High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation.

Quantum chemical calculations on atomistic systems have evolved into a standard approach to studying molecular matter. These calculations often involve a significant amount of manual input and expertise, although most of this effort could be automated, which would alleviate the need for expertise in software and hardware accessibility. Here, we present the AutoRXN workflow, an automated workflow for exploratory high-throughput electronic structure calculations of molecular systems, in which (i) density functional theory methods are exploited to deliver minimum and transition-state structures and corresponding energies and properties, (ii) coupled cluster calculations are then launched for optimized structures to provide more accurate energy and property estimates, and (iii) multi-reference diagnostics are evaluated to back check the coupled cluster results and subject them to automated multi-configurational calculations for potential multi-configurational cases. All calculations are carried out in a cloud environment and support massive computational campaigns. Key features of all components of the AutoRXN workflow are autonomy, stability, and minimum operator interference. We highlight the AutoRXN workflow with the example of an autonomous reaction mechanism exploration of the mode of action of a homogeneous catalyst for the asymmetric reduction of ketones.

Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF.

The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of Al-PMOF (Al2(OH)2TCPP) [H2TCPP = meso-tetra(4-carboxyphenyl)porphine], a promising material for carbon capture applications. Al-PMOF was previously synthesized using a hydrothermal reaction, which gave a low throughput yield due to its relatively long reaction time (16 hours). Here, we use a genetic algorithm to carry out a systematic search for the optimal synthesis conditions and a microwave-based high-throughput robotic platform for the syntheses. We show that, in just two generations, we could obtain excellent crystallinity and yield close to 80% in a much shorter reaction time (50 minutes). Moreover, by analyzing the failed and partially successful experiments, we could identify the most important experimental variables that determine the crystallinity and yield.

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. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution.

Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials' performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporous crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications.

AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance.

The ever-growing availability of computing power and the sustained development of advanced computational methods have contributed much to recent scientific progress. These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial. In recent years, we have been developing AiiDA (aiida.net), a robust open-source high-throughput infrastructure addressing the challenges arising from the needs of automated workflow management and data provenance recording. Here, we introduce developments and capabilities required to reach sustained performance, with AiiDA supporting throughputs of tens of thousands processes/hour, while automatically preserving and storing the full data provenance in a relational database making it queryable and traversable, thus enabling high-performance data analytics. AiiDA's workflow language provides advanced automation, error handling features and a flexible plugin model to allow interfacing with external simulation software. The associated plugin registry enables seamless sharing of extensions, empowering a vibrant user community dedicated to making simulations more robust, user-friendly and reproducible.

Materials Cloud, a platform for open computational science.

Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts (1) archival and dissemination services for raw and curated data, together with their provenance graph, (2) modelling services and virtual machines, (3) tools for data analytics, and pre-/post-processing, and (4) educational materials. Data is citable and archived persistently, providing a comprehensive embodiment of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow retracing and reproducing any computed result. When an AiiDA database is shared on Materials Cloud, peers can browse the interconnected record of simulations, download individual files or the full database, and start their research from the results of the original authors. The infrastructure is agnostic to the specific simulation codes used and can support diverse applications in computational science that transcend its initial materials domain.

In Silico Discovery of Covalent Organic Frameworks for Carbon Capture.

We screen a database of more than 69 000 hypothetical covalent organic frameworks (COFs) for carbon capture using parasitic energy as a metric. To compute CO2-framework interactions in molecular simulations, we develop a genetic algorithm to tune the charge equilibration method and derive accurate framework partial charges. Nearly 400 COFs are identified with parasitic energy lower than that of an amine scrubbing process using monoethanolamine; more than 70 are better performers than the best experimental COFs and several perform similarly to Mg-MOF-74. We analyze the effect of pore topology on carbon capture performance to guide the development of improved carbon capture materials.

Building a Consistent and Reproducible Database for Adsorption Evaluation in Covalent-Organic Frameworks.

We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent-organic frameworks (COFs) reported in the literature, to characterize their CO2 adsorption properties using the following steps: (1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory, (2) fitting atomic point charges based on the electron density, (3) characterizing the pore geometry of the structures before and after optimization, (4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally, (5) assessing the CO2 parasitic energy via process modeling. The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.

Band Gap of Atomically Precise Graphene Nanoribbons as a Function of Ribbon Length and Termination.

We study the band gap of finite N A = 7 armchair graphene nanoribbons (7-AGNRs) on Au(111) through scanning tunneling microscopy/spectroscopy combined with density functional theory calculations. The band gap of 7-AGNRs with lengths of 8 nm and more is converged to within 50 meV of its bulk value of ≈ 2 . 3 eV , while the band gap opens by several hundred meV in very short 7-AGNRs. We demonstrate that even an atomic defect, such as the addition of one hydrogen atom at the termini, has a significant effect - in this case, lowering the band gap. The effect can be captured in terms of a simple analytical model by introducing an effective "electronic length".

Capturing chemical intuition in synthesis of metal-organic frameworks.

We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal-organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.

Toward GW Calculations on Thousands of Atoms.

The GW approximation of many-body perturbation theory is an accurate method for computing electron addition and removal energies of molecules and solids. In a canonical implementation, however, its computational cost is [Formula: see text] in the system size N, which prohibits its application to many systems of interest. We present a full-frequency GW algorithm in a Gaussian-type basis, whose computational cost scales with N2 to N3. The implementation is optimized for massively parallel execution on state-of-the-art supercomputers and is suitable for nanostructures and molecules in the gas, liquid or condensed phase, using either pseudopotentials or all electrons. We validate the accuracy of the algorithm on the GW100 molecular test set, finding mean absolute deviations of 35 meV for ionization potentials and 27 meV for electron affinities. Furthermore, we study the length-dependence of quasiparticle energies in armchair graphene nanoribbons of up to 1734 atoms in size, and compute the local density of states across a nanoscale heterojunction.

On-Surface Synthesis and Characterization of 9-Atom Wide Armchair Graphene Nanoribbons.

The bottom-up approach to synthesize graphene nanoribbons strives not only to introduce a band gap into the electronic structure of graphene but also to accurately tune its value by designing both the width and edge structure of the ribbons with atomic precision. We report the synthesis of an armchair graphene nanoribbon with a width of nine carbon atoms on Au(111) through surface-assisted aryl-aryl coupling and subsequent cyclodehydrogenation of a properly chosen molecular precursor. By combining high-resolution atomic force microscopy, scanning tunneling microscopy, and Raman spectroscopy, we demonstrate that the atomic structure of the fabricated ribbons is exactly as designed. Angle-resolved photoemission spectroscopy and Fourier-transformed scanning tunneling spectroscopy reveal an electronic band gap of 1.4 eV and effective masses of ≈0.1 me for both electrons and holes, constituting a substantial improvement over previous efforts toward the development of transistor applications. We use ab initio calculations to gain insight into the dependence of the Raman spectra on excitation wavelength as well as to rationalize the symmetry-dependent contribution of the ribbons' electronic states to the tunneling current. We propose a simple rule for the visibility of frontier electronic bands of armchair graphene nanoribbons in scanning tunneling spectroscopy.

Giant edge state splitting at atomically precise graphene zigzag edges.

Zigzag edges of graphene nanostructures host localized electronic states that are predicted to be spin-polarized. However, these edge states are highly susceptible to edge roughness and interaction with a supporting substrate, complicating the study of their intrinsic electronic and magnetic structure. Here, we focus on atomically precise graphene nanoribbons whose two short zigzag edges host exactly one localized electron each. Using the tip of a scanning tunnelling microscope, the graphene nanoribbons are transferred from the metallic growth substrate onto insulating islands of NaCl in order to decouple their electronic structure from the metal. The absence of charge transfer and hybridization with the substrate is confirmed by scanning tunnelling spectroscopy, which reveals a pair of occupied/unoccupied edge states. Their large energy splitting of 1.9 eV is in accordance with ab initio many-body perturbation theory calculations and reflects the dominant role of electron-electron interactions in these localized states.

On-surface synthesis of graphene nanoribbons with zigzag edge topology.

Graphene-based nanostructures exhibit electronic properties that are not present in extended graphene. For example, quantum confinement in carbon nanotubes and armchair graphene nanoribbons leads to the opening of substantial electronic bandgaps that are directly linked to their structural boundary conditions. Nanostructures with zigzag edges are expected to host spin-polarized electronic edge states and can thus serve as key elements for graphene-based spintronics. The edge states of zigzag graphene nanoribbons (ZGNRs) are predicted to couple ferromagnetically along the edge and antiferromagnetically between the edges, but direct observation of spin-polarized edge states for zigzag edge topologies--including ZGNRs--has not yet been achieved owing to the limited precision of current top-down approaches. Here we describe the bottom-up synthesis of ZGNRs through surface-assisted polymerization and cyclodehydrogenation of specifically designed precursor monomers to yield atomically precise zigzag edges. Using scanning tunnelling spectroscopy we show the existence of edge-localized states with large energy splittings. We expect that the availability of ZGNRs will enable the characterization of their predicted spin-related properties, such as spin confinement and filtering, and will ultimately add the spin degree of freedom to graphene-based circuitry.

On-Surface Synthesis of Atomically Precise Graphene Nanoribbons.

The surface-assisted polymerization and cyclodehydrogenation of specifically designed organic precursors provides a route toward atomically precise graphene nanoribbons, which promises to combine the outstanding electronic properties of graphene with a bandgap that is sufficiently large for room-temperature digital-logic applications. Starting from the basic concepts behind the on-surface synthesis approach, this report covers the progress made in understanding the different reaction steps, in synthesizing atomically precise graphene nanoribbons of various widths and edge structures, and in characterizing their properties, ending with an outlook on the challenges that still lie ahead.

On-Surface Synthesis of BN-Substituted Heteroaromatic Networks.

We report on the bottom-up fabrication of BN-substituted heteroaromatic networks achieved by surface-assisted polymerization and subsequent cyclodehydrogenation of specifically designed BN-substituted precursor monomers based on a borazine core structural element. To get insight into the cyclodehydrogenation pathway and the influence of molecular flexibility on network quality, two closely related precursor monomers with different degrees of internal cyclodehydrogenation have been employed. Scanning tunneling microscopy shows that, for both monomers, surface-assisted cyclodehydrogenation allows for complete monomer cyclization and the formation of covalently interlinked BN-substituted polyaromatic hydrocarbon networks on the Ag(111) surface. In agreement with experimental observations, density functional theory calculations reveal a significantly lower energy barrier for the cyclodehydrogenation of the conformationally more rigid precursor monomer, which is also reflected in a higher degree of long-range order of the obtained heteroaromatic network. Our proof-of-concept study will allow for the fabrication of atomically precise substitution patterns within BNC heterostructures.

Graphene nanoribbon heterojunctions.

Despite graphene's remarkable electronic properties, the lack of an electronic bandgap severely limits its potential for applications in digital electronics. In contrast to extended films, narrow strips of graphene (called graphene nanoribbons) are semiconductors through quantum confinement, with a bandgap that can be tuned as a function of the nanoribbon width and edge structure. Atomically precise graphene nanoribbons can be obtained via a bottom-up approach based on the surface-assisted assembly of molecular precursors. Here we report the fabrication of graphene nanoribbon heterojunctions and heterostructures by combining pristine hydrocarbon precursors with their nitrogen-substituted equivalents. Using scanning probe methods, we show that the resulting heterostructures consist of seamlessly assembled segments of pristine (undoped) graphene nanoribbons (p-GNRs) and deterministically nitrogen-doped graphene nanoribbons (N-GNRs), and behave similarly to traditional p-n junctions. With a band shift of 0.5 eV and an electric field of 2 × 10(8) V m(-1) at the heterojunction, these materials bear a high potential for applications in photovoltaics and electronics.

Termini of bottom-up fabricated graphene nanoribbons.

Atomically precise graphene nanoribbons (GNRs) can be obtained via thermally induced polymerization of suitable precursor molecules on a metal surface. This communication discusses the atomic structure found at the termini of armchair GNRs obtained via this bottom-up approach. The short zigzag edge at the termini of the GNRs under study gives rise to a localized midgap state with a characteristic signature in scanning tunneling microscopy (STM). By combining STM experiments with large-scale density functional theory calculations, we demonstrate that the termini are passivated by hydrogen. Our results suggest that the length of nanoribbons grown by this protocol may be limited by hydrogen passivation during the polymerization step.