Untitled research

# > OpenAlex Metadata Hub · https://openalex.org/W2964121744 ## Bibliographic - **DOI:** 10.4230/lipics.cp.2021.42 - **Year:** 2021 - **Citations:** 50364 - **Open Access:** Yes (green) - **License:** cc-by - **Source:** https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.42 ## Authors - Mandi, Jayanta - Canoy, Rocsildes - Bucarey, Víctor - Guns, Tias ## Abstract Designing complex, dynamic yet multi-functional materials and devices is challenging because the design spaces for these materials have numerous interdependent and often conflicting constraints. Taking inspiration from advances in artificial intelligence and their applications in material discovery, we propose a computational method for designing metamorphic DNA-co-polymerized hydrogel structures. The method consists of a coarse-grained simulation and a deep learning-guided optimization system for exploring the immense design space of these structures. Here, we develop a simple numeric simulation of DNA-co-polymerized hydrogel shape change and seek to find designs for structured hydrogels that can fold into the shapes of different Arabic numerals in different actuation states. We train a convolutional neural network to classify and score the geometric outputs of the coarse-grained simulation to provide autonomous feedback for design optimization. We then construct a genetic algorithm that generates and selects large batches of material designs that compete with one another to evolve and converge on optimal objective-matching designs. We show that we are able to explore the large design space and learn important parameters and traits. We identify vital relationships between the material scale size and the range of shape change that can be achieved by individual domains and we elucidate trade-offs between different design parameters. Finally, we discover material designs capable of transforming into multiple different digits in different actuation states. ## Keywords Regret, Computer science, Mathematical optimization, Diagonal, Stochastic optimization, Convergence (economics), Rate of convergence, Optimization problem, Mathematics, Key (lock) ## Concepts - Regret - Computer science - Mathematical optimization - Diagonal - Stochastic optimization - Convergence (economics) - Rate of convergence - Optimization problem - Mathematics - Key (lock) - Machine learning - Economics - Economic growth - Computer security - Geometry --- *Metadata only — full text not imported unless Open Access license permits.*

Bài “Untitled research” được TradingBase chuyển thành Knowledge Product cho trader — không phải trang đọc abstract OpenAlex. Tóm lược học thuật (đã diễn giải): Designing complex, dynamic yet multi-functional materials and devices is challenging because the design spaces for these materials have numerous interdependent and often conflicting constraints. Taking inspiration from advances in artificial intelligence and their applications in material discovery, we propose a computational method for designing metamorphic DNA-co-polymerized hydrogel structures. The method consists of a coarse-grained simulation and a deep learning-guided optimization system for exploring the immense design space of these structures. Here, we develop a simple numeric simulation of DNA-co-polymerized hydrogel shape change and seek to find designs for structured hydrogels that can fold into the shapes of different Arabic numerals in different actuation states. We train a convolutional neural network to classify and score the geometric outputs of the coarse-grained simula… Phần Trading Insights bên dưới nối nghiên cứu với Forex, vàng, USD, lãi suất và risk regime — để bạn đưa vào journal và playbook. Metadata DOI/OA chỉ là rail tham chiếu; nội dung chính là summary, takeaways và ứng dụng thị trường do Content Factory sinh.