ArticleiScience2021
Using simulation to accelerate autonomous experimentation: A case study using mechanics.
Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Experimental mechanician for plate lattice metamaterial discovery.Nature communications · 2026Article
- Real-time experiment-theory closed-loop interaction for autonomous materials science.Science advances · 2025Article
- Science acceleration and accessibility with self-driving labs.Nature communications · 2025Review
- ASMI: An automated, low-cost indenter for soft matter.HardwareX · 2024Article
- Foams with 3D Spatially Programmed Mechanics Enabled by Autonomous Active Learning on Viscous Thread Printing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Accelerating the design of lattice structures using machine learning.Scientific reports · 2024Article
- Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership.Nature communications · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Autonomous experimentation (AE) accelerates research by combining automation and machine learning to perform experiments intelligently and rapidly in a sequential fashion. While AE systems are most needed to study properties that cannot be predicted analytically or computationally, even imperfect predictions can in principle be useful. Here, we investigate whether imperfect data from simulation can accelerate AE using a case study on the mechanics of additively manufactured structures. Initially, we study resilience, a property that is well-predicted by finite element analysis (FEA), and find that FEA can be used to build a Bayesian prior and experimental data can be integrated using discrepancy modeling to reduce the number of needed experiments ten-fold. Next, we study toughness, a property not well-predicted by FEA and find that FEA can still improve learning by transforming experimental data and guiding experiment selection. These results highlight multiple ways that simulation can improve AE through transfer learning.
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Registered trials
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