ArticleJournal of the American Chemical Society2022
Machine-Learning-Guided Discovery of Electrochemical Reactions.
Article in Journal of the American Chemical Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- An Automated Electrochemistry Platform for Accelerating the Characterization of Enzymatic Electrochemistry.ACS electrochemistry · 2026Article
- Machine Learning Optimization of Laser Ablation in Liquid for the Green and Low-Cost Synthesis of Clean Gold Nanoparticles.Journal of the American Chemical Society · 2026Article
- Review
- An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis.Communications chemistry · 2026Article
- Disordered Rocksalts as High-Energy and Earth-Abundant Li-Ion Cathodes.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Low-temperature molten-salt enabled synthesis of highly-efficient solid-state emitting carbon dots optimized using machine learning.Nature communications · 2025Article
- Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions.Angewandte Chemie (International ed. in English) · 2025Article
- Deductive machine learning models for product identification.Chemical science · 2024Article
- Paired Electrosynthesis at Interdigitated Microband Electrodes: Exploring Diffusion and Reaction Zones in the Absence of a Supporting Electrolyte.ACS measurement science au · 2024Article
- Machine learning-guided realization of full-color high-quantum-yield carbon quantum dots.Nature communications · 2024Article
- Deconvolution and Analysis of theJournal of chemical information and modeling · 2024Article
- Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation.Nature communications · 2024Article
- A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target.Chemical science · 2024Article
- Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit.Journal of chemical information and modeling · 2023Review
- Near-Infrared Spectroscopy and Machine Learning for Accurate Dating of Historical Books.Journal of the American Chemical Society · 2023Article
- Chemical Reaction Networks Explain Gas Evolution Mechanisms in Mg-Ion Batteries.Journal of the American Chemical Society · 2023Article
- A field guide to flow chemistry for synthetic organic chemists.Chemical science · 2023Review
- Digitizing chemical discovery with a Bayesian explorer for interpreting reactivity data.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The molecular structures synthesizable by organic chemists dictate the molecular functions they can create. The invention and development of chemical reactions are thus critical for chemists to access new and desirable functional molecules in all disciplines of organic chemistry. This work seeks to expedite the exploration of emerging areas of organic chemistry by devising a machine-learning-guided workflow for reaction discovery. Specifically, this study uses machine learning to predict competent electrochemical reactions. To this end, we first develop a molecular representation that enables the production of general models with limited training data. Next, we employ automated experimentation to test a large number of electrochemical reactions. These reactions are categorized as competent or incompetent mixtures, and a classification model was trained to predict reaction competency. This model is used to screen 38,865 potential reactions in silico, and the predictions are used to identify a number of reactions of synthetic or mechanistic interest, 80% of which are found to be competent. Additionally, we provide the predictions for the 38,865-member set in the hope of accelerating the development of this field. We envision that adopting a workflow such as this could enable the rapid development of many fields of chemistry.
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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.