26 citations · 62 across the 15 of their papers we have counts for
18 papers
AnchorPose for Geometry-Aware MOF Assembly through Meso-Grained Pose Generation
Zhonglong Peng, Rui Jiao, Chang Chen +3
Predicting metal-organic framework (MOF) structures from given building blocks requires recovering their positions and orientations in a periodic crystal. The spatial effects of ro…
Generative crystallographic phasing through invariant relationships
Qi Li, Rui Jiao, Liming Wu +16
Crystal structure determination requires the phases of scattered waves -- yet diffraction measures only their intensities. Direct methods exploit phase invariants but become less r…
Scalable Peptide Design via Memory-Efficient Equivariant Transformer
Rui Jiao, Xiangzhe Kong, Yinjun Jia +4
Target-specific peptide design requires sequence and structure co-design under full atom geometric constraints. Latent generative frameworks offer an effective route for this probl…
Agentic Fusion of Large Atomic and Language Models to Accelerate Superconductor Discovery
Mingze Li, Yu Rong, Songyou Li +16
Artificial intelligence has accelerated materials discovery through high-throughput prediction and generation, yet the decision problem remains a formidable bottleneck. While curre…
Experimental Powder X-ray Diffraction Crystal Structure Determination with RealPXRD-Solver
Qi Li, Mingyu Guo, Rui Jiao +14
Determining crystal structures from experimental powder X-ray diffraction data remains challenging because peak overlap, preferred orientation, and impurity phases obscure atomic a…
DMFlow: Disordered Materials Generation by Flow Matching
Liming Wu, Rui Jiao, Qi Li +4
The design of materials with tailored properties is crucial for technological progress. However, most deep generative models focus exclusively on perfectly ordered crystals, neglec…
Equivariant Diffusion for Crystal Structure Prediction
Peijia Lin, Pin Chen, Rui Jiao +6
In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CS…
Learning 3D Anisotropic Noise Distributions Improves Molecular Force Field Modeling
Xixian Liu, Rui Jiao, Zhiyuan Liu +6
Coordinate denoising has emerged as a promising method for 3D molecular pretraining due to its theoretical connection to learning molecular force field. However, existing denoising…
Zero-Shot Cyclic Peptide Design via Composable Geometric Constraints
Dapeng Jiang, Xiangzhe Kong, Jiaqi Han +6
Cyclic peptides, characterized by geometric constraints absent in linear peptides, offer enhanced biochemical properties, presenting new opportunities to address unmet medical need…
UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
Xiangzhe Kong, Zishen Zhang, Ziting Zhang +5
The design of target-specific molecules such as small molecules, peptides, and antibodies is vital for biological research and drug discovery. Existing generative methods are restr…
Siamese Foundation Models for Crystal Structure Prediction
Liming Wu, Wenbing Huang, Rui Jiao +8
Predicting crystal structures from chemical compositions is a fundamental challenge in materials discovery, complicated by complex 3D geometries that distinguish it from fields lik…
Powder Diffraction Crystal Structure Determination Using Generative Models
Qi Li, Rui Jiao, Liming Wu +6
Accurate crystal structure determination is critical across all scientific disciplines involving crystalline materials. However, solving and refining inorganic crystal structures f…
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Jiaqi Han, Jiacheng Cen, Liming Wu +13
Geometric graphs are a special kind of graph with geometric features, which are vital to model many scientific problems. Unlike generic graphs, geometric graphs often exhibit physi…
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning
Rui Jiao, Xiangzhe Kong, Li Zhang +5
Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications. However, prior efforts typically focus on pretraining models i…
Space Group Constrained Crystal Generation
Rui Jiao, Wenbing Huang, Yu Liu +2
Crystals are the foundation of numerous scientific and industrial applications. While various learning-based approaches have been proposed for crystal generation, existing methods…
Crystal Structure Prediction by Joint Equivariant Diffusion
Rui Jiao, Wenbing Huang, Peijia Lin +4
Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While CSP can be addressed by employing currently-prevailing generative models (e.g. diffusion mode…
Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs
Rui Jiao, Jiaqi Han, Wenbing Huang +2
Pretraining molecular representation models without labels is fundamental to various applications. Conventional methods mainly process 2D molecular graphs and focus solely on 2D ta…
Alternated Training with Synthetic and Authentic Data for Neural Machine Translation
Rui Jiao, Zonghan Yang, Maosong Sun +1
While synthetic bilingual corpora have demonstrated their effectiveness in low-resource neural machine translation (NMT), adding more synthetic data often deteriorates translation…