Integer linear programming for unsupervised training set selection in molecular machine learning

Integer linear programming for unsupervised training set selection in molecular machine learning

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relevance of an ILP formulation to select molecul...

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Bibliographic Details
Main Authors: Matthieu Haeberle, Puck van Gerwen, Ruben Laplaza, Ksenia R Briling, Jan Weinreich, Friedrich Eisenbrand, Clémence Corminboeuf
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
integer linear programming
molecular machine learning
quantum chemistry
training set selection
Online Access:https://doi.org/10.1088/2632-2153/adcd38
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https://doi.org/10.1088/2632-2153/adcd38

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