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Gaussian process regression to accelerate geometry optimizations relying on numerical differentiation.

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Gaussian Process Regression (GPR) accelerates molecular geometry optimizations by interpolating potential energy surfaces. This method reduces single-point calculations compared to traditional algorithms, achieving accurate results efficiently.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Materials Science

Background:

  • Geometry optimization is crucial for determining molecular structures and properties.
  • Traditional methods rely on numerical gradients, which can be computationally expensive.
  • Accelerating these optimizations is key for advancing chemical research.

Purpose of the Study:

  • To investigate the acceleration of geometry optimizations using Gaussian Process Regression (GPR).
  • To evaluate the efficiency of combining low-level computational results with GPR-calculated gradients.
  • To assess the accuracy of GPR-optimized structures compared to high-level theory.

Main Methods:

  • Gaussian Process Regression (GPR) for interpolating potential energy surfaces.
  • Combining low-level (HF/MP2) and high-level (CCSD(F12*)(T)) computational data.
  • Utilizing GPR-calculated gradients of the energy difference between methods.
  • Ensuring convergence of both potential energy and geometry.

Main Results:

  • GPR significantly reduces the number of required single-point energy calculations.
  • Optimized structures exhibit high accuracy, with energetic variations in the μEh regime.
  • The hybrid approach (low-level + GPR gradient) proves efficient for optimization.

Conclusions:

  • GPR offers an effective strategy for accelerating molecular geometry optimizations.
  • The method provides a good balance between computational cost and accuracy.
  • This approach is valuable for computational studies requiring precise molecular structures.