graph neural networks

A game-level difficulty-aware curriculum learning framework for progressive graph neural network training in distribution network fault diagnosis

Abstract Graph neural networks (GNNs) have shown considerable potential for distribution network fault diagnosis; however, conventional empirical risk minimization with randomly ordered samples may lead to unstable convergence and substantial performance degradation when complex network topologies and severely corrupte...

Graph-Based Intelligent Prediction of Non-Linear Buckling Capacity for Single-Layer Spherical Gridshells under Topology-Constrained Imperfection Fields

Natural hazards such as snowstorms and earthquakes can readily trigger instability and collapse of single-layer gridshells (SLGs), making it essential to incorporate multiple factors in probability reliability analysis for structural stability design. To enhance computational efficiency in the probability analysis, thi...

Quantitative Structure–Activity Relationships for Predicting Organic Contaminant Reactivity in Electrochemical Advanced Oxidation Processes: Descriptors, Kinetics, and Machine Learning Frontiers

ABSTRACT Electrochemical advanced oxidation processes (EAOPs) have become a prominent tool in wastewater remediation of refractory organic pollutants due to their versatility and ability to generate powerful, in situ oxidant species—predominantly hydroxyl radicals (•OH). Concurrently, quantitative structure–activity re...

Framework of Spatiotemporal Traffic Flow Forecasting using Self-Evolving Graph Neural Networks: Public T_freeze Protocol and Reproducibility Package

This record is the public T_freeze research artifact for the PhD research project “Framework of Spatiotemporal Traffic Flow Forecasting using Self-Evolving Graph Neural Networks,” conducted by Shahzaib Asghar under the supervision of Husnain Saleem. It freezes the architecture, development-stage evaluation protocol, re...

Physically consistent and explainable crystal graph neural networks for hydrogen desorption enthalpy prediction in crystalline hydrides

Validation Trial Manuscript: Physics-Embedded Graph Neural Network (PEGNN) for Steady-State Thermal Conduction Field Reconstruction

THE EL-RAKHAWI PHYSICO-CHEMICAL ARTIFICIAL INTELLIGENCE FRAMEWORK: AI-DRIVEN DISCOVERY OF ECO-FRIENDLY CATALYSTS AND ADVANCED SUSTAINABLE MATERIALS

The El-Rakhawi Physico-Chemical AI (EPC-AI) Framework revolutionizes sustainable material discovery by transcending the limitations of trial-and-error experimentation and computationally prohibitive Density Functional Theory. Addressing industry's dependency on scarce, toxic rare-earth metals, EPC-AI integrates four no...

Chiral Cartograph3R: A Quick Filtering Framework for Stereochemical Enumeration Integrating Chemistry Rules, 3D-Aware SDF Atom Mapping, Chirality Validation, and Spectral Geometry Features

The Self-and-Mutual Model: A General Computational Framework for Adaptive Switching Between Autonomous and Interactive Modes

Computational systems are commonly designed around particular organizational assumptions. Some systems emphasize independent computation within individual components, whereas others rely on interaction, coordination, information exchange, or collective decision-making among multiple components. Although both modes are...

Gas-Conditioned Message Passing Graph Neural Network for Permeability Prediction in Polymeric Membranes

Designing polymeric membranes for diverse gas-separation applications remains a critical challenge. Machine learning (ML) models can significantly accelerate this effort by capturing complex relationships between polymer-gas pairs and enabling permeability prediction over an extended chemical space of gases and polymer...

SizeResGraph-Guided GFN2-xTB Screening of Low-Gap Thiophene-Containing Polymer Repeat Units

Abstract Low-gap conjugated polymers are widely studied for optoelectronic applications, but their vast repeat-unit space makes exhaustive quantum-chemical screening impractical. We developed SizeResGraph, a directed-bond graph neural network with a bounded size-dependent readout, to rank thiophene-containing repeat un...

Research on International Politics Combining Coverage Set Theory With Artificial Intelligence

International conflicts in the post-Cold War era are characterized by multi-dimensional coupling and rapid evolution, and it is difficult for traditional models to capture their high-order nonlinear mechanisms. This paper proposes a dual-loop dynamic modeling framework that integrates Transformer and graph neural netwo...