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.

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.

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.

Here, we present a database of 69 840 largely novel covalent organic frameworks assembled in silico from 666 distinct organic linkers and four established synthetic routes. Due to their light weights and high internal surface areas, the frameworks are promising materials for methane storage applications. To assess their methane storage performance, we used grand-canonical Monte Carlo simulations to calculate their deliverable capacities. We demonstrate that the best structure, composed of carbon− carbon bonded triazine linkers in the tbd topology, has a predicted 65-bar deliverable capacity of 216 v STP/v, better than the best methane storage materials published to date. Using our approach, we also discovered other high-performing materials with 300 structures having calculated deliverable capacities greater than 190 v STP/v and 10% of these outperforming 200 v STP/v. To encourage screening studies of these materials for other applications, all structures and their properties were made available on the Materials Cloud.

Polycyclic hydrocarbons have received great attention due to their potential role in organic electronics and, for open-shell systems with unpaired electron densities, in spintronics and data storage. However, the intrinsic instability of polyradical hydrocarbons severely limits detailed investigations of their electronic structure. Here, we report the on-surface synthesis of conjugated polymers consisting of indeno[2,1-b]fluorene units, which are antiaromatic and open-shell biradicaloids. The observed reaction products, which also include a non-benzenoid porous ribbon arising from lateral fusion of unprotected indeno[2,1-b]fluorene chains, have 2 been characterized via low temperature scanning tunneling microscopy/spectroscopy and noncontact atomic force microscopy, complemented by density-functional theory calculations.These polymers present a low band gap when adsorbed on Au(111). Moreover, their pronounced antiaromaticity and radical character, elucidated by ab initio calculations, make them promising candidates for applications in electronics and spintronics. Further, they provide a rich playground to explore magnetism in low-dimensional organic nanomaterials.