paper-with-me

Papers

First principles physics-informed neural network for quantum wavefunctions and eigenvalue surfaces

2022-11-08 · Marios Mattheakis, Gabriel R. Schleder, Daniel T. Larson, Efthimios Kaxiras

Physics-informed neural networks have been widely applied to learn general parametric solutions of differential equations. Here, we propose a neural network to discover parametric eigenvalue and eigenfunction surfaces of quantum systems. We apply our method to solve the hydrogen molecular ion. This is an ab-initio deep learning method that solves the Schrodinger equation with the Coulomb potential yielding realistic wavefunctions that include a cusp at the ion positions. The neural solutions are continuous and differentiable functions of the interatomic distance and their derivatives are analytically calculated by applying automatic differentiation. Such a parametric and analytical form of the solutions is useful for further calculations such as the determination of force fields.

📄 PDF Abstract BibTeX arXiv:2211.04607

Code (1)

mariosmat/pinn_for_quantum_wavefunction_surfaces 공식 구현

Similar Papers 제목 키워드 기반

Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks

2019-09-05 · David Pfau, James S. Spencer, Alexander G. de G. Matthews, W. M. C. Foulkes

Given access to accurate solutions of the many-electron Schr\"odinger equation, nearly all chemistry could be derived from first principles. Exact wavefunctions of interesting chemical systems are out of reach because th…

Physics-informed Reduced-Order Learning from the First Principles for Simulation of Quantum Nanostructures

2023-01-31 · Martin Veresko, Ming-Cheng Cheng

Multi-dimensional direct numerical simulation (DNS) of the Schr\"odinger equation is needed for design and analysis of quantum nanostructures that offer numerous applications in biology, medicine, materials, electronic/p…

Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory

2026-03-03 · Xuan Zhang, Haiyang Yu, Chengdong Wang, Jacob Helwig 외 arxiv

We aim to learn wavefunctions simulated by time-dependent density functional theory (TDDFT), which can be efficiently represented as linear combination coefficients of atomic orbitals. In real-time TDDFT, the electronic …

Ab-initio quantum chemistry with neural-network wavefunctions

2022-08-26 · Jan Hermann, James Spencer, Kenny Choo, Antonio Mezzacapo 외

Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also play an increasingly important role in …

Quantizationscientific discovery

Physics-Informed Neural Networks for Discovering Localised Eigenstates in Disordered Media

2023-05-11 · Liam Harcombe, Quanling Deng

The Schr\"{o}dinger equation with random potentials is a fundamental model for understanding the behaviour of particles in disordered systems. Disordered media are characterised by complex potentials that lead to the loc…