Inverse design of glass structure with deep graph neural networks

Abstract

Directly manipulating the atomic structure to achieve a specific property is a long pursuit in the field of materials. However, hindered by the disordered, non-prototypical glass structure and the complex interplay between structure and property, such inverse design is dauntingly hard for glasses. Here, combining two cutting-edge techniques, graph neural networks and swap Monte Carlo, we develop a data-driven, property-oriented inverse design route that managed to improve the plastic resistance of Cu-Zr metallic glasses in a controllable way. Swap Monte Carlo, as "sampler", effectively explores the glass landscape, and graph neural networks, with high regression accuracy in predicting the plastic resistance, serves as "decider" to guide the search in configuration space. Via an unconventional strengthening mechanism, a geometrically ultra-stable yet energetically meta-stable state is unraveled, contrary to the common belief that the higher the energy, the lower the plastic resistance. This demonstrates a vast configuration space that can be easily overlooked by conventional atomistic simulations. The data-driven techniques, structural search methods and optimization algorithms consolidate to form a toolbox, paving a new way to the design of glassy materials.

References in corpus (11)

• A Comprehensive Survey on Graph Neural Networks
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• Dynamics of Viscoplastic Deformation in Amorphous Solids
• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• Amorphous Systems in Athermal, Quasistatic Shear
• Deep learning in nano-photonics: inverse design and beyond
• Identifying structural flow defects in disordered solids using machine learning methods
• Evolutionary Multi-Objective Optimisation of Colour Pixels based on Dielectric Nano-Antennas
• A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses
• Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning

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• Graph neural network-based structural classification of glass-forming liquids and its interpretation via self-attention mechanism
• Towards interpreting the thermally activated $\beta$ dynamics in metallic glass with the structural constraint neural network
• Computational Methods toward Ultrastable Glasses