CO2 is a critical pollutant of the atmosphere with noticeable climatic consequences. However, routes already exist to convert CO2 into useful chemicals or fuels. The key is catalysis – a process of accelerating desired chemical reactions, involving special materials (catalysts).

Using recently developed AI methods, NOMAD researchers have identified the materials genes important for CO2 activation and rules for finding improved or novel catalyst materials.

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The web interface of the Artificial-Intelligence (AI) Toolkit of the NOMAD Laboratory has been upgraded in terms of its functionalities and of its overall design. Under this new interface, users can learn about state-of-the-art AI methods, pertaining to materials science and engineering-related R&D challenges and applications, via the numerous available interactive tutorials. Furthermore, users can reproduce published results, and they can modify predefined Jupiter notebooks freely towards their own research goals.

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NOMAD CoE researchers from TU Wien and the Fritz Haber Institute have developed novel computer codes to enable massively parallel and highly accurate coupled cluster theory simulations of materials.

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The number of serious brain disorders and deaths worldwide caused by diseases of the nervous system has risen sharply in recent decades. Despite huge advances in neuroscience over the past century, our understanding of the brain is still far from complete. To understand the causes and to aid the growing number of affected people, we need to be able to study the brain more closely. New tailored sensors measuring small electromagnetic fluctuations produced by active neurons could contribute to rapidly developing treatments for brain disorders.

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The Atomic Simulation Recipes (ASR) is an open source Python framework for working with atomistic materials simulations in an efficient and sustainable way that is ideally suited for high-throughput computations. ASR contains a library of recipes, or high-level functions, that define specific atomistic simulations tasks using the Atomic Simulation Environment (ASE). The recipes can be combined into workflows that perform complex simulation tasks while keeping track of relevant metadata to ensure documentation and reproducibility of the data. The ASR also contains functionality for collecting the resulting data into databases and presenting them in a browser.

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The BiGmax Workshop 2022 on Big-Data-Driven Materials Science will be held at the University Conference Centre in Bochum from April 11 - 13, 2022.

The workshop is aimed at presenting results and new insights into data-driven materials science. Those can be based on approaches in statistical and machine learning, compressed sensing and other recent technologies from mathematics, computer science, statistics and information technology.

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The Open Databases Integration for Materials Design (OPTIMADE) consortium aims to make materials databases interoperable by developing a specification for a common REST API.

With the support of CECAM, the OPTIMADE consortium holds annual workshops to discuss and develop the specification and related ideas. The latest 2022 workshop will be held in the CECAM HQ at the EPFL in Lausanne, Switzerland, between May 30 and June 3, 2022.

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The central goal of the workshop is to discuss the state of the art and challenges of using quantum chemistry methods in materials science, to share the recent progresses in quantum chemistry, and to deepen the coalescence of two communities: molecular quantum-chemistry and solid state physics.

In addition, this workshop will focus on the computer implementation of massive parallel algorithms to perform quantum chemical calculations on modern supercomputers. This workshop will bring together some of the world's leading experts in the development of massively parallel algorithms for quantum chemistry calculations to foster cooperation and catalyze scientific software innovation.

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In close contact with academia and industry, the NOMAD CoE will develop exascale algorithms to build reliable models for realworld, industrially relevant, complex materials. We will provide exascale software and artificial intelligence (Al) methods that provide more efficient workflows and advanced modeling.

The NOMAD Midsummer School will feature 7 different sessions including tutorials and a hackathon on developments of exasca le solutions in Green function methods and advanced DFT. Additionally, there will be keynote talks by internationally renowned researchers.

WARNING: this event has unfortunately been canceled because of Corona and the sad war in the Ukraine. It will eventually be rescheduled next year.

This CECAM flagship workshop will focus on recent, important developments, addressing exascale computing and artificial intelligence (AI) methods with a specific focus on urgent topics of materials science.

The workshop hence aims at updating the audience about new and upcoming possibilities. For this, we will have lectures from top HPC centers from Europe, USA, Japan, and China. In particular, we will address how exascale computing can help materials modeling in terms of higher accuracy and precision and more controlled scale bridging. We will also discuss advancements of AI methods and exascale workflows.

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The “Workshop on HPC (High Performance Computing) co-design in computational materials and molecular science” will gather contributions from leading scientists, technologies, and SW engineers from the fields of  academia, HPC centres, HW-vendors, and industry. The workshop covers a broad range of current topics, taking materials and molecular science codes as reference HPC applications and possible co-design vehicles. 

The workshop will take place between October 3, 2022 and October 5, 2022, at the CECAM-HQ-EPFL, Lausanne, Switzerland.

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In order to harvest the richness contained in presently known and future materials-science data, four pillars need to be concurrently developed:

  • FAIR-compliant materials databases.
  • Identification of proper descriptors and metrics for capturing the similarity amongst materials.
  • Artificial-intelligence (AI) approaches for exploratory analysis.
  • Scalable implementations.
In this workshop, experts in all these aspects will interact to confront ideas and solutions for performing flexible, interactive, efficient, and insightful analyses of materials databases.

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Bringing eigensolvers and electronic-structure codes to exascale architectures requires further, considerable community efforts in the coming years. To facilitate and coordinate these efforts, this workshop aims at bringing together the key players from different fields.

The workshop will take place between November 16, 2022 and November 18, 2022, at the CECAM-HQ-EPFL, Lausanne, Switzerland.

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