Hugging Face Trending Papers
Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems
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arXiv AI
4d ago
Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems
arXiv:2610.08400v1 Announce Type: cross Abstract: Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to ge...
arXiv Machine Learning
Jun 2
Latent Diffusion Pretraining for Crystal Property Prediction
arXiv:2606. 00776v1 Announce Type: new Abstract: Fast and accurate prediction of crystal properties is a central challenge in new materials design.
Hugging Face Trending Papers
Sep 15
Robust and Efficient AI Frameworks for Scalable Material Design and Property Prediction
This thesis develops robust and efficient AI frameworks for accelerating crystalline materials discovery by addressing both major stages of the materials-design pipeline: crystal property prediction a...
arXiv Machine Learning
Jul 1
ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table
arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
arXiv AI
2d ago
Zatom-2: Multitask Pretraining on Atomistic Data for Generative Modeling across Domains
Zatom-2 is a multitask generative model for atomistic data that has been pretrained on about five million structures from the OMol25 and OMat24 datasets. It uses a multiscale Transformer with conditional flow matching to support tasks such as generation, structure prediction, and energy/force prediction for both molecules and materials. The model outperforms its predecessor, Zatom-1, on molecular distribution fidelity and benchmark generation tasks, and improves protein backbone designability from 67.8% to 74.8% after finetuning on 2,000 protein domains.
arXiv Machine Learning
Sep 17
Robust and Efficient AI Frameworks for Scalable Material Design and Property Prediction
The thesis presents AI frameworks that accelerate crystalline materials discovery by tackling both crystal property prediction and crystal structure generation. It introduces CrysXPP, CrysGNN, and CrysMMNet for efficient, data‑sparse property prediction using graph autoencoding, self‑supervised pretraining, and multimodal learning. For generation, TGDMat is a text‑guided diffusion model that jointly learns lattice parameters, atomic types, and coordinates, enabling valid, stable, and conditionally generated periodic materials.