Rapid And Accurate Predictions Of Perfect And Defective Material Properties In Atomistic Simulation Using The Power Of 3d Cnn-based Trained Artificial Neural Networks

This article introduces an innovative approach that utilizes machine learning (ML) to address the computational challenges of accurate atomistic simulations in materials science. Focusing on the field of molecular dynamics (MD), the study demonstrates the potential of trained artificial neural networks (tANNs) as surrogate models to capture complex patterns from built datasets, enabling fast and accurate predictions of material properties. The article highlights the application of 3D convolutional neural networks (CNNs) to incorporate atomistic details and defects in predictions, a significant advancement compared to current 2D image-based or descriptor-based methods. Through a dataset of atomistic structures and MD simulations, the trained 3D CNN achieves impressive accuracy, predicting material properties with a root-mean-square error below 0.65 GPa and a speed-up of approximately 185 to 2100 times compared to traditional MD simulations.