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.
Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations.Chem
3 Pith papers cite this work. Polarity classification is still indexing.
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Chirality emerges in SMILES translation models through an abrupt encoder-centered reorganization of representations after a long plateau, identified via checkpoint analysis and ablation.
A Gaussian mixture model is used to learn spectral densities from 2DES experiments, enabling extraction of vibronic couplings, spectral extrapolation, and optimized experiment selection across simulated and experimental systems.
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Streamlining Analysis and Design of Two-Dimensional Electronic Spectroscopy using Machine Learning
A Gaussian mixture model is used to learn spectral densities from 2DES experiments, enabling extraction of vibronic couplings, spectral extrapolation, and optimized experiment selection across simulated and experimental systems.