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Machine learning phase space quantum dynamics approaches
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DOI：10.1063/5.0046689.png)
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Abstract
En 中文
Derived from phase space expressions of the quantum Liouville theorem, equilibrium continuity dynamics is a category of trajectory-based phase space dynamics methods, which satisfies the two critical fundamental criteria: conservation of the quantum Boltzmann distribution for the thermal equilibrium system and being exact for any thermal correlation functions (even of nonlinear operators) in the classical and harmonic limits. The effective force and effective mass matrix are important elements in the equations of motion of equilibrium continuity dynamics, where only the zeroth term of an exact series expansion of the phase space propagator is involved. We introduce a machine learning approach for fitting these elements in quantum phase space, leading to a much more efficient integration of the equations of motion. Proof-of-concept applications to realistic molecules demonstrate that machine learning phase space dynamics approaches are possible as well as competent in producing reasonably accurate results with a modest computation effort.
Keywords:
TIME-CORRELATION FUNCTIONS
VIBRATIONAL-ENERGY RELAXATION
CENTROID MOLECULAR-DYNAMICS
LIQUID PARA-HYDROGEN
NEURAL-NETWORK
STATISTICAL-MECHANICS
RATE CONSTANTS
SEMICLASSICAL THEORY
REPRESENTATION
MODEL
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