ArticleChemical science2025
Machine learning workflows beyond linear models in low-data regimes.
Article in Chemical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Transforming Molecular Synthesis With Large Language Models.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Review
- Stereoselective Generalizations over Diverse Sets of Chiral Acids Enabled by Buried Volume.Journal of the American Chemical Society · 2026Article
- Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices.ACS catalysis · 2026Article
- Effect of Molecular Descriptors on the Development of Machine Learning Models for the Prediction of Yield Sooting Index.ACS omega · 2025Article
- Classifying metal passivity from EIS using interpretable machine learning with minimal data.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data-driven methodologies are transforming chemical research by providing chemists with digital tools that accelerate discovery and promote sustainability. In this context, non-linear machine learning algorithms are among the most disruptive technologies in the field and have proven effective for handling large datasets. However, in data-limited scenarios, linear regression has traditionally prevailed due to its simplicity and robustness, while non-linear models have been met with skepticism over concerns related to interpretability and overfitting. In this study, we introduce ready-to-use, automated workflows designed to overcome these challenges. These frameworks mitigate overfitting through Bayesian hyperparameter optimization by incorporating an objective function that accounts for overfitting in both interpolation and extrapolation. Benchmarking on eight diverse chemical datasets, ranging from 18 to 44 data points, demonstrates that when properly tuned and regularized, non-linear models can perform on par with or outperform linear regression. Furthermore, interpretability assessments and
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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.