Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems

Abstract

We introduce a multi-tasking graph convolutional neural network, HydraGNN, to simultaneously predict both global and atomic physical properties and demonstrate with ferromagnetic materials. We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum (FePt) with a fixed body centered tetragonal (BCT) lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range. By taking advantage of underlying physical correlations between material properties, multi-task learning (MTL) with HydraGNN provides effective training even with modest amounts of data. Moreover, this is achieved with just one architecture instead of three, as required by single-task learning (STL). The first convolutional layers of the HydraGNN architecture are shared by all learning tasks and extract features common to all material properties. The following layers discriminate the features of the different properties, the results of which are fed to the separate heads of the final layer to produce predictions. Numerical results show that HydraGNN effectively captures the relation between the configurational entropy and the material properties over the entire compositional range. Overall, the accuracy of simultaneous MTL predictions is comparable to the accuracy of the STL predictions. In addition, the computational cost of training HydraGNN for MTL is much lower than the original DFT calculations and also lower than training separate STL models for each property.

arXiv comment13 pages, 6 figures

References in corpus (15)

• Adam: A Method for Stochastic Optimization
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
• AFLOW: An automatic framework for high-throughput materials discovery
• Recent Advances and Applications of Deep Learning Methods in Materials Science
• Atomistic Line Graph Neural Network for Improved Materials Property Predictions
• Physically-informed artificial neural networks for atomistic modeling of materials
• Developing an improved Crystal Graph Convolutional Neural Network framework for accelerated materials discovery
• Principal Neighbourhood Aggregation for Graph Nets
• A recommendation engine for suggesting unexpected thermoelectric chemistries
• Overcoming data scarcity with transfer learning
• MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
• Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture
• Fast and stable deep-learning predictions of material properties for solid solution alloys
• A scalable constructive algorithm for the optimization of neural network architectures

Cited by in corpus (3)

• Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering
• Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN
• Ab initio framework for deciphering trade-off relationships in multi-component alloys