Hybrid Parallelization and Performance Optimization of the FLEUR Code: New Possibilities for All-Electron Density Functional Theory

U. Alekseeva, G. Michalicek, D. Wortmann, S. Blügel

In: Aldinucci M., Padovani L., Torquati M. (eds) Euro-Par 2018: Parallel Processing. Euro-Par 2018.

Lecture Notes in Computer Science, 2018, 11014. DOI: 10.1007/978-3-319-96983-1_52

A Massively Parallel Algorithm for the Approximate Calculation of Inverse p-th Roots of Large Sparse Matrices

M. Lass, S. Mohr, H. Wiebeler, R. D. Kuhne, C. Plessl,

Conference Proceedings PASC18, 2018 DOI: 10.1145/3218176.3218231

Ab initio study of electron-phonon coupling in rubrene

P. Ordejon, D. Boskovic, M. Panhans, F. Ortmann.

Physical Review B, 2017, 96, 035202 DOI: 10.1103/PhysRevB.96.035202

Accurate tight-binding Hamiltonians for two-dimensional and layered materials

L. A. Agapito, M. Fornari, D. Ceresoli, A. Ferretti, S. Curtarolo, M. Buongiorno Nardelli

Phys. Rev. B, 2016,93, 125137 DOI: 10.1103/PhysRevB.93.125137

AiiDA: automated interactive infrastructure and database for computational science

G. Pizzi, A. Cepellotti, R. Sabatini, N. Marzari, B. Kozinsky

Computational Materials Science, 2016,111, 218 125137 DOI:10.1016/j.commatsci.2015.09.013

Band structure diagram paths based on crystallography

Y. Hinuma, G. Pizzi, Y. Kumagai, F. Oba, I. Tanaka

Computational Materials Science, 2017, 128, 140-184 DOI:10.1016/j.commatsci.2016.10.015

Designing Materials with High-Performance Computing

G. Chiarotti, LinkedIn Pulse 2016

Yambo: a general purpose tool for theoretical spectroscopy

D. Varsano, LinkedIn 2017

Transferable Machine-Learning Model of the Electron Density

A. Grisafi, A. Fabrizio, B. Meyer, D. M. Wilkins, C. Corminboeuf, and M. Ceriotti

ACS Cent. Sci., 5 (1), 57–64 (2019) DOI: 10.1021/acscentsci.8b00551

Insights from Optimized Codes on Cineca’s Marconi

interview by C. Cavazzoni and A. Ferretti 

HPC Wire 15/02/2019

Hybrid quantum anomalous Hall effect at graphene-oxide interfaces

Z. Zanolli, C. Niu, G. Bihlmayer, Y. Mokrousov, P. Mavropoulos, M. J. Verstraete, and S. Blügel
Phys. Rev. B 98, 155404 (2018) DOI: 10.1103/PhysRevB.00.005400, 5/10/2018

Open Science Platform for Materials Science: AiiDA and the Materials Cloud

invited talk by G. Pizzi

Open Science Days 2019 (2019)

Designing a bioremediator: mechanistic models guide cellular and molecular specialization

M. Zaccaria, W. Dawson, V. Cristiglio, M. Reverberi, L. E. Ratcliff, T. Nakajima, L. Genovese, and B. Momeni

Science Direct, Volume 62, 98-105 (2020) DOI: 10.1016/j.copbio.2019.09.006

DFT study of graphene doping due to metal contacts

P. Khakbaz , F. Driussi , A. Gambi , P. Giannozzi , S. Venica, D. Esseni, A. Gahoi, S. Kataria, and M.C. Lemme

International Conference on Simulation of Semiconductor Processes and Devices - SISPAD (2019) DOI: 10.1109/SISPAD.2019.8870456

Improved understanding of metal–graphene contacts

F. Driussi, S. Venica, A. Gahoi, A. Gambi, P. Giannozzi, S. Kataria, M.C. Lemme, P. Palestri, and D. Esseni

Microelectronic Engineering 216, 111035 (2019) DOI: 10.1016/j.mee.2019.111035

CP2K: An Electronic Structure and Molecular Dynamics Software Package I. Quickstep: Efficient and Accurate Electronic Structure Calculations

T. D. Kühne, M. Iannuzzi, M. Del Ben, V. V. Rybkin, P. Seewald, F. Stein, T. Laino, R. Z. Khaliullin, O. Schütt, F. Schiffmann, D. Golze, J. Wilhelm, S. Chulkov, M. Hossein Bani-Hashemian, V. Weber, U. Borstnik, M. Taillefumier, A. S. Jakobovits, A. Lazzaro, H. Pabst, T. Müller, R. Schade, M. Guidon, S. Andermatt, N. Holmberg, G. K. Schenter, A. Hehn, A. Bussy, F. Belleflamme, G. Tabacchi, A. Glöß, M. Lass, I. Bethune, C. J. Mundy, C. Plessl, M. Watkins, J. VandeVondele, M. Krack, and J. Hutter

Materials Informatics: Overview

N. Marzari

Handbook of Materials Modeling : Methods: Theory and Modeling, W. Andreoni and S. Yip, eds. (Springer, Cham, 2019), 1–7 DOI: 10.1007/978-3-319-42913-7_92-1

Open-Science Platform for Computational Materials Science: AiiDA and the Materials Cloud

G. Pizzi

Handbook of Materials Modeling : Methods: Theory and Modeling, W. Andreoni and S. Yip, eds. (Springer, Cham, 2018), pp. 1–24 DOI: 10.1007/978-3-319-42913-7_64-1

Provenance, workflows, and crystallographic tools in materials science: AiiDA, spglib, and seekpath

G. Pizzi, A. Togo, and B. Kozinsky

MRS Bulletin 43, 696 (2018) DOI: 10.1557/mrs.2018.203

Koopmans compliance: a functional theory for spectral properties

A. Ferretti, N. Colonna, N. Nguyen, and N. Marzari

APS March Meeting 2019, abstract id.S18.009

Gauge optimization of time series for thermal-transport simulations

A. Marcolongo, L. Ercole, and S. Baroni

J. Chem. Theory Comput. 16, 5, 3352–3362 (2020) DOI:10.1021/acs.jctc.9b01174

Wannier90 as a community code: new features and applications

G. Pizzi, V. Vitale, R. Arita, S. Blügel, F. Freimuth, G. Géranton, M. Gibertini, D. Gresch, C. Johnson, T. Koretsune, J. Ibañez-Azpiroz, H. Lee, J. M. Lihm, D. Marchand, A. Marrazzo, Y. Mokrousov, J. I. Mustafa, Y. Nohara, Y. Nomura, L. Paulatto, S. Poncé, T. Ponweiser, J. Qiao, F. Thöle, S. S. Tsirkin, M. Wierzbowska, N. Marzari, D. Vanderbilt, I. Souza, A. A. Mostofi, and J. R. Yates

J. Phys. Cond. Matt. 32, 165902 (2020) DOI:10.1088/1361-648X/ab51ff

Multiwavelength Raman spectroscopy of ultranarrow nanoribbons made by solution-mediated bottom-up approach

D. Rizzo, D. Prezzi, A. Ruini, V. Nagyte, A. Keerthi, A. Narita, U. Beser, F. Xu, Y. Mai, X. Feng, K. Müllen, E.Molinari, and C. Casiraghi

Phys. Rev. B 100, 045406 (2019) DOI:10.1103/PhysRevB.100.045406

Bandgap Engineering of Graphene Nanoribbons by Control over Structural Distortion

Y. Hu, P. Xie, M. De Corato, A. Ruini, S. Zhao, F. Meggendorfer, L. Arnt Straas, L. Rondin, P. Simon, J. Li, J. J. Finley, M. R. Hansen, J.-S. Lauret, ́ E. Molinari, X. Feng, J. V. Barth, C.-A. Palma, D. Prezzi, K. Müllen, and A. Narita

J. Am. Chem. Soc. 140, 25, 7803–7809 (2018) DOI:10.1021/jacs.8b02209

At SC21, Experts Ask: Can Fast HPC Be Green?

D. Cesarini (CINECA)

HPCWire article

Density functional theory calculations of large systems: Interplay between fragments, observables, and computational complexity

W. Dawson, A. Degomme, M. Stella, T. Nakajima, L. E. Ratcliff, and L. Genovese

WIREs Computational Molecular Science 12, 3 (2021) DOI: 10.1002/wcms.1574

Predicting potential SARS-CoV-2 mutations of concern via full quantum mechanical modelling

M. Zaccaria, L. Genovese, B. E. Lawhorn, W. Dawson,A. S. Joyal, J. Hu , P. Autissier, T. Nakajima, W. E. Johnson, I. Fofana, M. Farzan, and B. Momeni

J. R. Soc. Interface (2024) DOI: 10.1098/rsif.2023.0614

Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview

A. Herten

https://arxiv.org/pdf/2309.05445