30 citations · 60 across the 13 of their papers we have counts for
13 papers
MakoXC: Rearchitecting DFT Exchange-Correlation with Matrix-Aligned and Knowledge-Organized Sparsity
Haozhi Han, Fusong Ju, Jing Bai +8
Density Functional Theory (DFT) is indispensable for materials science and drug discovery, yet the exchange--correlation (XC) evaluation remains a major bottleneck due to its cubic…
LLM4LLM: Bridging Kernel Benchmarks and Real Deployment via Closed-Loop Agentic Optimization
Hui Zeng, Pengfei Yang, Yanxin Chen +2
Large language models have become increasingly capable agents for low-level code and kernel optimization, but isolated kernel benchmarks provide only a proxy for the deployment beh…
Accelerating Locality-Driven Integration in Quantum Chemistry with Block-Structured Matrix Multiplication
Xinran Wei, Yan Pan, Fusong Ju +8
Locality-driven integration is a pervasive computational pattern in quantum chemistry, arising whenever spatially localized basis functions interact through numerical quadrature or…
FusionRCG: Orchestrating Recursive Computation Graphs across GPU Memory Hierarchies
Yihong Zhang, Xinran Wei, Junshi Chen +4
Evaluating high-dimensional integrals via deep hierarchical recurrences is a dominant cost in quantum chemistry. While CPUs manage these efficiently, GPUs suffer a critical mismatc…
GPU Accelerated Minimal Auxiliary Basis Approach TDDFT for Large Organic Molecules
Zehao Zhou, Xiaojie Wu, Yanheng Li +5
We introduce a GPU-accelerated implementation of time-dependent density functional theory with the minimal auxiliary basis approach (TDDFT-risp) in GPU4PySCF, together with large s…
Matryoshka: Optimization of Dynamic Diverse Quantum Chemistry Systems via Elastic Parallelism Transformation
Tuowei Wang, Kun Li, Donglin Bai +6
AI infrastructures, predominantly GPUs, have delivered remarkable performance gains for deep learning. Conversely, scientific computing, exemplified by quantum chemistry systems, s…
Acceleration without Disruption: DFT Software as a Service
Fusong Ju, Xinran Wei, Lin Huang +13
Density functional theory (DFT) has been a cornerstone in computational chemistry, physics, and materials science for decades, benefiting from advancements in computational power a…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
Shuxin Zheng, Jiyan He, Chang Liu +15
Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not fun…
Exploring evolution-aware & -free protein language models as protein function predictors
Mingyang Hu, Fajie Yuan, Kevin K. Yang +5
Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions. In particula…
Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model
Liang He, Shizhuo Zhang, Lijun Wu +10
Understanding protein sequences is vital and urgent for biology, healthcare, and medicine. Labeling approaches are expensive yet time-consuming, while the amount of unlabeled data…
Seq-SetNet: Exploring Sequence Sets for Inferring Structures
Fusong Ju, Jianwei Zhu, Guozheng Wei +3
Sequence set is a widely-used type of data source in a large variety of fields. A typical example is protein structure prediction, which takes an multiple sequence alignment (MSA)…
Predicting protein inter-residue contacts using composite likelihood maximization and deep learning
Haicang Zhang, Qi Zhang, Fusong Ju +7
Accurate prediction of inter-residue contacts of a protein is important to calcu- lating its tertiary structure. Analysis of co-evolutionary events among residues has been proved e…