ArticleCommunications chemistry2021
Data-science driven autonomous process optimization.
Article in Communications chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
53 citing papers in PubMed, 223 citations in OpenAlex.
- AI-Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery.Medicinal research reviews · 2026Review
- Breaking the Boundaries of Bayesian Optimization Utilizing Continuous Chemistry Digital Twins.Organic process research & development · 2026Article
- Staged Diversity-Constrained Machine Learning for High-Dimensional Reaction Condition Optimization.Angewandte Chemie (International ed. in English) · 2026Article
- The ADePT framework for assessing autonomous laboratory robotics.Communications chemistry · 2026Review
- Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments.ACS nanoscience Au · 2026Article
- A Simple Microplate Assay for Accelerated Photocatalytic Activity Evaluation.ACS environmental Au · 2026Article
- Self-driving lab discovers principles for steering spontaneous emission beyond conventional Fourier optics.Nature communications · 2025Article
- Optimized Machine Learning for Autonomous Enzymatic Reaction Intensification in a Self-Driving Lab.Biotechnology and bioengineering · 2025Article
- Quantum Descriptor-Based Machine-Learning Modeling of Thermal Hazard of Cyclic Sulfamidates.Journal of chemical information and modeling · 2025Article
- Highly parallel optimisation of chemical reactions through automation and machine intelligence.Nature communications · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Deep Reinforcement Learning-Based Self-Optimization of Flow Chemistry.ACS engineering Au · 2025Article
- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
- IvoryOS: an interoperable web interface for orchestrating Python-based self-driving laboratories.Nature communications · 2025Article
- Carbohydrate Synthesis is Entering the Data-Driven Digital Era.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Review
- Applying statistical modeling strategies to sparse datasets in synthetic chemistry.Science advances · 2025Review
- Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning.Beilstein journal of organic chemistry · 2025Review
- Advancing Rare-Earth (4ACS sustainable chemistry & engineering · 2024Article
- Article
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 7 institutions in 3 countries.
Funding
Abstract
Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield.
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.