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Machine learning many-body potentials for colloidal systems
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AI
DOI：10.1063/5.0063377.png)
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摘要
En 中文
Simulations of colloidal suspensions consisting of mesoscopic particles and smaller species such as ions or depletants are computationally challenging as different length and time scales are involved. Here, we introduce a machine learning (ML) approach in which the degrees of freedom of the microscopic species are integrated out and the mesoscopic particles interact with effective many-body potentials, which we fit as a function of all colloid coordinates with a set of symmetry functions. We apply this approach to a colloid-polymer mixture. Remarkably, the ML potentials can be assumed to be effectively state-independent and can be used in direct-coexistence simulations. We show that our ML method reduces the computational cost by several orders of magnitude compared to a numerical evaluation and accurately describes the phase behavior and structure, even for state points where the effective potential is largely determined by many-body contributions.
Keyword:
FLUID-FLUID INTERFACE
PHASE-BEHAVIOR
MOLECULAR-DYNAMICS
SIMULATION
CHARGE
COEXISTENCE
期刊
IF:
3.1
论文数:
7.2W
被引数:
23.2W
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