Sampling the Materials Space for Conventional Superconducting Compounds.

A large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow is performed, starting by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with reasonable convergence parameters. This dataset is then used to train a robust machine learning model capable of predicting the electron-phonon and superconducting properties based on structural, compositional, and electronic ground-state properties. Using this machine, the transition temperatures (Tc ) of approximately 200 000 metallic compounds are evaluated, all of which are on the convex hull of thermodynamic stability (or close to it) to maximize the probability of synthesizability. Compounds predicted to have Tc values exceeding 5 K are further validated using density-functional perturbation theory. As a result, 541 compounds with Tc values surpassing 10 K, encompassing a variety of crystal structures and chemical compositions, are identified. This work is complemented with a detailed examination of several interesting materials, including nitrides, hydrides, and intermetallic compounds. Particularly noteworthy is LiMoN2 , which is predicted to be superconducting in the stoichiometric trigonal phase, with a Tc exceeding 38 K. LiMoN2 has previously been synthesized in this phase, further heightening its potential for practical applications.

Machine-Learning-Assisted Determination of the Global Zero-Temperature Phase Diagram of Materials.

Crystal-graph attention neural networks have emerged recently as remarkable tools for the prediction of thermodynamic stability. The efficacy of their learning capabilities and their reliability is however subject to the quantity and quality of the data they are fed. Previous networks exhibit strong biases due to the inhomogeneity of the training data. Here a high-quality dataset is engineered to provide a better balance across chemical and crystal-symmetry space. Crystal-graph neural networks trained with this dataset show unprecedented generalization accuracy. Such networks are applied to perform machine-learning-assisted high-throughput searches of stable materials, spanning 1 billion candidates. In this way, the number of vertices of the global T = 0 K phase diagram is increased by 30% and find more than ≈150 000 compounds with a distance to the convex hull of stability of less than 50 meV atom-1 . The discovered materials are then accessed for applications, identifying compounds with extreme values of a few properties, such as superconductivity, superhardness, and giant gap-deformation potentials.

A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals.

In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE) functional of density-functional theory, a well established and reliable technique that is by now the standard in materials science. However, there have been recent theoretical developments that allow for increased accuracy in the calculations. Here, we present a dataset of calculations for 175k crystalline materials obtained with two functionals: geometry optimizations are performed with PBE for solids (PBEsol) that yields consistently better geometries than the PBE functional, and energies are obtained from PBEsol and from SCAN single-point calculations at the PBEsol geometry. Our results provide an accurate overview of the landscape of stable (and nearly stable) materials, and as such can be used for reliable predictions of novel compounds. They can also be used for training machine learning models, or even for the comparison and benchmark of PBE, PBEsol, and SCAN.

High-throughput search of ternary chalcogenides for p-type transparent electrodes.

Delafossite crystals are fascinating ternary oxides that have demonstrated transparent conductivity and ambipolar doping. Here we use a high-throughput approach based on density functional theory to find delafossite and related layered phases of composition ABX2, where A and B are elements of the periodic table, and X is a chalcogen (O, S, Se, and Te). From the 15 624 compounds studied in the trigonal delafossite prototype structure, 285 are within 50 meV/atom from the convex hull of stability. These compounds are further investigated using global structural prediction methods to obtain their lowest-energy crystal structure. We find 79 systems not present in the materials project database that are thermodynamically stable and crystallize in the delafossite or in closely related structures. These novel phases are then characterized by calculating their band gaps and hole effective masses. This characterization unveils a large diversity of properties, ranging from normal metals, magnetic metals, and some candidate compounds for p-type transparent electrodes.

Nanostructured water and carbon dioxide inside collapsing carbon nanotubes at high pressure.

We present simulations of the collapse under hydrostatic pressure of carbon nanotubes containing either water or carbon dioxide. We show that the molecules inside the tube alter the dynamics of the collapse process, providing either mechanical support and increasing the collapse pressure, or reducing mechanical stability. At the same time the nanotube acts as a nanoanvil, and the confinement leads to the nanostructuring of the molecules inside the collapsed tube. In this way, depending on the pressure and on the concentration of water or carbon dioxide inside the nanotube, we observe the formation of 1D molecular chains, 2D nanoribbons, and even molecular single and multi-walled nanotubes. The structure of the encapsulated molecules correlates with the mechanical response of the nanotube, opening up opportunities for the development of new devices or composite materials. Our analysis is quite general and it can be extended to other molecules in carbon nanotube nanoanvils, providing a strategy to obtain a variety of nano-objects with controlled features.

Materials Design On-the-Fly.

The dream of any solid-state theorist is to be able to predict new materials with tailored properties from scratch, i.e., without any input from experiment. Over the past decades, we have steadily approached this goal. Recent developments in the field of high-throughput calculations focused on finding the best material for specific applications. However, a key input for these techniques still had to be obtained experimentally, namely, the crystal structure of the materials. Here, we give a step further and show that one can indeed optimize material properties using as a single starting point the knowledge of the periodic table and the fundamental laws of quantum mechanics. This is done by combining state-of-the-art methods of global structure prediction that allow us to obtain the ground-state crystal structure of arbitrary materials, with an evolutionary algorithm that optimizes the chemical composition for the desired property. As a first showcase demonstration of our method, we perform an unbiased search for superhard materials and for transparent conductors. We stress that our method is completely general and can be used to optimize any property (or combination of properties) that can be calculated in a computer.

Novel phases of lithium-aluminum binaries from first-principles structural search.

Intermetallic Li-Al compounds are on the one hand key materials for light-weight engineering, and on the other hand, they have been proposed for high-capacity electrodes for Li batteries. We determine from first-principles the phase diagram of Li-Al binary crystals using the minima hopping structural prediction method. Beside reproducing the experimentally reported phases (LiAl, Li3Al2, Li9Al4, LiAl3, and Li2Al), we unveil a structural variety larger than expected by discovering six unreported binary phases likely to be thermodynamically stable. Finally, we discuss the behavior of the elastic constants and of the electric potential profile of all Li-Al stable compounds as a function of their stoichiometry.

Benchmarking the AK13 Exchange Functional: Ionization Potentials and Electron Affinities.

We perform benchmark calculations for the ionization potential and electronic affinity of atoms and small molecules using several semilocal exchange-correlation functionals of density-functional theory with improved asymptotic behavior. We are particularly interested in a new generalized-gradient approximation for exchange [Armiento and Kümmel, Phys. Rev. Lett. 2013, 111, 036402] that provides an energy functional whose functional derivative yields a potential with better decay behavior. We find that it yields energies that are worse than traditional energy functionals and potentials that are less accurate than functionals that model directly the exchange-correlation potential. However, we find that this functional offers a excellent balance between the quality of the energy and of the potential and is therefore a good compromise for applications that require at the same time reasonable energies and good potentials.