Developing Machine-Learned Potentials for Coarse-Grained Molecular Simulations: Challenges and Pitfalls

Abstract

Coarse graining (CG) enables the investigation of molecular properties for larger systems and at longer timescales than the ones attainable at the atomistic resolution. Machine learning techniques have been recently proposed to learn CG particle interactions, i.e. develop CG force fields. Graph representations of molecules and supervised training of a graph convolutional neural network architecture are used to learn the potential of mean force through a force matching scheme. In this work, the force acting on each CG particle is correlated to a learned representation of its local environment that goes under the name of SchNet, constructed via continuous filter convolutions. We explore the application of SchNet models to obtain a CG potential for liquid benzene, investigating the effect of model architecture and hyperparameters on the thermodynamic, dynamical, and structural properties of the simulated CG systems, reporting and discussing challenges encountered and future directions envisioned.

arXiv commentProceedings of 12th Conference on Artificial Intelligence (SETN), 2022

References in corpus (5)

• SchNet - a deep learning architecture for molecules and materials
• Interatomic potentials from first-principles calculations: the force-matching method
• SchNetPack: A Deep Learning Toolbox For Atomistic Systems
• Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks
• Investigation of Machine Learning-based Coarse-Grained Mapping Schemes for Organic Molecules

Cited by in corpus (1)

• Investigation of Machine Learning-based Coarse-Grained Mapping Schemes for Organic Molecules