ArticleChemical science2020
Pushing property limits in materials discovery
Article in Chemical science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed.
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Large language models open new way of AI-assisted molecule design for chemists.Journal of cheminformatics · 2025Article
- Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science.Scientific reports · 2024Article
- Clover: An unbiased method for prioritizing differentially expressed genes using a data-driven approach.Genes to cells : devoted to molecular & cellular mechanisms · 2024Article
- Theoretical and data-driven approaches to semiconductors and dielectrics: from prediction to experiment.Science and technology of advanced materials · 2024Review
- The rise of automated curiosity-driven discoveries in chemistry.Chemical science · 2023Review
- QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization.Journal of chemical information and modeling · 2022Article
- Selecting molecules with diverse structures and properties by maximizing submodular functions of descriptors learned with graph neural networks.Scientific reports · 2022Article
- Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring.Science and technology of advanced materials · 2022Article
- Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery.Advanced materials (Deerfield Beach, Fla.) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Materials chemists develop chemical compounds to meet often conflicting demands of industrial applications. This process may not be properly modeled by black-box optimization because the target property is not well defined in some cases. Herein, we propose a new algorithm for automated materials discovery called BoundLess Objective-free eXploration (BLOX) that uses a novel criterion based on kernel-based Stein discrepancy in the property space. Unlike other objective-free exploration methods, a boundary for the materials properties is not needed; hence, BLOX is suitable for open-ended scientific endeavors. We demonstrate the effectiveness of BLOX by finding light-absorbing molecules from a drug database. Our goal is to minimize the number of density functional theory calculations required to discover out-of-trend compounds in the intensity-wavelength property space. Using absorption spectroscopy, we experimentally verified that eight compounds identified as outstanding exhibit the expected optical properties. Our results show that BLOX is useful for chemical repurposing, and we expect this search method to have numerous applications in various scientific disciplines.
Identifiers
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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.