Improved decision making with similarity based machine learning: applications in chemistry
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 s...
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Published in | Machine learning: science and technology Vol. 4; no. 4; pp. 45043 - 45056 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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Bristol
IOP Publishing
01.12.2023
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Abstract | 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. |
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AbstractList | 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. 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. |
Author | Falk von Rudorff, Guido Lemm, Dominik Anatole von Lilienfeld, O |
Author_xml | – sequence: 1 givenname: Dominik orcidid: 0000-0002-8075-1765 surname: Lemm fullname: Lemm, Dominik organization: University of Vienna, Vienna Doctoral School in Physics , Boltzmanngasse 5, AT-1090 Vienna, Austria – sequence: 2 givenname: Guido orcidid: 0000-0001-7987-4330 surname: Falk von Rudorff fullname: Falk von Rudorff, Guido organization: University of Vienna Faculty of Physics, Kolingasse 14-16, Vienna, AT-1090 Vienna, Austria – sequence: 3 givenname: O orcidid: 0000-0001-7419-0466 surname: Anatole von Lilienfeld fullname: Anatole von Lilienfeld, O organization: Machine Learning Group, Technische Universität Berlin and Institute for the Foundations of Learning and Data , 10587 Berlin, Germany |
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Despite the fundamental progress in autonomous molecular and materials discovery, data scarcity throughout chemical compound space still severely... Despite the fundamental progress in autonomous molecular and materials discovery, data scarcity throughout chemical compound space still severely hampers the... |
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SubjectTerms | Chemical compounds Chemical synthesis Decision making Harmonic oscillators local learning Machine learning Model accuracy Quantum mechanics Similarity |
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Title | Improved decision making with similarity based machine learning: applications in chemistry |
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