Outstanding Reviewers for Digital Discovery in 2023

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-09-06 DOI:10.1039/D4DD90037E
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Abstract

We would like to take this opportunity to thank all of Digital Discovery’s reviewers for helping to preserve quality and integrity in chemical science literature. We would also like to highlight the Outstanding Reviewers for Digital Discovery in 2023.

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2023 年数字发现杰出评审员
我们想借此机会感谢数字发现的所有审稿人,感谢他们帮助维护化学科学文献的质量和完整性。同时,我们还想特别介绍一下2023年数字发现的杰出审稿人。
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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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