Development of the RF-MEAM Interatomic Potential for the Fe-C System to Study the Temperature-Dependent Elastic Properties

Abstract

One of the major impediments to the computational investigation and design of complex alloys such as steel is the lack of effective and versatile interatomic potentials to perform large-scale calculations. In this study, we developed an RF-MEAM potential for the iron-carbon (Fe-C) system to predict the elastic properties at elevated temperatures. Several potentials were produced by fitting potential parameters to the various datasets containing forces, energies, and stress tensor data generated using density functional theory (DFT) calculations. The potentials were then evaluated using a two-step filter process. In the first step, the optimized RSME error function of the potential fitting code, MEAMfit, was used as the selection criterion. In the second step, molecular dynamics (MD) calculations were employed to calculate ground-state elastic properties of structures present in the training set of the data fitting process. The calculated single crystal and poly-crystalline elastic constants for various Fe-C structures were compared with the DFT and experimental results. The resulting best potential accurately predicted the ground state elastic properties of B1, cementite, and orthorhombic-Fe7C3 (O-Fe7C3), and also calculated the phonon spectra in good agreement with the DFT-calculated ones for cementite and O-Fe7C3. Furthermore, the potential was used to successfully predict the elastic properties of interstitial Fe-C alloys (FeC-0.2% and FeC-0.4%) and O-Fe7C3 at elevated temperatures. The results were in good agreement with the published literature. The successful prediction of elevated temperature properties of structures not included in data fitting validated the potential’s ability to model elevated-temperature elastic properties.

Accelerating Elastic Property Prediction in Fe-C Alloys through Coupling of Molecular Dynamics and Machine Learning

“…Potential development methodology and calculation details have been discussed in our previous publication [28]. Here, we briefly summarize the process to provide a complete picture of the framework that integrates the previous work with the ML model for elastic properties prediction.…”

“…For both alloys, our potential was able to reproduce Young's modulus for a wide range of temperatures more accurately (i.e., closely following the experimental result) than the previously reported SMM result. Detailed results on potential verification can be found in our previous work [28]. For comparison, experimental [62] and SMM-calculated data by Tinh et al [63] are also included.…”