Browsing by Subject "Ab-initio"

The structure-property relationship is the foundation for materials modeling, predicting the behavior of compounds based on structural characteristics. With the advancement of ab initio methods and high performance computing, atomic configurations are being explored at an unprecedented rate. To effectively navigate the vast search space, procedures are presented for analyzing and prototyping crystalline compounds for high-throughput simulation. Integrated into the Automatic Flow (AFLOW) framework for computational materials discovery, these tools are the underlying workhorse for symmetry classification and materials generation. In particular, algorithms are detailed for determining the set of isometries for crystals, featuring a comprehensive collection of symmetry descriptions along with routines to handle ill-conditioned structural data. A library of crystallographic structures is also introduced — showcasing nearly 600 prototypes with representatives from each space group — and is complemented with functionality for rapidly creating materials via prototype decoration. Lastly, a module for comparing crystalline compounds is described to identify duplicate entries within large data sets and detect novel structure-types, independent of representation. Mechanisms are featured for converting geometries into a standard prototype convention, providing a direct pathway for incorporation into the crystallographic library. With these autonomous computational approaches, compounds are automatically classified and generated, enabling the design of new and structurally distinct materials.

Traditional materials discovery approaches - relying primarily on laborious experiments - have controlled the pace of technology. Instead, computational approaches offer an accelerated path: high-throughput exploration and characterization of virtual structures. These ventures, performed by automated ab-initio frameworks, have rapidly expanded the volume of programmatically-accessible data, cultivating opportunities for data-driven approaches. Herein, a collection of robust characterization methods are presented, implemented within the Automatic Flow Framework for Materials Discovery (AFLOW), that leverages materials data for the prediction of phase diagrams and properties of disordered materials. These methods directly address the issue of materials synthesizability, bridging the gap between simulation and experiment. Powering these predictions is the AFLOW.org repository for inorganic crystals, the largest and most comprehensive database of its kind, containing more than 2 million compounds with about 100 different properties computed for each. As calculated with standardized parameter sets, the wealth of data also presents a favorable learning environment. Machine learning algorithms are employed for property prediction, descriptor development, design rule discovery, and the identification of candidate functional materials. When combined with physical models and intelligently formulated descriptors, the data becomes a powerful tool, facilitating the discovery of new materials for applications ranging from high-temperature superconductors to thermoelectrics. These methods have been validated by the synthesis of two new permanent magnets introduced herein - the first discovered by computational approaches.