ArticleACS central science2021
Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network.
Article in ACS central science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 74 citations in OpenAlex.
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- An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis.Communications chemistry · 2026Article
- Utilizing Similarity Measures to Map Chemical Reactivity.Angewandte Chemie (International ed. in English) · 2026Article
- Applications of quantitativeJournal of pharmaceutical analysis · 2026Review
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data.Nature communications · 2025Article
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- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Machine Learning Optimizing Enzyme/ZIF Biocomposites for Enhanced Encapsulation Efficiency and Bioactivity.JACS Au · 2024Article
- A robotic system for automated chemical synthesis of therapeutic agents.Materials advances · 2024Article
- Reaction Kinetics using a Chemputable Framework for Data Collection and Analysis.Angewandte Chemie (International ed. in English) · 2024Article
- Self-driving laboratories to autonomously navigate the protein fitness landscape.Nature chemical engineering · 2024Article
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- Robotic Modules for the Programmable Chemputation of Molecules and Materials.ACS central science · 2023Review
- Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit.Journal of chemical information and modeling · 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
- Article
- Assisted Self-Assembly to Target Heterometallic Mn-Nd and Mn-Sm SMMs: Synthesis and Magnetic Characterisation of [MnChemistry (Weinheim an der Bergstrasse, Germany) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a robotic chemical discovery system capable of navigating a chemical space based on a learned general association between molecular structures and reactivity, while incorporating a neural network model that can process data from online analytics and assess reactivity without knowing the identity of the reagents. Working in conjunction with this learned knowledge, our robotic platform is able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown chemical spaces, regardless of the identity of the starting materials. Through the system, we identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, and some unpredictable reactions that yielded new molecules. The validation of the system was done within a budget of 15 inputs combined in 1018 reactions, further analysis of which allowed us to discover not only a new photochemical reaction but also a new reactivity mode for a well-known reagent (
Identifiers
What OpenQuestion holds
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.