Learning Chemical Intuition: From Molecular Models to Neural Networks

This article examines how both chemists and neural networks learn to predict chemical reactivity through different yet complementary frameworks, exploring how to effectively combine human chemical intuition with machine learning.

Publication Details

Journal
Repository for Publications and Research Data (ETH Zurich)
Published
2025-03-26
DOI
https://doi.org/10.3929/ethz-b-000730285
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Learning Chemical Intuition: From Molecular Models to Neural Networks

Repository for Publications and Research Data (ETH Zurich)
Machine Learning in Materials Science
article

Learning Chemical Intuition: From Molecular Models to Neural Networks

article en

Abstract

This article examines how both chemists and neural networks learn to predict chemical reactivity through different yet complementary frameworks, exploring how to effectively combine human chemical intuition with machine learning.

Repository for Publications and Research Data (ETH Zurich)
Quality Education
Openalex Percentile: Top 64%
Machine Learning in Materials Science
DOIOpen Access PDFOpenAlex
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Learning Chemical Intuition: From Molecular Models to Neural Networks · Repository for Publications and Research Data (ETH Zurich) (2025) | TGRS Research Map | TGRS