ArticleOpen research Europe2023
scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science.
Article in Open research Europe, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Identifying the impact of chemical functional groups on ionic liquid conductivity.Chemical science · 2026Article
- How Long Is Long Enough? Extrapolation of Machine-Learning Interatomic Potentials for Oligomeric and Polymeric Systems.Journal of chemical theory and computation · 2026Article
- Article
- From genomes to interventions: computational strategies transforming parasitology.Frontiers in veterinary science · 2026Review
- Electronic Excited States from Physically Constrained Machine Learning.ACS central science · 2024Article
- Mechanism of Charge Transport in Lithium Thiophosphate.Chemistry of materials : a publication of the American Chemical Society · 2024Article
- The rule of four: anomalous distributions in the stoichiometries of inorganic compounds.npj computational materials · 2024Article
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Authors and funding
10 authors.
Funding
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Abstract
Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. While many of the algorithms implemented in these libraries originated in specific scientific fields, they have gained in popularity in part because of their generalisability across multiple domains. Over the past two decades, researchers in the chemical and materials science community have put forward general-purpose machine learning methods. The deployment of these methods into workflows of other domains, however, is often burdensome due to the entanglement with domain-specific functionalities. We present the python library scikit-matter that targets domain-agnostic implementations of methods developed in the computational chemical and materials science community, following the scikit-learn API and coding guidelines to promote usability and interoperability with existing workflows.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.