DGLD applies domain-gated latent diffusion with label-quality gating and multi-task guidance to discover 12 novel energetic material leads validated by DFT, outperforming SMILES-LSTM, SELFIES-GA, and REINVENT baselines in novelty and on-target performance.
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NIMS-OS is an open-source Python framework that orchestrates AI modules (Bayesian optimization, phase diagram construction) with robotic hardware (NAREE) to enable autonomous closed-loop materials exploration.
High-fidelity multiphysics simulations of laser powder bed fusion melt pools match 2025 NIST experimental data across depth, width, bead height, overlap, and area metrics for varied powder heights and geometries.
Machine learning models that respect material symmetries are accelerating the identification of topological phases and the discovery of d-wave, g-wave, and i-wave altermagnets in quantum materials.
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Laser Powder Bed Fusion Melt Pool Dynamics for Different Geometric Variations and Powder Layer Heights: High-Fidelity Multiphysics Modeling vs 2025 NIST Experiments
High-fidelity multiphysics simulations of laser powder bed fusion melt pools match 2025 NIST experimental data across depth, width, bead height, overlap, and area metrics for varied powder heights and geometries.
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Machine Learning and Deep Learning in Quantum Materials: Symmetry, Topology, and the Rise of Altermagnets
Machine learning models that respect material symmetries are accelerating the identification of topological phases and the discovery of d-wave, g-wave, and i-wave altermagnets in quantum materials.