Autonomous computational frameworks such as AFLOW (Automatic FLOW) are generating large databases that power materials discovery workflows. The AFLOW.org repository is the largest of its kind, containing more than three million compounds. The data, generated by the AFLOW software, has been employed for the discovery of two magnets – the first discovered by computational approaches – and six new high-entropy, high-hardness metal carbides.
This tutorial will provide an overview of select features of the AFLOW code that are relevant to materials development for both computational and experimental researchers. Topics covered include accessing and interacting with the AFLOW database, structure prototypes and crystal symmetry, determining the synthesizability of a material, and modeling chemically disordered materials such as high-entropy alloys.
Participants will learn how to install AFLOW, use our online tools, and to integrate AFLOW into their workflow using python.
Materials Database Access: AFLOW.org and AFLUX
Marco Esters, Duke University
Computational materials databases play an important role in the design and synthesis of new compounds with desired physical properties. With more than three million entries, the AFLOW.org repository is the largest of its kind. This session will start with an introduction to AFLOW and its installation. It will conclude with a demonstration on how to use AFLUX, a search language developed for AFLOW, to query results from our database programatically. Python will be used to show how the data can be retrieved and returned in JSON format.
Structural Analysis and Generation of Materials—AFLOW-SYM, AFLOW Prototype Encyclopedia, and AFLOW-XtalFinder
David Hicks, Duke University
Classification of crystallographic structures is necessary for understanding and tuning the properties of materials. Coupling structural analysis tools with automatic methods for modeling compounds accelerates materials design. This part will introduce the symmetry and structure generation engines inside AFLOW. Participants will learn the AFLOW prototyping system, how to create structure files in various ab initio code formats, how to calculate the symmetries of a material, and how to compare structures. Python interfaces and web interfaces to these tools will be provided as well.
Thermodynamics—AFLOW-CHULL and CCE
Cormac Toher, Duke University
One of the most important aspects of computational materials discovery is the prediction of synthesizability. The AFLOW Convex HULL (AFLOW-CHULL) application provides the tools to assess the stability and synthesizability of materials. Attendants will be taught how to use AFLOW-CHULL through both the command line and our web application. This part will also demonstrate how to correct the formation enthalpies of polar compounds (e.g. oxides), which are often inaccurate in standard density functional theory calculations, using the Coordination Corrected Enthalpies (CCE) method.
Corey Oses, Duke University
Disordered materials such as high-entropy alloys have attracted considerable interest due to their enhanced physical properties such as high hardness. Modeling these materials, however, poses a considerable computational challenge. This section will cover AFLOW's Partial OCCupation (AFLOW-POCC) algorithm which has been successfully employed to predict six new high-entropy carbides. Participants will be introduced to the algorithm and learn how to use AFLOW to generate the structures to determine the properties of disordered materials.