Bridging microscopy with molecular dynamics and quantum simulations: An AtomAI based pipeline

TL;DR

This paper introduces a machine learning pipeline that connects microscopy data directly to atomistic simulations, enabling real-time analysis and exploration of material structures with improved accuracy and efficiency.

Contribution

It develops a neural network-based workflow that bridges microscopy data with molecular dynamics and quantum simulations, facilitating rapid, physics-informed analysis of materials.

Findings

Successfully reconstructed graphene structures from microscopy data

Simulated temperature-dependent dynamics including adsorption and healing

Demonstrated universal applicability to other material systems

Abstract

Recent advances in (scanning) transmission electron microscopy have enabled routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly non-trivial due to the extreme disparity between intrinsic time scales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. The pathways to ensure the structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems.