Browsing by Author "Curtarolo, Stefano"

Effective computational materials search, categorization, and design necessitates a high-throughput (HT) approach. System by system analyses lack the scope and speed needed to uncover large portions of the materials landscape. By performing broad searches over structural or chemical classes of materials and guided by fundamental physical principles, materials with specific desired properties can be systematically found. Furthermore, the HT approach is an effective general tool for materials classification. Depending on the application, various properties can be computed leading to powerful classification schemes. To implement HT materials studies, however, a versatile and robust framework must first be developed. In this paper, the HT framework AFLOW that has been developed and used successfully over the last decade is presented. Specifically, attention is given to an origin-specific symmetry algorithm. The algorithm is designed to determine the relevant symmetry properties of an arbitrary crystal structure (e.g., point group, space group, etc.).

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.

Recently, in materials innovations, computational methods are used more frequently than in past decades. In this thesis, the materials genome initiative, an advanced new framework, will be introduced. With this blueprint, our efficient high-throughput software, AFLOW, has been implemented with several compatible functions for ma- terials properties investigations, such as prototype searching, phase diagram studying and magnetic properties discovering. With this effective tool, we apply ab initio cal- culations to discover new generation of specific materials properties.

An efficient algorithm for prototypes comparision has been designed and imple- mented into our high-throughput framework AFLOW. In addition, prototypes clas- sification was utilized to differentiate the our materials database. This classification will accelerate the materials properties searching speed. With respect to structure prototypes, low temperature phase diagrams were used for binary and ternary alloy systems stability investigation. The alogrithms have been integrated into AFLOW. With this tool, we systematically explored the binary Ru systems and Tc systems and predicted new stable compounds.

The high-throughput framework AFLOW that has been developed and used successfully over the last decade is improved to include fully-integrated software for crystallographic symmetry characterization. The standards used in the symmetry algorithms conform with the conventions and prescriptions given in the International Tables of Crystallography (ITC). A standard cell choice with standard origin is selected, and the space group, point group, Bravais lattice, crystal system, lattice system, and representative symmetry operations are determined. Following the conventions of the ITC, the Wyckoff sites are also determined and their labels and site symmetry are provided. The symmetry code makes no assumptions on the input cell orientation, origin, or reduction and has been integrated in the AFLOW high-throughput framework for materials discovery by adding to the existing code base and making use of existing classes and functions. The software is written in object-oriented C++ for flexibility and reuse. A performance analysis and examination of the algorithms scaling with cell size and symmetry is also reported.

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.