Aliaksandr Yakutovich | EPFL Graph Search

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Common workflows for computing material properties using different quantum engines

Marnik Bercx, Kristjan Eimre, Sebastiaan Philippe Huber, Conrad Johnston, Nicola Marzari, Giovanni Pizzi, Samuel Poncé, Gian-Marco Rignanese, Christopher John Sewell, Berend Smit, Martin Uhrin, Aliaksandr Yakutovich, Austin Jacob Zadoks

The prediction of material properties based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use them. We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification. We introduce design rules for reusable, code-agnostic, workflow interfaces to compute well-defined material properties, which we implement for eleven quantum engines and use to compute various material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer’s expertise of the quantum engine directly available to non-experts. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.

2021

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

Casper Welzel Andersen, Andrea Cepellotti, Fernando Gargiulo, Sebastiaan Philippe Huber, Conrad Johnston, Leonid Kahle, Boris Kozinsky, Snehal Pramod Kumbhar, Nicola Marzari, Andrius Merkys, Nicolas Frank Mounet, Elsa Passaro, Giovanni Pizzi, Christoph Leopold Talirz, Martin Uhrin, Aliaksandr Yakutovich, Spyros Zoupanos

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.

2020

In Silico Discovery of Covalent Organic Frameworks for Carbon Capture

Daiane Damasceno Borges, Kathryn Suzanne Deeg, Johanna Maria Huck, Daniele Ongari, Nakul Rampal, Berend Smit, Christoph Leopold Talirz, Aliaksandr Yakutovich

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.

2020