Bulletin of the American Physical Society
2024 APS March Meeting
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session EE04: V: Machine Learning of Molecules and Materials
11:30 AM–12:54 PM,
Tuesday, March 5, 2024
Room: Virtual Room 04
Sponsoring
Unit:
DCOMP
Chair: Michele Pavanello, Rutgers University - Newark; Valeria Rios Vargas, Rutgers University
Abstract: EE04.00003 : Quantifying the Lennard–Jones Potential between Two Hard Ellipsoids Using Coarse-Grained Deep Learning Techniques
11:54 AM–12:06 PM
Presenter:
Erin Wong
(Great Neck South High School)
Authors:
Erin Wong
(Great Neck South High School)
Dylan Fei
(Jericho Senior High School)
Georgios Kementzidis
(Stony Brook University)
Yuefan Deng
(Stony Brook University)
Classical molecular dynamics is a highly intensive method for simulating the kinetics, thermodynamics, and structural properties of a many-body system over time. Such simulations require the use of energy functions describing the trajectory and momenta of the particles. We focus on the Lennard–Jones potential (LJP), which governs the van der Waals energetics between two particles. By accelerating this non-bonded energy function, we will have simplified the simulation of simple particles (e.g. platelets, nanoparticles) in materials science. We seek to modify the potential’s conventional form, (1), which fails to accurately model the vdW forces between large anisotropic particles. While (3), the current standard for calculation of the potential accurately represents such forces, it is computationally expensive.
We propose a physics-informed neural network (PINN) to learn the parameters of (2). Our goal is to replicate the accuracy of (3) by training our PINN to recognize the relationship between the coefficients of (2) and the parameters of two ellipsoids, our particle of choice. The architecture of the PINN is as follows: two neural networks were ensembled together to find the coefficients of εAB and σAB and tune for σ0 in (2). A minimum training loss of 0.0004 over 250 epochs was achieved by implementing an adaptive learning rate algorithm.
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