Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference (1903.09613v1)

Abstract: Realistic finite temperature simulations of matter are a formidable challenge for first principles methods. Long simulation times and large length scales are required, demanding years of compute time. Here we present an on-the-fly machine learning scheme that generates force fields automatically during molecular dynamics simulations. This opens up the required time and length scales, while retaining the distinctive chemical precision of first principles methods and minimizing the need for human intervention. The method is widely applicable to multi-element complex systems. We demonstrate its predictive power on the entropy driven phase transitions of hybrid perovskites, which have never been accurately described in simulations. Using machine learned potentials, isothermal-isobaric simulations give direct insight into the underlying microscopic mechanisms. Finally, we relate the phase transition temperatures of different perovskites to the radii of the involved species, and we determine the order of the transitions in Landau theory.

Phase Transitions of Hybrid Perovskites Modeled by On-the-Fly Machine-Learning Force Fields

The paper by Jinnouchi et al. investigates the phase transitions of hybrid perovskites using a novel approach involving on-the-fly machine-learning (ML) force fields. The paper addresses a significant challenge in computational materials science: the accurate modeling of complex materials at finite temperatures, which often requires extensive computational resources far beyond the reach of traditional first-principles (FP) methods.

Methodology Overview

The authors have developed an ML framework adapted to automatically generate force fields during molecular dynamics (MD) simulations. Unlike previous methods relying on pre-selected data sets, this approach leverages Bayesian inference to dynamically decide the necessity of FP calculations based on predicted errors in force field estimates. Consequently, calculations are selectively bypassed when high-confidence predictions are made, significantly reducing computational time without compromising accuracy.

The implementation centers on a variant of the Gaussian Approximation Potential (GAP) and employs Smooth Overlap of Atomic Positions (SOAP) descriptors for kernel-based similarity measures. The integration within the Vienna Ab initio Simulation Package (VASP) framework allows for efficient sampling in simulations, covering structures over broad phase space scales.

Application to Hybrid Perovskites

The paper applied the methodology to hybrid perovskites, namely methylammonium lead iodide (MAPbI₃), a material with applications in solar cells. The slow rotational dynamics of the molecules within these materials present a unique challenge, making them an ideal test case for this simulation approach.

Key outcomes from this paper include:

• Simulation Efficiency and Accuracy: The on-the-fly method bypassed approximately 99% of FP calculations during training, leading to a computational time reduction by a factor of nearly 100 while achieving near-FP quality in terms of energy predictions, forces, and stress tensors.
• Phase Transition Insights: Through detailed isothermal-isobaric simulations, the paper mapped out the detailed phase transitions of MAPbI₃, accurately predicting the orthorhombic to tetragonal phase transition at $215 \pm 10$ K and the tetragonal to cubic transition at 353 K. Furthermore, the critical exponent for the tetragonal distortion matched well with experimental results, showcasing the method's predictive power.
• Molecular and Structural Dynamics: Analysis of the orientation of methylammonium molecules revealed detailed insights into the microscopic transition mechanisms, including the conditions under which rotational freedom emerges and how it influences phase behavior, providing unparalleled insight compared to experimental observation alone.

Implications and Future Directions

The integration of ML with on-the-fly learning represents a substantial advancement in material simulation capabilities. This approach opens the door for efficiently modeling complex multi-elemental systems at finite temperatures with significantly reduced human input.

For hybrid perovskites and materials with similar dynamic properties, this method offers qualitative and quantitative agreements with experimental data, suggesting its broad applicability for predictive modeling in material science. Future work could extend this capability to explore other temperature-dependent properties and apply it across different classes of materials where traditional methods fall short.

Further research could also focus on enhancing the adaptability of the ML model to account for a wider range of environmental conditions and material types, paving the way for advancements in design and discovery of materials for application in energy, electronics, and beyond.