Liming Wu

DMFlow: Disordered Materials Generation by Flow Matching

Liming Wu, Rui Jiao, Q Li, Mingze Li et autres

The design of materials with tailored properties is crucial for technological progress. However, most deep generative models focus exclusively on perfectly ordered crystals, neglecting the important class of disordered materials. To address this gap, we introduce DMFlow, a generative framework specifically designed …

cn (code pays fourni par la source)

One Path to Model Them All: Learnable-Time Flow Matching for Crystal Structure and Energy Prediction

Songyou Li, Mingze Li, Qianpu Liu, Jiacheng Cen et autres

Crystals are cornerstone materials for semiconductors and renewable energy, yet their discovery is hindered by the prohibitive cost of Density Functional Theory (DFT). While geometric graph neural networks have advanced Crystal Structure Prediction (CSP) and energy estimation, existing methods treat these tasks …

cn (code pays fourni par la source)

Deep reinforcement learning-based approach to tackle positive influence maximization in signed social networks

Nuan Song, Liming Wu, Tianqi Sun, Wei Si et autres

The Influence Maximization problem has garnered significant research interest since its introduction. In 2014, the problem was further extended to include signed social networks, resulting in the positive influence maximization problem and negative influence maximization problem. However, current solutions mainly rely on …

cn (code pays fourni par la source)

Parametric Assembly Skill Acquisition Method for Space Robots Utilizing Deep Reinforcement Learning

Liming Wu, Mingming Wang, Pudong Liu

cn (code pays fourni par la source)