5 papers
Prolate spheroidal wave functions enable fast and exponent-aware long-range machine learning interatomic potentials
Jiuyang Liang, Libin Lu, Yajie Ji +1
Long-range interactions such as electrostatics and dispersion remain a central bottleneck for machine learning interatomic potentials (MLIPs), especially in ionic, polar and interf…
Machine-Learning Interatomic Potentials for Long-Range Systems
Yajie Ji, Jiuyang Liang, Zhenli Xu
Machine-learning interatomic potentials have emerged as a revolutionary class of force-field models in molecular simulations, delivering quantum-mechanical accuracy at a fraction o…
Derivative-informed Graph Convolutional Autoencoder with Phase Classification for the Lifshitz-Petrich Model
Yanlai Chen, Yajie Ji, Zhenli Xu
The Lifshitz-Petrich (LP) model is a classical model for describing complex spatial patterns such as quasicrystals and multiphase structures. Solving and classifying the solutions…
SGPT-PINNs: Sparse and Small models for PDEs
Yajie Ji, Yanlai Chen, Shawn Koohy
We propose SGPT-PINN, a sparse and small model for solving parametric partial differential equations (PDEs). Similar to Small Language Models (SLMs), SGPT-PINN is tailored…
EGPT-PINN: Entropy-enhanced Generative Pre-Trained Physics Informed Neural Networks for parameterized nonlinear conservation laws
Yajie Ji, Yanlai Chen, Zhenli Xu
We propose an entropy-enhanced Generative Pre-Trained Physics-Informed Neural Network with a transform layer (EGPT-PINN) for solving parameterized nonlinear conservation laws. The…