ArticleScientific reports2018
Multi-objective Optimization for Materials Discovery via Adaptive Design.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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Who cites it
17 citing papers in PubMed, 164 citations in OpenAlex.
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- A Problem-Setup-Centric Framework for Mixed-Variable Chemical Formulation Optimization with Customized Constraints.Materials (Basel, Switzerland) · 2026Article
- High temperature Nb-Si alloys using data science: optimization of fracture toughness and high-temperature strength.Nature communications · 2026Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery.Patterns (New York, N.Y.) · 2023Review
- Computer-aided multi-objective optimization in small molecule discovery.Patterns (New York, N.Y.) · 2023Review
- 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
- Article
- 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
- Machine-Learning-Guided Discovery ofJournal of the American Chemical Society · 2021Article
- Article
- Investigating Active Learning and Meta-Learning for Iterative Peptide Design.Journal of chemical information and modeling · 2021Article
- An adaptive design approach for defects distribution modeling in materials from first-principle calculations.Journal of molecular modeling · 2020Article
- Pushing property limits in materials discoveryChemical science · 2020Article
- Efficient Bayesian Function Optimization of Evolving Material Manufacturing Processes.ACS omega · 2019Article
- Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics.MRS communications · 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
Guiding experiments to find materials with targeted properties is a crucial aspect of materials discovery and design, and typically multiple properties, which often compete, are involved. In the case of two properties, new compounds are sought that will provide improvement to existing data points lying on the Pareto front (PF) in as few experiments or calculations as possible. Here we address this problem by using the concept and methods of optimal learning to determine their suitability and performance on three materials data sets; an experimental data set of over 100 shape memory alloys, a data set of 223 M
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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.