Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange.

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the v1.

Materials design for hypersonics.

Hypersonic vehicles must withstand extreme conditions during flights that exceed five times the speed of sound. These systems have the potential to facilitate rapid access to space, bolster defense capabilities, and create a new paradigm for transcontinental earth-to-earth travel. However, extreme aerothermal environments create significant challenges for vehicle materials and structures.

Magnetic State Control of Non-van der Waals 2D Materials by Hydrogenation.

Controlling the magnetic state of two-dimensional (2D) materials is crucial for spintronics. By employing data-mining and autonomous density functional theory calculations, we demonstrate the switching of magnetic properties of 2D non-van der Waals materials upon hydrogen passivation. The magnetic configurations are tuned to states with flipped and enhanced moments.

AFLOW-CCE for the thermodynamics of ionic materials.

Accurate thermodynamic stability predictions enable data-driven computational materials design. Standard density functional theory (DFT) approximations have limited accuracy with average errors of a few hundred meV/atom for ionic materials, such as oxides and nitrides. Thus, insightful correction schemes as given by the coordination corrected enthalpies (CCE) method, based on an intuitive parametrization of DFT errors with respect to coordination numbers and cation oxidation states, present a simple, yet accurate solution to enable materials stability assessments.

Disordered enthalpy-entropy descriptor for high-entropy ceramics discovery.

The need for improved functionalities in extreme environments is fuelling interest in high-entropy ceramics. Except for the computational discovery of high-entropy carbides, performed with the entropy-forming-ability descriptor, most innovation has been slowly driven by experimental means. Hence, advancement in the field needs more theoretical contributions.

Shared metadata for data-centric materials science.

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles () must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments.

Plasmonic high-entropy carbides.

Discovering multifunctional materials with tunable plasmonic properties, capable of surviving harsh environments is critical for advanced optical and telecommunication applications. We chose high-entropy transition-metal carbides because of their exceptional thermal, chemical stability, and mechanical properties. By integrating computational thermodynamic disorder modeling and time-dependent density functional theory characterization, we discovered a crossover energy in the infrared and visible range, corresponding to a metal-to-dielectric transition, exploitable for plasmonics.

The Microscopic Diamond Anvil Cell: Stabilization of Superhard, Superconducting Carbon Allotropes at Ambient Pressure.

A metallic, covalently bonded carbon allotrope is predicted via first principles calculations. It is composed of an sp carbon framework that acts as a diamond anvil cell by constraining the distance between parallel cis-polyacetylene chains. The distance between these sp carbon atoms renders the phase metallic, and yields two well-nested nearly parallel bands that cross the Fermi level.

Data-Driven Quest for Two-Dimensional Non-van der Waals Materials.

Two-dimensional (2D) materials are frequently associated with the sheets forming bulk layered compounds bonded by van der Waals (vdW) forces. The anisotropy and weak interaction between the sheets have also been the main criteria in the computational search for new 2D systems, predicting ∼2000 exfoliable compounds. However, some representatives of a new type of non-vdW 2D systems, without layered 3D analogues, were recently manufactured.

Settling the matter of the role of vibrations in the stability of high-entropy carbides.

High-entropy ceramics are attracting significant interest due to their exceptional chemical stability and physical properties. While configurational entropy descriptors have been successfully implemented to predict their formation and even to discover new materials, the contribution of vibrations to their stability has been contentious. This work unravels the issue by computationally integrating disorder parameterization, phonon modeling, and thermodynamic characterization.

Entropy Landscaping of High-Entropy Carbides.

The entropy landscape of high-entropy carbides can be used to understand and predict their structure, properties, and stability. Using first principles calculations, the individual and temperature-dependent contributions of vibrational, electronic, and configurational entropies are analyzed, and compare them qualitatively to the enthalpies of mixing. As an experimental complement, high-entropy carbide thin films are synthesized with high power impulse magnetron sputtering to assess structure and properties.

OPTIMADE, an API for exchanging materials data.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages.

On-the-fly closed-loop materials discovery via Bayesian active learning.

Active learning-the field of machine learning (ML) dedicated to optimal experiment design-has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools.

Correction: QSAR without borders.

Correction for 'QSAR without borders' by Eugene N. Muratov et al., Chem.

QSAR without borders.

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics.

High-Throughput Computational Search for Half-Metallic Oxides.

Half metals are a peculiar class of ferromagnets that have a metallic density of states at the Fermi level in one spin channel and simultaneous semiconducting or insulating properties in the opposite one. Even though they are very desirable for spintronics applications, identification of robust half-metallic materials is by no means an easy task. Because their unusual electronic structures emerge from subtleties in the hybridization of the orbitals, there is no simple rule which permits to select a priori suitable candidate materials.

Vibrational spectral fingerprinting for chemical recognition of biominerals.

Pathologies associated with calcified tissue, such as osteoporosis, demand in vivo and/or in situ spectroscopic analysis to assess the role of chemical substitutions in the inorganic component. High energy X-ray or NMR spectroscopies are impractical or damaging in biomedical conditions. Low energy spectroscopies, such as IR and Raman techniques, are often the best alternative.

Mechanical Properties of Chemically Modified Clay.

Serpentine clay minerals are found in many geological settings. The rich diversity, both in chemical composition and crystal structure, alters the elastic behavior of clay rocks significantly, thus modifying seismic and sonic responses to shaley sequences. Computation of the elastic properties is a useful tool to characterize this diversity.

High-entropy high-hardness metal carbides discovered by entropy descriptors.

High-entropy materials have attracted considerable interest due to the combination of useful properties and promising applications. Predicting their formation remains the major hindrance to the discovery of new systems. Here we propose a descriptor-entropy forming ability-for addressing synthesizability from first principles.

Vibrational Properties of Metastable Polymorph Structures by Machine Learning.

Despite vibrational properties being critical for the ab initio prediction of finite-temperature stability as well as thermal conductivity and other transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on 121 different mechanically stable structures of KZnF reaches a mean absolute error of 0.

AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis.

A priori prediction of phase stability of materials is a challenging practice, requiring knowledge of all energetically competing structures at formation conditions. Large materials repositories-housing properties of both experimental and hypothetical compounds-offer a path to prediction through the construction of informatics-based, ab initio phase diagrams. However, limited access to relevant data and software infrastructure has rendered thermodynamic characterizations largely peripheral, despite their continued success in dictating synthesizability.

AFLOW-SYM: platform for the complete, automatic and self-consistent symmetry analysis of crystals.

Determination of the symmetry profile of structures is a persistent challenge in materials science. Results often vary amongst standard packages, hindering autonomous materials development by requiring continuous user attention and educated guesses. This article presents a robust procedure for evaluating the complete suite of symmetry properties, featuring various representations for the point, factor and space groups, site symmetries and Wyckoff positions.

The Structure and Composition Statistics of 6A Binary and Ternary Crystalline Materials.

The fundamental principles underlying the arrangement of elements into solid compounds with an enormous variety of crystal structures are still largely unknown. This study presents a general overview of the structure types appearing in an important subset of the solid compounds, i.e.

Improved electronic structure and magnetic exchange interactions in transition metal oxides.

We discuss the application of the Agapito Curtarolo and Buongiorno Nardelli (ACBN0) pseudo-hybrid Hubbard density functional to several transition metal oxides. For simple binary metal oxides, ACBN0 is found to be a fast, reasonably accurate and parameter-free alternative to traditional DFT  +  U and hybrid exact exchange methods. In ACBN0, the Hubbard energy of DFT  +  U is calculated via the direct evaluation of the local Coulomb and exchange integrals in which the screening of the bare Coulomb potential is accounted for by a renormalization of the density matrix.

Universal fragment descriptors for predicting properties of inorganic crystals.

Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data.