A Generalized Machine-Learning Framework for Developing Alchemical Many-Body Interaction Models for Polymer-Grafted Nanoparticles

Abstract

Polymer-grafted nanoparticles (PGNs) serve as highly customizable building blocks for technologically relevant self-assembled nanomaterials. Physics-informed inverse design strategies are crucial for expediting exploration of the massive associated design space by identifying optimal PGN attributes, such as polymer length and grafting density, to meet a target self-assembled structure. However, their success hinges on an accurate description of how PGN interactions vary as a function of both particle positions and physical attributes. We introduce a framework to meet this need. Specifically, we develop an “alchemical” machine-learned interatomic model (ML-IAM) that describes how PGN interactions vary as a function of both inter-PGN distances and tunable PGN attributes, simultaneously. This model is an extension of the physics-informed and explicitly many-bodied ChIMES ML-IAM. The resulting extended ChIMES (X-ChIMES) ML-IAM is trained on potential of mean force (PMF) data for PGNs with varied polymer ligand lengths. We enable efficient training data generation by combining the forward–reverse steered molecular dynamics enhanced sampling approach with a grid-sampling scheme in the HOOMD-blue software package. We demonstrate the efficacy of ChIMES for generating coarse-grained (CG) models for PGNs with fixed design attributes, development of X-ChIMES, and its application for enhancing digital alchemy inverse-design simulations using HOOMD-blue and the ChIMES Calculator. This is the first application of ChIMES in modeling CG systems and coupling with an inverse design method to target nanomaterial self-assembly.