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8 papers published across 0 labs.
A hybrid approach that draws on the stability and generation quality of denoising diffusion models, which yields a stable training process and generates microstructures of varying complexity with minimal slice artefacts and close agreement with ground-truth phase fractions and structural descriptors.
TorchCraft is presented, a unified binder-design framework that optimizes sequence logits through a frozen all-atom predictor and extends predictor inversion to multiple binder formats and molecular contexts, providing a common framework for reusing all-atom structural priors in design.
This work highlights that diagnosing and factually augmenting data to long-tail deficits is essential for robust scientific retrieval, and presents REPAIR, a self-evolving data augmentation framework for scientific dense retrievers.
QALPA ("Quantum-Aware Learning for Property-space Augmentation"), a property-guided generative framework that combines an E(3)-equivariant diffusion model with active learning and efficient quantum-mechanical methods to iteratively explore targeted QM property manifolds, is introduced.
It is found that although most repurposed datasets do not achieve broad visibility in the short term, data repurposing is associated with greater disruption, and that its successful adoption is more common among larger teams and collaborations spanning academia and industry.
A hybrid approach that draws on the stability and generation quality of denoising diffusion models, which yields a stable training process and generates microstructures of varying complexity with minimal slice artefacts and close agreement with ground-truth phase fractions and structural descriptors.
TorchCraft is presented, a unified binder-design framework that optimizes sequence logits through a frozen all-atom predictor and extends predictor inversion to multiple binder formats and molecular contexts, providing a common framework for reusing all-atom structural priors in design.
This work highlights that diagnosing and factually augmenting data to long-tail deficits is essential for robust scientific retrieval, and presents REPAIR, a self-evolving data augmentation framework for scientific dense retrievers.
QALPA ("Quantum-Aware Learning for Property-space Augmentation"), a property-guided generative framework that combines an E(3)-equivariant diffusion model with active learning and efficient quantum-mechanical methods to iteratively explore targeted QM property manifolds, is introduced.
It is found that although most repurposed datasets do not achieve broad visibility in the short term, data repurposing is associated with greater disruption, and that its successful adoption is more common among larger teams and collaborations spanning academia and industry.
Results show that an AI scientist workflow can propose thermal structures, test them under common constraints, and quantify their electrical cost, and show that lower temperature can accompany higher thermal resistance when current falls.
SoupFold is introduced, which improves co-folding predictions by learning simple mappings between the representation spaces of co-folding models, and achieves state-of-the-art performance on both protein-protein and protein-ligand structure prediction.
The results support a staged screening design in which a pooled default is followed by ligand-specific calibration and expert review, and SCScore showed the highest association with the blinded consensus, but performance varied by ligand.