The high-level goal of this tutorial is to demonstrate the versatility of graph neural networks for inverse design of materials. Different strategies for materials design will be explored.
The specific objectives of the tutorial are:
- Introduction to graph neural networks - In this section, we will introduce the fundamentals of graph networks including essential components and tasks. The tutorial will describe the basics of molecules and crystals as graphs and describe graph connectivity, representations, centralities, and message passing for predicting materials properties.
- Graph neural network potential for the periodic table - In this section, we will demonstrate using GNN as interatomic potentials for general crystals. This tutorial will cover the basics of computational materials, running the M3GNet model for structural relaxations, property predictions, and molecular dynamics to get the diffusivity and conductivity.
- Hierarchical GNNs for design and analysis of Zintl phases - In this section, we will use the family of materials called Zintl phases to illustrate how hierarchical graph neural networks can be used to automatically identify structural motifs that can aid in the prediction of materials properties and design of new materials.
- Reinforcement learning with GNNs for inverse material design - In this section, we will demonstrate using GNNs as a surrogate objective function in inverse design applications. This will consist of a series of simple code demos, where we optimize over structure space for a simple crystal system. We will demonstrate how to specify the search space, construct a GNN policy model, and optimize a given GNN surrogate energy function.
Introduction to Graph NN
Taylor Sparks, University of Utah
This section will include fundamentals of graph networks: components, directionality and tasks, molecules and crystals as graphs, matrix representation, graph connectivity, and centralities, message passing, node/edge/graph representations, and comments on advanced alternative GNNs (multigraphs, hierarchical graphs etc).
GNN Universal Interatomic Potential for Materials Design
Chi Chen, Microsoft Quantum
This section will include the basics of computational materials and tools, running M3GNet for structural relaxation and property predictions, and running M3GNet for molecular dynamics and diffusivity and conductivity calculations.
Zintl Phases and Hierarchical Graph NN
Prashun Gorai, Colorado School of Mines, NREL; Qian Yang, University of Connecticut
This section will include an introduction to Zintl phases and structural motifs, hierarchical graph neural networks for crystals, and examples of running code and results of trained model - visualization of automatically identified motifs for Zintl phases.
Reinforcement Learning with Graph NN
Peter St. John, National Renewable Energy Lab
This section will include an introduction to the graph-env and crystal packages, specification of an action space for a reduced crystal structure search, construction of a GNN policy model, and running RL search for top-performing candidates.