Parametrically constrained geometry relaxations for high-throughput materials science

Reducing parameter spaces via exploiting symmetries has greatly accelerated and increased the quality of electronic-structure calculations. Unfortunately, many of the traditional methods fail when the global crystal symmetry is broken, even when the distortion is only a slight perturbation (e.g., Jahn-Teller like distortions). Here we introduce a flexible and generalizable parametric relaxation scheme and implement it in the all-electron code FHI-aims. This approach utilizes parametric constraints to maintain symmetry at any level. After demonstrating the method’s ability to relax metastable structures, we highlight its adaptability and performance over a test set of 359 materials, across 13 lattice prototypes. Finally we show how these constraints can reduce the number of steps needed to relax local lattice distortions by an order of magnitude. The flexibility of these constraints enables a significant acceleration of high-throughput searches for novel materials for numerous applications

Thermodynamic Equilibria in Carbon Nitride Photocatalyst Materials and Conditions for the Existence of Graphitic Carbon Nitride g-C3N4

We quantify the thermodynamic equilibrium conditions that govern the formation of crystalline heptazine-based carbon nitride materials, currently of enormous interest for photocatalytic applications including solar hydrogen evolution. Key phases studied include the monomeric phase melem, the 1D polymer melon, and the hypothetical hydrogen free 2D graphitic carbon nitride phase "g-C3N4". Our study is based on. density functional theory including van der Waals dispersion terms with different experimental conditions represented by the chemical potential of NH3. Graphitic carbon nitride is the subject of a vast number of studies, but its existence is still controversial. We show that typical conditions found in experiments pertain to the polymer melon (2D planes of 1D hydrogen-bonded polymer strands). In contrast, equilibrium synthesis of heptazine (h)-based g-h-C3N4 below its experimentally known decomposition temperature requires much less likely conditions, equivalent to low NH3 partial pressures around 1 Pa at 500 degrees C and around 10(3) Pa even at 700 degrees C. A recently reported synthesis of triazine (t)-based g-t-C3N4 in a salt melt is interpreted as a consequence of the altered local chemical environment of the C3N4 nanocrystallites

Computational polymorph screening reveals late-appearing and poorly-soluble form of rotigotine

The active pharmaceutical ingredient rotigotine—a dopamine agonist for the treatment of Parkinson’s and restless leg diseases—was known to exist in only one polymorphic form since 1985. In 2008, the appearance of a thermodynamically more stable and significantly less soluble polymorph led to a massive batch recall followed by economic and public health implications. Here, we carry out state-of-the-art computational crystal structure prediction, revealing the late-appearing polymorph without using any prior information. In addition, we predict a third crystalline form of rotigotine having thermodynamic stability between forms I and II. We provide quantitative description of the relative stability and solubility of the rotigotine polymorphs. Our study offers new insights into a challenging polymorphic system and highlights the robustness of contemporary computational crystal structure prediction during pharmaceutical development

All-electron formalism for total energy strain derivatives and stress tensor components for numeric atom-centered orbitals

We derive and implement the strain derivatives of the total energy of solids, i.e., the analytic stress tensor components, in an all-electron, numeric atom-centered orbital based density-functional formalism. We account for contributions that arise in the semilocal approximation (LDA/GGA) as well as in the generalized Kohn-Sham case, in which a fraction of exact exchange (hybrid functionals) is included. In this work, we discuss the details of the implementation including the numerical corrections for sparse integrations grids which allow to produce accurate results. We validate the implementation for a variety of test cases by comparing to strain derivatives performed via finite dierences. Additionally, we include the detailed definition of the overlapping atom-centered integration formalism used in this work to obtain total energies and their derivatives

A machine learning route between band mapping and band structure

The electronic band structure (BS) of solid state materials imprints the multidimensional and multi-valued functional relations between energy and momenta of periodically confined electrons. Photoemission spectroscopy is a powerful tool for its comprehensive characterization. A common task in photoemission band mapping is to recover the underlying quasiparticle dispersion, which we call band structure reconstruction. Traditional methods often focus on specific regions of interests yet require extensive human oversight. To cope with the growing size and scale of photoemission data, we develop a generic machine-learning approach leveraging the information within electronic structure calculations for this task. We demonstrate its capability by reconstructing all fourteen valence bands of tungsten diselenide and validate the accuracy on various synthetic data. The reconstruction uncovers previously inaccessible momentum-space structural information on both global and local scales in conjunction with theory, while realizing a path towards integrating band mapping data into materials science databases