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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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MELTS: Fully Automated Active Learning for Fewest-Switches Surface Hopping Dynamics.

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MELTS, an automated active learning program, significantly accelerates simulations of photochemical processes. It uses machine learning potentials to achieve accurate results up to 1000 times faster than traditional methods.

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Area of Science:

  • Computational chemistry
  • Photochemistry
  • Machine learning

Background:

  • Simulating photochemical processes using fewest-switches surface hopping (FSSH) is computationally intensive due to numerous electronic structure calculations.
  • Machine learning (ML) interatomic potentials can reduce computational cost but require training data from relevant configurational spaces.

Purpose of the Study:

  • To develop an automated active learning (AL) program, MELTS, for efficiently generating accurate ML potentials for FSSH simulations.
  • To reduce the computational cost of simulating photochemical processes across various time scales.

Main Methods:

  • MELTS employs an AL protocol that uses trajectory propagation to guide the iterative improvement of ML models.
  • It integrates Newton-X and MLatom via socket communication for efficient, large-scale simulations.
  • The program features a user-friendly interface for accessibility.

Main Results:

  • MELTS achieved quantitative agreement with reference quantum results for both ultrafast fulvene dynamics and nanosecond-scale pyrene fluorescence.
  • Computational time was reduced by up to three orders of magnitude compared to conventional methods.
  • The AL protocol effectively guided sampling to relevant configurational regions.

Conclusions:

  • MELTS demonstrates efficient and accurate generation of ML potentials for photochemical simulations.
  • The program significantly reduces computational demands for studying processes from femtoseconds to nanoseconds.
  • MELTS offers a user-friendly solution for large-scale photochemical simulations.