ArticleNature communications2017
An autonomous organic reaction search engine for chemical reactivity.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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The trial behind it
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Who cites it
24 citing papers in PubMed.
- Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes.Chemical science · 2024Article
- AI-driven robotic chemist for autonomous synthesis of organic molecules.Science advances · 2023Article
- Robotic Modules for the Programmable Chemputation of Molecules and Materials.ACS central 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
- Navigating with chemometrics and machine learning in chemistry.Artificial intelligence review · 2023Article
- An all-round AI-Chemist with a scientific mind.National science review · 2022Article
- The case for data science in experimental chemistry: examples and recommendations.Nature reviews. Chemistry · 2022Review
- Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network.ACS central science · 2021Article
- DeepReac+: deep active learning for quantitative modeling of organic chemical reactions.Chemical science · 2021Article
- Review
- A robotic prebiotic chemist probes long term reactions of complexifying mixtures.Nature communications · 2021Article
- Digitising chemical synthesis in automated and robotic flow.Chemical science · 2020Review
- Big-Data Science in Porous Materials: Materials Genomics and Machine Learning.Chemical reviews · 2020Review
- Autonomous intelligent agents for accelerated materials discovery.Chemical science · 2020Article
- A mobile robotic chemist.Nature · 2020Article
- Optical monitoring of polymerizations in droplets with high temporal dynamic range.Chemical science · 2020Article
- Benchmarking the acceleration of materials discovery by sequential learning.Chemical science · 2020Article
- Article
- Disposable cartridge concept for the on-demand synthesis of turbo Grignards, Knochel-Hauser amides, and magnesium alkoxides.Beilstein journal of organic chemistry · 2020Article
- Intuition-Enabled Machine Learning Beats the Competition When Joint Human-Robot Teams Perform Inorganic Chemical Experiments.Journal of chemical information and modeling · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The exploration of chemical space for new reactivity, reactions and molecules is limited by the need for separate work-up-separation steps searching for molecules rather than reactivity. Herein we present a system that can autonomously evaluate chemical reactivity within a network of 64 possible reaction combinations and aims for new reactivity, rather than a predefined set of targets. The robotic system combines chemical handling, in-line spectroscopy and real-time feedback and analysis with an algorithm that is able to distinguish and select the most reactive pathways, generating a reaction selection index (RSI) without need for separate work-up or purification steps. This allows the automatic navigation of a chemical network, leading to previously unreported molecules while needing only to do a fraction of the total possible reactions without any prior knowledge of the chemistry. We show the RSI correlates with reactivity and is able to search chemical space using the most reactive pathways.
Identifiers
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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.