#inverse-design News & Analysis

6 articles tagged with #inverse-design. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · Jun 97/10

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A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design

Researchers have created a large-scale database of 160,000 aligned nanocrystal synthesis-property entries using AI, enabling generative inverse design for materials discovery. The system successfully predicts viable synthesis routes for both established and novel nanocrystals, including counter-intuitive formulations validated experimentally, demonstrating AI's potential to accelerate materials science beyond traditional trial-and-error methods.

AINeutralarXiv – CS AI · Jun 256/10

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Gradient-based inverse lithography for EUV masks via the waveguide method and a physics-informed neural operator

Researchers present a novel gradient-based inverse lithography technology (ILT) for extreme ultraviolet (EUV) masks that uses physics-informed neural operators and automatic differentiation to optimize mask absorber permittivity. The method combines a differentiable waveguide approach with waveguide neural operators (WGNO) to recover mask structures achieving desired field patterns on wafers, demonstrated on realistic 2D and 3D absorbers at 11.2 nm wavelengths.

AINeutralarXiv – CS AI · Jun 96/10

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Physics-Guided Sequence-Based Generative Framework for Acoustic Metamaterial Inverse Design

Researchers introduce MetaSeq, a physics-guided generative framework that uses sequence-based representations to design acoustic metamaterials with broadband responses. The approach reduces design errors by 45% compared to existing methods by combining machine learning with physics-based validation, addressing a long-standing challenge in materials engineering where structures optimized for one frequency often fail at others.

AIBullisharXiv – CS AI · Jun 96/10

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CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials

CatalyticMLLM presents a unified graph-text multimodal large language model that integrates property prediction and inverse structural design for catalytic materials within a single framework. This approach overcomes limitations of traditional decoupled systems by eliminating representation space inconsistencies and evaluator bias, enabling more stable closed-loop optimization workflows for materials discovery.

AINeutralarXiv – CS AI · May 276/10

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PolyFusionAgent: A Multimodal Foundation Model and Autonomous AI Assistant for Polymer Property Prediction and Inverse Design

Researchers introduce PolyFusionAgent, a multimodal AI framework combining a foundation model (PolyFusion) with an autonomous design agent (PolyAgent) for polymer discovery. The system integrates multiple polymer representations into a shared latent space to predict properties and generate novel structures, while grounding predictions in scientific literature for actionable design decisions.

AINeutralarXiv – CS AI · May 276/10

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MatFormBench: A Benchmarking Evaluation Framework for Target-Driven Materials Formulation

Researchers introduce MatFormBench, a comprehensive benchmarking framework designed to evaluate inverse design algorithms for materials formulation—addressing a critical gap in machine learning benchmarks that previously focused only on forward property prediction. The framework tests 39 diverse algorithms across 1,170 evaluations, revealing that diffusion-based models achieve superior overall performance, while VAE and genetic algorithm approaches excel in specific scenarios.