Publications (6)
Improving Subgraph Representation Learning via Multi-View Augmentation
Yili Shen, Xiao Liu, Cheng-Wei Ju +4
Subgraph representation learning based on Graph Neural Network (GNN) has exhibited broad applications in scientific advancements, such as predictions of molecular structure-propert…
Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces
Siqi Chen, Zhiqiang Wang, Xianqi Deng +8
Rational design of next-generation functional materials relied on quantitative predictions of their electronic structures beyond single building blocks. First-principles quantum me…
Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory
Siqi Chen, Zhiqiang Wang, Yili Shen +8
Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks. Howeve…
Quantifying Bounded Rationality: Formal Verification of Simon's Satisficing Through Flexible Stochastic Dominance
Jingyuan Li, Zhou Lin
This paper introduces Flexible First-Order Stochastic Dominance (FFSD), a mathematically rigorous framework that formalizes Herbert Simon's concept of bounded rationality using the…
Triplet-Tuning: A Novel Family of Non-Empirical Exchange-Correlation Functionals
Zhou Lin, Troy Van Voorhis
In the framework of DFT, the lowest triplet excited state, T, can be evaluated using multiple formulations, the most straightforward of which are UDFT and TDDFT. Assuming the e…
Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads
Dharma Shukla, Muthian Sivathanu, Srinidhi Viswanatha +23
Lowering costs by driving high utilization across deep learning workloads is a crucial lever for cloud providers. We present Singularity, Microsoft's globally distributed schedulin…