On-lattice voxelated convolutional neural networks for prediction of phase diagrams and diffusion barriers in cubic alloys

2021/03/03 by S. Mohadeseh Taheri‐Mousavi, Taheri-Mousavi, Seyedeh Mohadeseh, Seyed Sina Moeini-Ardakani +7
Engineering · Materials Science · #Advanced Materials Characterization Techniques #Computational Physics (physics.comp-ph) #FOS: Physical sciences #High Temperature Alloys and Creep #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci)

paper · pdf · doi:10.48550/arxiv.2103.02638

openalex publication_date 2021/03/03 · openalex created_date 2021/03/15 · openalex updated_date 2026/07/28

Abstract

Cluster expansion approximates an on-lattice potential with polynomial regression. We show that using a convolutional neural network (CNN) instead leads to more accurate prediction due to the depth of the network. We construct our CNN potential directly on cubic lattice sites, representing voxels in a 3D image, and refer to our method as the voxelated CNN (VCNN). The convolutional layers automatically integrate interaction terms in the regressor; thus, no explicit definition of clusters is required. As a model system, we combine our VCNN potential with Monte Carlo simulations on a Ni_1-xAl_x (x < 30%) and predict a disordered-to-ordered phase transition with less than 1 meV/atom error. We also predict the energetic landscape of vacancy diffusion. Classification of formation energy with respect to short-range-ordering of Al alloys around a vacancy reveals that the ordering decreases the probability of Ni diffusion. As the width of our input layer does not depend on the atomic composition, VCNNs can be applied to study alloys with arbitrary numbers of elements and empty lattice sites, without additional computational costs.

Citations

• Fast Parallel Algorithms for Short-Range Molecular Dynamics
• Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks

Related

• 3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys
• Effects of the local chemical environment on vacancy diffusion in multi-principal element alloys
• GNN-Assisted Phase Space Integration with Application to Atomistics
• Deep learning in-depth analysis of crystal graph convolutional neural networks: A new era in materials discovery and its applications
• Machine Learned Phase Transitions in a System of Anisotropic Particles\n on a Square Lattice