A certificate-based regret analysis framework for guided-diffusion black-box optimization is introduced, with mass lift as the central quantity explaining convergence from pretrained generators.
Guiding generative models to uncover diverse and novel crystals via reinforcement learning
4 Pith papers cite this work. Polarity classification is still indexing.
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81-92% of chemically valid and metastable crystals from generative models are training duplicates or substitution-derived, with low-symmetry cases showing interpolation and high-symmetry cases showing memorization.
MatMind is a unified LLM-based generative model for crystals that reports lowest MAE on energy above hull, bulk modulus and band gap while achieving 65.3% S.U.N. rate on unconditional generation.
Crys-JEPA introduces a joint embedding predictive architecture that creates an energy-aware latent space, enabling embedding-based stability screening and a refinement pipeline that yields up to 72.7% gains on the V.S.U.N. metric for crystal generation.
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MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science
MatMind is a unified LLM-based generative model for crystals that reports lowest MAE on energy above hull, bulk modulus and band gap while achieving 65.3% S.U.N. rate on unconditional generation.