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#materials-discovery News & Analysis

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

6 articles
AIBullisharXiv – CS AI · 6d ago7/10
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DGLD: Domain-Gated Latent Diffusion for the Discovery of Novel Energetic Materials

Researchers introduce Domain-Gated Latent Diffusion (DGLD), an AI method that discovered 12 novel energetic materials using generative diffusion models with quality-gated training and multi-task guidance. The breakthrough identified two lead compounds with performance metrics rivaling HMX-class materials for the first time in 15 years, validated through DFT simulations and released with open-source code.

AIBullisharXiv – CS AI · Apr 137/10
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EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers

EquiformerV3, an advanced SE(3)-equivariant graph neural network, achieves significant improvements in efficiency, expressivity, and generality for 3D atomistic modeling. The new version delivers 1.75x speedup, introduces architectural innovations like SwiGLU-S² activations and smooth-cutoff attention, and achieves state-of-the-art results on major molecular modeling benchmarks including OC20 and OMat24.

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AIBullisharXiv – CS AI · 4d ago6/10
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Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era

Researchers propose integrating artificial intelligence with metal-organic frameworks (MOFs) to accelerate the discovery of sustainable water harvesting materials for arid regions. By combining AI-driven design optimization with MOF chemistry principles, the approach promises faster development of high-performance atmospheric water capture systems with improved stability and scalability.

AINeutralarXiv – CS AI · May 116/10
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LLM-Guided Open Hypothesis Learning from Autonomous Scanning Probe Microscopy Experiments

Researchers have developed an open hypothesis-learning framework that combines symbolic regression with large language models to autonomously discover physical laws from scanning probe microscopy experiments. Rather than optimizing within predefined objectives, the system generates and evaluates candidate physical models directly from experimental data, demonstrating success in characterizing ferroelectric domain switching behavior.

AINeutralarXiv – CS AI · Mar 44/102
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Sustainable Materials Discovery in the Era of Artificial Intelligence

Researchers propose ML-LCA framework to integrate machine learning-based materials discovery with lifecycle assessment for sustainable-by-design materials. The framework addresses the current inefficiency where environmental impacts are evaluated only after resources are invested in potentially unsustainable solutions.