GPAW: An open Python package for electronic structure calculations.

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for the implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE), providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe-Salpeter Equation, variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn-Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support for graphics processing unit (GPU) acceleration has been achieved with minor modifications to the GPAW code thanks to the CuPy library. We end the review with an outlook, describing some future plans for GPAW.

Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance.

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. We finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

Understanding the Electronic and Structural Effects in ORR Intermediate Binding on Anion-Substituted Zirconia Surfaces.

For oxygen reduction reaction (ORR), the surface adsorption energies of O and OH* intermediates are key descriptors for catalytic activity. In this work, we investigate anion-substituted zirconia catalyst surfaces and determine that adsorption energies of O and OH* intermediates is governed by both structural and electronic effects. When the adsorption energies are not influenced by the structural effects of the catalyst surface, they exhibit a linear correlation with integrated crystal orbital Hamiltonian population (ICOHP) of the adsorbate-surface bond. The influence of structural effects, due to the re-optimisation slab geometry after adsorption of intermediate species, leads to stronger adsorption of intermediates. Our calculations show that there is a change in the bond order to accommodate the incoming adsorbate species which leads to stronger adsorption when both structural and electronic effects influence the adsorption phenomena. The insights into the catalyst-adsorbate interactions can guide the design of future ORR catalysts.

Synergistic effects of mixing and strain in high entropy spinel oxides for oxygen evolution reaction.

Developing stable and efficient electrocatalysts is vital for boosting oxygen evolution reaction (OER) rates in sustainable hydrogen production. High-entropy oxides (HEOs) consist of five or more metal cations, providing opportunities to tune their catalytic properties toward high OER efficiency. This work combines theoretical and experimental studies to scrutinize the OER activity and stability for spinel-type HEOs. Density functional theory confirms that randomly mixed metal sites show thermodynamic stability, with intermediate adsorption energies displaying wider distributions due to mixing-induced equatorial strain in active metal-oxygen bonds. The rapid sol-flame method is employed to synthesize HEO, comprising five 3d-transition metal cations, which exhibits superior OER activity and durability under alkaline conditions, outperforming lower-entropy oxides, even with partial surface oxidations. The study highlights that the enhanced activity of HEO is primarily attributed to the mixing of multiple elements, leading to strain effects near the active site, as well as surface composition and coverage.

Autonomous intelligent agents for accelerated materials discovery.

We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments to carry out. In choosing next experiments, agents can make use of past knowledge, surrogate models, logic, thermodynamic or other physical constructs, heuristic rules, and different exploration-exploitation strategies. We show a series of examples for (i) how the discovery campaigns for finding materials satisfying a relative stability objective can be simulated to design new agents, and (ii) how those agents can be deployed in real discovery campaigns to control experiments run externally, such as the cloud-based density functional theory simulations in this work. In a sample set of 16 campaigns covering a range of binary and ternary chemistries including metal oxides, phosphides, sulfides and alloys, this autonomous platform found 383 new stable or nearly stable materials with no intervention by the researchers.

High-throughput calculations of catalytic properties of bimetallic alloy surfaces.

A comprehensive database of chemical properties on a vast set of transition metal surfaces has the potential to accelerate the discovery of novel catalytic materials for energy and industrial applications. In this data descriptor, we present such an extensive study of chemisorption properties of important adsorbates - e.g., C, O, N, H, S, CHx, OH, NH, and SH - on 2,035 bimetallic alloy surfaces in 5 different stoichiometric ratios, i.e., 0%, 25%, 50%, 75%, and 100%. To our knowledge, it is the first systematic study to compile the adsorption properties of such a well-defined, large chemical space of catalytic interest. We propose that a collection of catalytic properties of this magnitude can assist with the development of machine learning enabled surrogate models in theoretical catalysis research to design robust catalysts with high activity for challenging chemical transformations. This database is made publicly available through the platform www.Catalysis-hub.org for easy retrieval of the data for further scientific analysis.

Catalysis-Hub.org, an open electronic structure database for surface reactions.

We present a new open repository for chemical reactions on catalytic surfaces, available at https://www.catalysis-hub.org . The featured database for surface reactions contains more than 100,000 chemisorption and reaction energies obtained from electronic structure calculations, and is continuously being updated with new datasets. In addition to providing quantum-mechanical results for a broad range of reactions and surfaces from different publications, the database features a systematic, large-scale study of chemical adsorption and hydrogenation on bimetallic alloy surfaces. The database contains reaction specific information, such as the surface composition and reaction energy for each reaction, as well as the surface geometries and calculational parameters, essential for data reproducibility. By providing direct access via the web-interface as well as a Python API, we seek to accelerate the discovery of catalytic materials for sustainable energy applications by enabling researchers to efficiently use the data as a basis for new calculations and model generation.

Graph Theory Approach to High-Throughput Surface Adsorption Structure Generation.

We present a methodology for graph based enumeration of surfaces and unique chemical adsorption structures bonded to those surfaces. Utilizing the graph produced from a bulk structure, we create a unique graph representation for any general slab cleave and further extend that representation to include a large variety of catalytically relevant adsorbed molecules. We also demonstrate simple geometric procedures to generate 3D initial guesses of these enumerated structures. While generally useful for generating a wide variety of structures used in computational surface science and heterogeneous catalysis, these techniques are also key to facilitating an informatics approach to the high-throughput search for more effective catalysts.

Local Plasmon Engineering in Doped Graphene.

Single-atom B or N substitutional doping in single-layer suspended graphene, realized by low-energy ion implantation, is shown to induce a dampening or enhancement of the characteristic interband π plasmon of graphene through a high-resolution electron energy loss spectroscopy study using scanning transmission electron microscopy. A relative 16% decrease or 20% increase in the π plasmon quality factor is attributed to the presence of a single substitutional B or N atom dopant, respectively. This modification is in both cases shown to be relatively localized, with data suggesting the plasmonic response tailoring can no longer be detected within experimental uncertainties beyond a distance of approximately 1 nm from the dopant. Ab initio calculations confirm the trends observed experimentally. Our results directly confirm the possibility of tailoring the plasmonic properties of graphene in the ultraviolet waveband at the atomic scale, a crucial step in the quest for utilizing graphene's properties toward the development of plasmonic and optoelectronic devices operating at ultraviolet frequencies.

Interlayer Excitons and Band Alignment in MoS2/hBN/WSe2 van der Waals Heterostructures.

van der Waals heterostructures (vdWH) are ideal systems for exploring light-matter interactions at the atomic scale. In particular, structures with a type-II band alignment can yield detailed insight into carrier-photon conversion processes, which are central to, for example, solar cells and light-emitting diodes. An important first step in describing such processes is to obtain the energies of the interlayer exciton states existing at the interface. Here we present a general first-principles method to compute the electronic quasi-particle (QP) band structure and excitonic binding energies of incommensurate vdWHs. The method combines our quantum electrostatic heterostructure (QEH) model for obtaining the dielectric function with the many-body GW approximation and a generalized 2D Mott-Wannier exciton model. We calculate the level alignment together with intra- and interlayer exciton binding energies of bilayer MoS2/WSe2 with and without intercalated hBN layers, finding excellent agreement with experimental photoluminescence spectra. A comparison to density functional theory calculations demonstrates the crucial role of self-energy and electron-hole interaction effects.

An excess of chromosome 1 breakpoints in male infertility.

In a search for potential infertility loci, which might be revealed by clustering of chromosomal breakpoints, we compiled 464 infertile males with a balanced rearrangement from Mendelian Cytogenetics Network database (MCNdb) and compared their karyotypes with those of a Danish nation-wide cohort. We excluded Robertsonian translocations, rearrangements involving sex chromosomes and common variants. We identified 10 autosomal bands, five of which were on chromosome 1, with a large excess of breakpoints in the infertility group. Some of these could potentially harbour a male-specific infertility locus. However, a general excess of breakpoints almost everywhere on chromosome 1 was observed among the infertile males: 26.5 versus 14.5% in the cohort. This excess was observed both for translocation and inversion carriers, especially pericentric inversions, both for published and unpublished cases, and was significantly associated with azoospermia. The largest number of breakpoints was reported in 1q21; FISH mapping of four of these breakpoints revealed that they did not involve the same region at the molecular level. We suggest that chromosome 1 harbours a critical domain whose integrity is essential for male fertility.