AI model DGLD discovers novel energetic materials with high performance

Researchers have developed Domain-Gated Latent Diffusion (DGLD), a novel AI approach for discovering energetic materials. DGLD addresses the challenge of limited labeled data by using a label-quality gate during training and multi-task guidance during sampling. This method has successfully identified 12 new compounds confirmed by first-principles DFT calculations, including two headline leads with high performance metrics. AI

IMPACT This AI approach could accelerate the discovery of new materials with significant applications in defense and civilian industries.

RANK_REASON The cluster describes a new AI method published in an arXiv paper for scientific discovery. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• 3,4,5-trinitro-1,2-isoxazole
• 4-nitro-1,2,3,5-oxatriazole
• DFT
• DGLD
• SELFIES-GA
• SMILES-LSTM
• Zenodo

• paper
• model release

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