AI for Computational Physical Science
Artificial intelligence is rapidly changing how physical systems are modeled, simulated, and understood. This Special Topic will highlight AI methods that advance theoretical and computational physical science, with emphasis on condensed matter, materials, chemical physics, molecular systems, and quantum simulations. Contributions may develop new algorithms or use AI to accelerate electronic-structure theory, atomistic simulation, quantum dynamics, materials discovery, property prediction, and the analysis of complex simulation data.The collection welcomes work that combines machine learning with physical constraints, symmetry, conservation laws, uncertainty quantification, and interpretable models, as well as studies that build reliable datasets, benchmarks, and open software for reproducible AI-enabled computation. The goal is to present advances that improve accuracy, efficiency, scalability, and scientific insight beyond conventional workflows, while keeping the focus on theory and computation for physical science.
Topics covered include, but are not limited to:
- Machine-Learning Interatomic Potentials and Force Fields for Atomistic Simulation
- AI-Accelerated Electronic-Structure Theory, Density-Functional Theory, and Many-Body Calculations
- Physics-Informed and Symmetry-Preserving Neural Network Architectures
- Active Learning, Uncertainty Quantification, and Autonomous Computational Workflows
- AI-Driven Materials Discovery, Inverse Design, and Structure-Property Prediction
- Generative and Foundation Models for Molecules, Crystals, Defects, and Interfaces
- Data Infrastructure, Benchmarks, Reproducibility, and Open-Source Software for Computational Physical Science
- Multiscale Modeling Linking Quantum, Atomistic, and Mesoscale Simulations
- AI Methods for Quantum Dynamics, Excited States, Spectroscopy, and Nonadiabatic Processes
- Interpretable AI for Mechanisms, Descriptors, and Discovery of Physical Laws
Guest Editors
Hongjun Xiang (Fudan University), Jian Sun (Nanjing University), Fang Liu (Emory University)