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    About MAX

    MAX (MAterials design at the eXascale) is a European Centre of Excellence which enables materials modelling, simulations, discovery and design at the frontiers of the current and future High Performance Computing (HPC), High Throughput Computing (HTC) and data analytics technologies.

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    The software developed by MAX is made available to the whole community in open-source form. In this section you can find our main software output and how to obtain it.
     

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    MAX addresses the challenges of porting, scaling, and optimising material science application codes for the peta- and exascale platforms in order to deliver best code performance and improve users productivity on the upcoming architectures.

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    DATA

    MAX is committed in supporting data stewardship by adhering to the FAIR-sharing principles. High-quality data is provided both in the format of curated scientific results and raw data, focusing on the tracking of provenance to ensure the full reproducibility of results.

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    MAX develops and offers services and technical support dedicated to the general public and the expert users from both industry and academia.

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    MAX offers integrated training and education in the field of HPC developments and in the computational materials science domain, including workshops and schools, contributions to University courses and training through research in the CoE labs.

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    SCIENCE & MAX

    MAX codes enable about 3500 publications throughout the globe every year. A selection of the scientific papers produced by the community using the MAX flagship codes can be found in this area. “Highlights” contains the publications produced by MAX consortium members to develop and validate MAX codes towards the exascale (red frame) and a subset of the scientific works produced by the materials science community (gray frame). “Lists of publications” collects selected papers produced by the scientific community at large and using MAX flagship codes.

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May 10, 2022

Young Researcher's Workshop on Machine Learning for Materials 2022

Event: Young Researcher's Workshop on Machine Learning for Materials 2022 Where: SISSA Miramare...


May 9, 2022

Young Researcher's Workshop on Machine Learning for Materials 2022

MaX is committed to the enlargement of the users community and to the development of new...


May 9, 2022

Exploring the robust extrapolation of high-dimensional machine learning potentials

C. Zeni, A. Anelli, A. Glielmo, and K. Rossi


May 9, 2022

Exploring the robust extrapolation of high-dimensional machine learning potentials

In this article appearing on Physical Reviews B, an international team comprised of young researchers from Italy and Switzerland show how, contrary to popular assumptions, predictions from machine learning potentials almost exclusively occur in an extrapolation regime.


April 26, 2022

Excitonic effects in graphene-like C3N

Carbon nitrides are gaining growing attention in recent years: they are metal-free carbon-based...


April 13, 2022

Flexibilities of wavelets as a computational basis set for large-scale electronic structure calculations

L. E. Ratcliff, W. Dawson, G. Fisicaro, D. Caliste, S. Mohr, A. Degomme, B. Videau, V. Cristiglio,...


April 13, 2022

Electronic-structure methods for materials design

N. Marzari, A. Ferretti, and C. Wolverton


April 13, 2022

Common workflows for computing material properties using different quantum engines

S. P. Huber, E. Bosoni, M. Bercx, J. Bröder, A. Degomme, V. Dikan, K. Eimre, E. Flage-Larsen, A...


April 13, 2022

Thermal and Tidal Evolution of Uranus with a Growing Frozen Core

L. Stixrude, S. Baroni, and F. Grasselli


April 13, 2022

Interference effects in one-dimensional moiré crystals

N. Wittemeier, M. J. Verstraete, P. Ordejón, and Z. Zanolli


April 13, 2022

Data-driven simulation and characterisation of gold nanoparticle melting

C. Zeni, K. Rossi, T. Pavloudis, J. Kioseoglou, S. de Gironcoli, R. E. Palmer, and F. Baletto


April 13, 2022

Validity of the on-site spin-orbit coupling approximation

R. Cuadrado, R. Robles, A. García, M. Pruneda, P. Ordejón, J. Ferrer, and Jorge I. Cerdá


April 13, 2022

Compact atomic descriptors enable accurate predictions via linear models

C. Zeni, K. Rossi, A. Glielmo, and S. de Gironcoli


April 13, 2022

Spinorial formulation of the GW -BSE equations and spin properties of excitons in two-dimensional transition metal dichalcogenides

M. Marsili, A. Molina-Sánchez, M. Palummo, D. Sangalli, and A. Marini


April 13, 2022

Gap Opening in Double-Sided Highly Hydrogenated Free-Standing Graphene

M. G. Betti, E. Placidi, C. Izzo, E. Blundo, A. Polimeni, M. Sbroscia, J. Avila, P. Dudin, K. Hu, Y...


April 13, 2022

Excitonic effects in graphene-like C3N

M. Bonacci, M. Zanfrognini, E. Molinari, A. Ruini, M. J. Caldas, A. Ferretti, and D. Varsano


April 11, 2022

Co-Design for HPC in Computational Materials and Molecular Science

The Workshop on HPC (High Performance Computing) Co-design in Computational Materials and Molecular...


March 14, 2022

Evidence for equilibrium exciton condensation in monolayer WTe2

B. Sun, W. Zhao, T. Palomaki, Z. Fei, E. Runburg , P. Malinowski, X. Huang, J. Cenker, Y-T. Cui, J...


March 10, 2022

MaX at EuroHPC Summit Week 2022 in Paris

The EuroHPC Summit Week (EHPCSW) 2022 will take place in person from 22nd to 24th of March in Paris...


March 2, 2022

Temperature- and vacancy-concentration-dependence of heat transport in Li3ClO from multi-method numerical simulations

Thermal transport in Solid State Electrolytes (SSEs) is of paramount importance for the industrial...


February 23, 2022

Complexity Reduction in Density Functional Theory Calculations of Large Systems: System Partitioning and Fragment Embedding

W. Dawson, S. Mohr, L. E. Ratcliff, T. Nakajima, and L. Genovese


February 15, 2022

Wannier 2022 Summer School

This 5-day school (May 16-20, 2022) is designed to allow participants to join both in-person and...


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© Copyright 2019 – MaX - Materials design at the Exascale has received funding from the European Union’s Horizon 2020 - Research and Innovation program - under grant agreement no. 824143. The content of this document does not represent the opinion of the European Union, and the European Union is not responsible for any use that might be made of such content.