rely heavily on the paradigm, ‘the bigger the data the better’. Presenting similarity based machine learning (SML), we show an approach to select data and train a model on-the-fly for specific queries, enabling decision making in data scarce scenarios in chemistry. By solely relying on query and training data proximity to choose training points, only a fraction of data is necessary to converge to competitive performance. After introducing SML for the harmonic oscillator and the Rosenbrock function, we describe applications to scarce data scenarios in chemistry which include quantum mechanics based molecular design and organic synthesis planning. Finally, we derive a relationship between the intrinsic dimensionality and volume of feature space, governing the overall model accuracy.