ArticleNature communications2021
Bias free multiobjective active learning for materials design and discovery.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 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
48 citing papers in PubMed.
- Artificial intelligence-assisted design of self-assembling peptide hydrogels for neural regeneration: Principles and opportunities.Bioactive materials · 2027Review
- Sequence-defined polymers for predictable gene delivery.Nature reviews. Chemistry · 2026Review
- Noise-Aware Machine Learning Accelerates Development of High-Latent-Heat Cu-Al-Ni Shape Memory Alloys for Thermal Management.Materials (Basel, Switzerland) · 2026Article
- De novo design of peptides localizing at the interface of biomolecular condensates.Nature communications · 2026Article
- Machine Learning-Assisted Bio-Interfacial Engineering Resolves Structural-Functional Conflicts in Nanocomposites.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Machine learning guided resolution of mechanical trade-off in polymer composites via stress adaptive interface.Nature communications · 2026Article
- Decoding active sites in high-entropy catalysts via attention-enhanced model.Science advances · 2026Article
- Deep learning methods for 2D material electronic properties.Digital discovery · 2026Review
- A unified active learning framework for photosensitizer design.Chemical science · 2026Article
- Programmable Light-Driven Color Tuning of Perovskite Quantum Dots.ACS central science · 2025Article
- Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.Gels (Basel, Switzerland) · 2025Review
- Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroups.Digital discovery · 2025Article
- Predicting heteropolymer phase separation using two-chain contact maps.The Journal of chemical physics · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.Bioactive materials · 2025Review
- Transfer learning Bayesian optimization for competitor DNA molecule design for use in diagnostic assays.Biotechnology and bioengineering · 2025Article
- Article
- Noise-Aware Active Learning to Develop High-Temperature Shape Memory Alloys with Large Latent Heat.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Automation and machine learning augmented by large language models in a catalysis study.Chemical science · 2024Review
- AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria.Nature communications · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work, we leverage an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and drastically reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.
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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.