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Found 6 papers, 1 papers with code
Scalable Diffusion for Materials Generation
Lastly, we show that conditional generation with UniMat can scale to previously established crystal datasets with up to millions of crystals structures, outperforming random structure search (the current leading method for structure discovery) in discovering new stable materials.
Predicting emergence of crystals from amorphous matter with deep learning
Crystallization of the amorphous phases into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to synthesis and development of new materials in the laboratory.
VeLO: Training Versatile Learned Optimizers by Scaling Up
While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers.
Learn2Hop: Learned Optimization on Rough Landscapes
Optimization of non-convex loss surfaces containing many local minima remains a critical problem in a variety of domains, including operations research, informatics, and material design.
Does Data Augmentation Benefit from Split BatchNorms
Data augmentation has emerged as a powerful technique for improving the performance of deep neural networks and led to state-of-the-art results in computer vision.
What Happens To BERT Embeddings During Fine-tuning?
While there has been much recent work studying how linguistic information is encoded in pre-trained sentence representations, comparatively little is understood about how these models change when adapted to solve downstream tasks.
