ArticleScience advances2020
A Bayesian experimental autonomous researcher for mechanical design.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 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
55 citing papers in PubMed.
- Roadmap collaborative research on structured magnetic elastomers and their material behavior-bridging from fundamental and materials science to educational science.The European physical journal. E, Soft matter · 2026Review
- The past, present and future of self-driving laboratories.Nature reviews. Chemistry · 2026Review
- Tunable Surface Chemistry of Molten Salt Derived MXenes for Data-Driven Electrochemical Materials Discovery.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- ASCENT: an active transfer learning paradigm for efficient drug-target interaction prediction.Science China. Life sciences · 2026Article
- Accelerated Discovery of 3D Printing Calcium Sulphoaluminate Cement Composites Using Data-Driven Multi-Objective Optimization.Materials (Basel, Switzerland) · 2026Article
- Breaking the Boundaries of Bayesian Optimization Utilizing Continuous Chemistry Digital Twins.Organic process research & development · 2026Article
- SPACESHIP: Autonomous Mapping of Hardware-Dependent Synthesizable Space in Solution-Phase Gold Nanomaterials.Journal of the American Chemical Society · 2026Article
- Accelerate Flash Removal of PFAS from Soil by Human-Guided Bayesian Optimization and Interpretable Machine Learning.ACS nano · 2026Article
- Accelerated Emergence of Self-Driving Laboratories for Accelerating Materials Discovery.ACS central science · 2026Review
- Experimental mechanician for plate lattice metamaterial discovery.Nature communications · 2026Article
- AI-driven robotic crystal explorer for rapid polymorph identification.Digital discovery · 2026Article
- PhaseTransfer: A transfer learning framework for efficient phase diagram mapping.npj computational materials · 2026Article
- Self-driving lab discovers principles for steering spontaneous emission beyond conventional Fourier optics.Nature communications · 2025Article
- Highly Efficient Discovery of 3D Mechanical Metamaterials via Monte Carlo Tree Search.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- PhaseXplorer Creates High-Dimensional Phase Diagrams with Closed-Loop Active Learning.ACS nano · 2025Article
- Autonomous Bayesian Optimization-Based Control System for Droplet Generation.Small methods · 2025Article
- Bayesian Optimization for Multicomponent Supramolecular Systems.Journal of the American Chemical Society · 2025Article
- Evolving Data-Driven Strategies for the Characterization of Supramolecular Polymers and Systems.Angewandte Chemie (International ed. in English) · 2025Review
- AutoSAS: A new human-aside-the-loop paradigm for automated SAS fitting for high throughput and autonomous experimentation.APL machine learning · 2025Article
- Real-time experiment-theory closed-loop interaction for autonomous materials science.Science advances · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While additive manufacturing (AM) has facilitated the production of complex structures, it has also highlighted the immense challenge inherent in identifying the optimum AM structure for a given application. Numerical methods are important tools for optimization, but experiment remains the gold standard for studying nonlinear, but critical, mechanical properties such as toughness. To address the vastness of AM design space and the need for experiment, we develop a Bayesian experimental autonomous researcher (BEAR) that combines Bayesian optimization and high-throughput automated experimentation. In addition to rapidly performing experiments, the BEAR leverages iterative experimentation by selecting experiments based on all available results. Using the BEAR, we explore the toughness of a parametric family of structures and observe an almost 60-fold reduction in the number of experiments needed to identify high-performing structures relative to a grid-based search. These results show the value of machine learning in experimental fields where data are sparse.
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.