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Related Experiment Video

Updated: Feb 22, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Constructing Diabatic Potential Energy Matrices with Quantum Dynamic Accuracy: A Neural Network Based Δ-Machine

Siting Hou1, Zejie Zhang1, Changjian Xie1

  • 1Institute of Modern Physics, Shaanxi Key Laboratory for Theoretical Physics Frontiers, Northwest University, Xi'an 710127, China.

Journal of Chemical Theory and Computation
|February 20, 2026
PubMed
Summary
This summary is machine-generated.

A novel neural network (NN) based Delta-machine learning (Δ-ML) method efficiently constructs global diabatic potential energy matrices (PEMs) for molecular systems. This approach significantly reduces computational costs while maintaining high accuracy for complex chemical reactions and photodissociation processes.

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Area of Science:

  • Computational Chemistry
  • Quantum Mechanics
  • Machine Learning in Chemistry

Background:

  • Accurate potential energy matrices (PEMs) are crucial for understanding molecular dynamics.
  • Calculating high-level diabatic PEMs is computationally expensive.
  • Existing methods struggle with global accuracy and efficiency for coupled states.

Purpose of the Study:

  • To propose a new neural network (NN)-based Delta-machine learning (Δ-ML) approach.
  • To construct global diabatic potential energy matrices (PEMs) for molecular systems.
  • To reduce computational costs associated with high-level energy calculations.

Main Methods:

  • Utilized inexpensive low-level energy data combined with a few high-level energies.
  • Developed two NN-based Δ-ML schemes (A and B) for training adiabatic energy data.
  • Applied the approach to nonadiabatic reactions (Na + H₂) and photodissociation (NH₃).

Main Results:

  • Achieved effective and accurate global diabatic PEMs for both example systems.
  • Reduced high-level calculation costs by approximately 87%.
  • Successfully reproduced nonadiabatic reaction probabilities, absorption spectra, and product branching ratios.

Conclusions:

  • The NN-based Δ-ML approach provides a computationally efficient and accurate method for PEM construction.
  • Scheme B demonstrated superior training efficiency in the NH₃ system due to higher degrees of freedom.
  • This method holds significant promise for advancing studies in molecular dynamics and spectroscopy.