ArticleScience advances2022
Inverse design of soft materials via a deep learning-based evolutionary strategy.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Photonic biosensors based on nanoparticle superstructures: from data analysis to artificial intelligence (AI) detection.Fundamental research · 2026Review
- Metadensity Functional Learning for Classical Fluids: Regularizing with Pair Correlations.The journal of physical chemistry. B · 2026Article
- Dynamical universality and vibrational divergence in 2D supercooled liquids, quasicrystals, and crystals.Soft matter · 2026Article
- Rapid and high-quality formation of dodecagonal quasicrystals and their approximants using a purely mechanical approach.National science review · 2026Article
- Research Progress in Artificial Intelligence-Assisted Preparation of High-Quality Biomaterials.ACS omega · 2026Review
- Learning nature's assembly language with polymers.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Near real-time full-wave inverse design of electromagnetic devices.Nature communications · 2026Article
- Machine-Assisted Inverse Design of Patchy Particles for Self-Assembly of Archimedean Tilings.ACS nano · 2025Article
- Encoding hierarchical 3D architecture through inverse design of programmable bonds.Nature materials · 2025Article
- Pushing the Frontiers: Artificial Intelligence (AI)-Guided Programmable Concepts in Binary Self-Assembly of Colloidal Nanoparticles.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Inverse Design Method with Enhanced Sampling for Complex Open Crystals: Application to Novel Zeolite Self-assembly.ACS nano · 2025Article
- Fast Reverse Design of 4D-Printed Voxelized Composite Structures Using Deep Learning and Evolutionary Algorithm.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Investigating the effect of textural properties on COScientific reports · 2023Article
- Learning to learn by using nonequilibrium training protocols for adaptable materials.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices.Science and technology of advanced materials · 2023Article
- Predicting stress, strain and deformation fields in materials and structures with graph neural networks.Scientific reports · 2022Article
- Curiosity-driven search for novel nonequilibrium behaviors.Physical review researchArticle
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colloidal self-assembly—the spontaneous organization of colloids into ordered structures—has been considered key to produce next-generation materials. However, the present-day staggering variety of colloidal building blocks and the limitless number of thermodynamic conditions make a systematic exploration intractable. The true challenge in this field is to turn this logic around and to develop a robust, versatile algorithm to inverse design colloids that self-assemble into a target structure. Here, we introduce a generic inverse design method to efficiently reverse-engineer crystals, quasicrystals, and liquid crystals by targeting their diffraction patterns. Our algorithm relies on the synergetic use of an evolutionary strategy for parameter optimization, and a convolutional neural network as an order parameter, and provides a way forward for the inverse design of experimentally feasible colloidal interactions, specifically optimized to stabilize the desired structure.
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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.