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VortexChat is an LLM-based agentic framework that automates the inverse design of integrated photonic devices from natural language specifications, demonstrated by autonomously fabricating a terahertz multiplexer with high performance.
The article highlights LLMs' lack of innate physical world understanding and introduces a Fourier Neural Operator-based framework that accelerates quantum dynamics prediction by 10^7 times, enabling efficient inverse design of quantum control protocols with improved success rates.
This paper presents a conditional catalyst generative model based on GPT architecture, pretrained on 133 million catalyst structures, achieving 98% structural validity and enabling controllable inverse design for targeted properties such as binding energy.
This paper presents a range-aware Bayesian optimization framework that directly scores the posterior probability that a candidate satisfies a target property range, enabling discovery of diverse valid designs across multiple specifications.
Researchers from MIT present a methodology for inverse design of nuclear critical experiments using deep neural networks with a novel multigroup attention pooling architecture and gradient-based optimization to maximize neutronic similarity coefficients. The approach is applied to validate a HALEU fuel transportation cask, achieving high similarity scores for three configurations of interest.
PolyFusionAgent is a framework that combines a multimodal polymer foundation model (PolyFusion) with a tool-augmented, literature-grounded design agent (PolyAgent) for polymer property prediction and inverse design, enabling evidence-linked discovery.
Proposes CoMole, a controllable molecular generative foundation model using motif-aware graph diffusion and reinforcement learning, achieving superior controllability across materials and drug discovery benchmarks.
This paper introduces RL-Kirigami, a framework combining optimal-transport conditional flow matching and reinforcement learning to solve the inverse design problem for kirigami metamaterials, achieving high accuracy and enabling rapid laser-cut prototype fabrication.