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Machine learning now predicts molecular excited states by learning potentials and nonadiabatic coupling vectors (NACs). A new method reconstructs double-valued NACs, overcoming challenges posed by conical intersections for reliable machine learning.

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Area of Science:

  • Computational Chemistry
  • Quantum Mechanics
  • Machine Learning

Background:

  • Machine learning (ML) excels at fitting ab initio potential-energy surfaces for ground-state molecular simulations.
  • Extending ML to excited-state dynamics requires learning both potentials and nonadiabatic coupling vectors (NACs).
  • Standard ML techniques struggle with NACs in systems with conical intersections due to double-valued nature arising from geometric-phase effects.

Purpose of the Study:

  • To develop a reliable machine learning approach for excited-state dynamics simulations.
  • To address the challenge of learning double-valued nonadiabatic coupling vectors (NACs) in systems with conical intersections.

Main Methods:

  • Introduced auxiliary single-valued functions to represent NACs.
  • Developed a method to reconstruct double-valued NACs from these auxiliary functions.
  • Enabled the use of NACs as training data for standard machine learning techniques.

Main Results:

  • Successfully reconstructed nonadiabatic coupling vectors (NACs) from auxiliary single-valued functions.
  • Overcame the limitation of double-valued NACs in systems with conical intersections.
  • Enabled reliable machine learning for excited-state dynamics.

Conclusions:

  • The proposed method allows for the reliable machine learning of nonadiabatic coupling vectors (NACs).
  • This advancement extends the applicability of machine learning to complex excited-state molecular dynamics.
  • Facilitates more accurate and efficient simulations of molecular excited states.