ArticleScientific reports2016
Adaptive Strategies for Materials Design using Uncertainties.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 224 citations in OpenAlex.
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- Accelerated discovery of high-performance Al-Si-Mg-Sc casting alloys by integrating active learning with high-throughput CALPHAD calculations.Science and technology of advanced materials · 2023Article
- Uncertainty-aware mixed-variable machine learning for materials design.Scientific reports · 2022Article
- A universal similarity based approach for predictive uncertainty quantification in materials science.Scientific reports · 2022Article
- Data-Driven Methods for Accelerating Polymer Design.ACS polymers Au · 2022Review
- Sample-efficient parameter exploration of the powder film drying process using experiment-based Bayesian optimization.Scientific reports · 2022Article
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- Advances in Kriging-Based Autonomous X-Ray Scattering Experiments.Scientific reports · 2020Article
- Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges.Polymers · 2020Review
- Detecting stable adsorbates of (1Beilstein journal of nanotechnology · 2020Article
- Accelerated Search for BaTiOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2019Article
- A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering.Scientific reports · 2019Article
- Bayesian optimization for conformer generation.Journal of cheminformatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We compare several adaptive design strategies using a data set of 223 M2AX family of compounds for which the elastic properties [bulk (B), shear (G), and Young's (E) modulus] have been computed using density functional theory. The design strategies are decomposed into an iterative loop with two main steps: machine learning is used to train a regressor that predicts elastic properties in terms of elementary orbital radii of the individual components of the materials; and a selector uses these predictions and their uncertainties to choose the next material to investigate. The ultimate goal is to obtain a material with desired elastic properties in as few iterations as possible. We examine how the choice of data set size, regressor and selector impact the design. We find that selectors that use information about the prediction uncertainty outperform those that don't. Our work is a step in illustrating how adaptive design tools can guide the search for new materials with desired properties.
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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.