Novel Materials Discovery

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Report on the ASE-FireWorks workshop

The report will contain a description of scientific program and the main outcome of the workshop. Based on feedback from the participants (users of the ASE-FireWorks workflows) we will also provide an analysis of where future work on LibFlow-X should be focused to ensure the best service for the community.

Researcher-exchange, workshop and summer-schools report

This document reports on the academic workshops, researcher-exchange, summer schools and hackathons, described in Task 1, 2, 3 and 4 of WP12. The report will include the description of the presentations and tutorial training materials; the report will also briefly describe the feedback from the participants.

Final report, documentation and software release on Green-X, for RPA, GW and EPC

The report will complete the report D2.1, and describe the final product, its testing, and its availability.

Evaluation of mini-apps in pre-exascale architectures

The suite of mini-apps will be executed and analysed in the availablepre-exascle architectures at BSC, CSC and MPG. The deliverable will be aperformance report on the studied architectures including withrecommendations for hardware industry and code developers.

Report and documentation of scalable data infrastructure with deployment on multiple sites; data replication and synchronisation

Documentation of the all developed and implemented data infrastructure components. This also addresses that the infrastructure allows distributed deployment, the functionality to replicate and synchronize data between deployments, and the multiple-site properties.

Report on industry workshops

This report summarizes, analyzes and comments upon the two industry workshops, with particular focus on (a) impact and value of the CoE for industry: whether and how the CoE meets industrial needs; (b) feedback from industry on how the CoE could be made more valuable to them.

Report on applications of DNNs for dimensionality reduction in materials systems; of kernel learning to dimensional reduction in materials; of descriptor discovery to materials systems; and user-guided interactive force-field generation using kernel metho

Report on applications of (i) high-accuracy prediction of extensive and intensive materials properties with distributed DNN, (ii) active-learning-driven interatomic force-field generation using kernel methods, and (iii) descriptor discovery for complex materials properties with SGD and SISSO.

Report and documentation of finalized enhanced releases of Libxc-X and ELPA-X for DFT for exascale architectures

Report on the performed optimization of ELPA-X as well as on the achieved improvements. Both single node usage efficiency as well as usability on higher number of nodes should be addressed. Provide the final, fully enhanced version of ELPA-X. Furthermore, the final, fully enhanced version of Libxc-X will be provided, featuring improved non-local operator evaluation and reference implementations of the basis set transformation for each code family. For both ELPA-X and of Libxc-X, porting to essential next generation supercomputers will have been performed, and results on the co-development with respect to DFT codes will be reported.

CSA engagement report - activities, results, analysis

This report will detail our collaboration with the Focus CoE on training and industry outreach, including an analysis of the effectiveness.

Report and documentation of validated workflows for beyond DFT calculations in LibFlow-X

This report describes the workflow implemented in LibFlow-X for RPA, MP2, CC, and GW calculations. The report will also include a detailed documentation of the input parameters for controlling the workflows and of the output produced by these workflows. A series of examples validating the workflows will be provided and described in detail.

Report and documentation of CC4S-X and CTF-X including block-sparse tensors

Report describing the additional support for block-sparse tensors in CC4S calculations to fully account for the translational symmetry of periodic crystals. Test of the implementation will be described for a prototypical periodic system.

Report and documentation of running baseline infrastructure with storage, automated processing, and basic search for common meta-data

Description of implementation of the data infrastructure that allows human users (GUI) and computer programs (API) to upload data, automatically process data, search based on a subset of common pre-defined metadata, download raw-data and download archive data in a common code-independent format.

Report on industry training

This document reports on summer schools described in Task 2 of WP10. The core of this report is the presentations and tutorial training materials. The report also briefly describes the feedback from the participants and suggestions for the final workshop.

Data management plan

This plan outlines what data will be generated by the project; its format and scope; how and where it will be stored, backed up, curated and preserved (where appropriate); any protection or security it requires; how it will be shared in the future in any open data repository; and any conditions for access.

Website (updated throughout the project)

The project website, with its associated Twitter feed, YouTube channel, and facebook page, is the online ‘shop window’ of the project. It will summarize the key parts of NOMAD (project aims, project team, approach and science, newsletters, publications of the team, etc.); it will be updated on an ongoing basis, with news and information on project developments and activities.

Report and documentation of NOMAD CoE AI-X Toolkit: JupyterHub based implementation of the near-real-time data HPAI methods developed in WP6

Final report and documentation of the following aspects: a) The data infrastructure includes a JupyterHub deployment localized close to archive storage (same data centre) and b) notebooks implementing the HPAI methods of WP6 can be run on this JupyterHUB.

Report and documentation of codes and frameworks of DNN, exascale kernel methods and exascale SISSO and SGD

Description of codes and documentation for software frameworks of distributed DNN, towards-exascale kernel methods and towards-exascale SISSO and SGD. The frameworks will enable near-real-time, interactive analysis of data in the NOMAD Archive.

Dissemination materials (brochure, flyer, posters, newsletters)

A brochure, flyer and poster will be produced during the first 12 months of the project, in order to have resources for distribution at conferences, meetings, industry briefings, etc. The poster may be displayed at the groups of the beneficiaries. The newsletters will be produced at M18, M30 and M42, and will primarily describe the progress of the project during the relevant year. It should address an audience like the second shell and the Psi-k community.

Pubblicazioni

optimade-python-tools: a Python library for serving and consuming materials data via OPTIMADE APIs

Autori: M. L. Evans, C. W. Andersen, S. Dwaraknath, M. Scheidgen, Á. Fekete, and D. Winston
Pubblicato in: Journal of Open Source Software, Numero 6(65), 2021, Pagina/e 3458, ISSN 2475-9066
Editore: creative commons
DOI: 10.21105/joss.03458

Shift current photovoltaic efficiency of 2D materials

Autori: Thomas Pedersen, Mikkel Sauer, Alireza Taghizadeh, Urko Holguin, Martin Ovesen, Kristian Thygesen, Thomas Olsen, Horia Cornean
Pubblicato in: ResearchSquare, 2022, ISSN 2693-5015
Editore: ResearchSquare
DOI: 10.21203/rs.3.rs-2158047/v1

Optimal data generation for machine learned interatomic potentials

Autori: Connor Allen, Albert P. Bartók
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2207.11828

NOMAD: A distributed web-based platform for managing materials science research data

Autori: Scheidgen et al.
Pubblicato in: Journal of Open Source Software, Numero 8, 2023, ISSN 2475-9066
Editore: Journal of Open Source Software
DOI: 10.21105/joss.05388

Absorption versus Adsorption: High-Throughput Computation of Impurities in 2D Materials

Autori: Joel Davidsson, Fabian Bertoldo, Kristian S. Thygesen, Rickard Armiento
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2207.05353

Data-driven discovery of novel 2D materials by deep generative models

Autori: Peder Lyngby, Kristian Sommer Thygesen
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2206.12159

Atomic Simulation Recipes: A Python framework and library for automated workflows.

Autori: M. Gjerding, T. Skovhus, A. Rasmussen, F. Bertoldo, A. H. Larsen, J. J. Mortensen, K. S. Thygesen
Pubblicato in: Computational Materials Science, Numero 199, 2021, Pagina/e 110731, ISSN 0927-0256
Editore: Elsevier BV
DOI: 10.1016/j.commatsci.2021.110731

Interpretability of machine-learning models in physical sciences.Challenges and perspectives for interoperability and reuse of heterogenous data collectionsLearning Rules for Materials Properties and Functions

Autori: Luca M. Ghiringhelli, Draxl, M. Kuban, S. Rigamonti, M. Scheidgen, M. Boley and M. Scheffler
Pubblicato in: Roadmap on Machine Learning in Electronic Structure, Numero 4, 2022, Pagina/e 023004, ISSN 2516-1075
Editore: IOP
DOI: 10.1088/2516-1075/ac572f

Adaptively compressed exchange in the linearized augmented plane wave formalism

Autori: D. Zavickis, K. Kacars, J. Cīmurs, and A. Gulans
Pubblicato in: Phys. Rev. B, Numero 24699950, 2022, Pagina/e 165101, ISSN 2469-9950
Editore: APS
DOI: 10.48550/arxiv.2201.10914

Recent advances in the SISSO method and their implementation in the SISSO++ code

Autori: Thomas A. R. Purcell, Matthias Scheffler, Luca M. Ghiringhelli
Pubblicato in: arxiv.org, 2023, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2305.01242

GIMS: Graphical Interface for Materials Simulations

Autori: S. Kokott, I. Hurtado, C. Vorwerk, C. Draxl, V. Blum, and M. Scheffler
Pubblicato in: Journal of Open Source Software, Numero 6(57), 2021, Pagina/e 2767, ISSN 2475-9066
Editore: NumFOCUS / Open Source Initiative
DOI: 10.21105/joss.02767

All-electron periodic G0W0 implementation with numerical atomic orbital basis functions: algorithm and benchmarks

Autori: Xinguo Ren; Florian Merz; Hong Jiang; Yi Yao; Yi Yao; Markus Rampp; Hermann Lederer; Volker Blum; Matthias Scheffler
Pubblicato in: Phys. Rev. Materials, Numero 5, 2021, Pagina/e 013807, ISSN 2475-9953
Editore: American Physical Society
DOI: 10.1103/physrevmaterials.5.013807

Sensitivity and Dimensionality of Atomic Environment Representations used for Machine Learning Interatomic Potentials

Autori: B. Onat, C. Ortner and J. R. Kermode
Pubblicato in: J. Chem. Phys., Numero 153, 2020, Pagina/e 144106, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.48550/arxiv.2006.01915

OPTIMADE, an API for exchanging materials data

Autori: Andersen, Casper W.; Armiento, Rickard; Blokhin, Evgeny; Conduit, Gareth J.; Dwaraknath, Shyam; Evans, Matthew L.; Fekete, Ádám; Gopakumar, Abhijith; Gražulis, Saulius; Merkys, Andrius; Mohamed, Fawzi; Oses, Corey; Pizzi, Giovanni; Rignanese, Gian-Marco; Scheidgen, Markus; Talirz, Leopold; Toher, Cormac; Winston, Donald; Aversa, Rossella; Choudhary, Kamal; Colinet, Pauline; Curtarolo, Stefano;
Pubblicato in: Scientific Data, Numero Vol 8, Iss 1, 2021, Pagina/e 1-10, ISSN 2052-4463
Editore: Springer Nature
DOI: 10.1038/s41597-021-00974-z

Critical assessment of G0W0 calculations for 2D materials: the example of monolayer MoS2

Autori: Rodrigues Pela, R., Vona, C., Lubeck, S. et al.
Pubblicato in: npj Comput Mater, Numero 10, 2024, ISSN 2057-3960
Editore: Nature
DOI: 10.1038/s41524-024-01253-2

On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials

Autori: Shuaihua Lu, Luca M. Ghiringhelli, Christian Carbogno, Jinlan Wang, Matthias Scheffler
Pubblicato in: arxiv.org, 2023, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2309.00195

Ab initio approach for thermodynamic surface phases with full consideration of anharmonic effects – the example of hydrogen at Si(100)

Autori: Y. Zhou, C. Zhu, M. Scheffler, and L. M. Ghiringhelli
Pubblicato in: Physical Review Letters, Numero 00319007, 2022, Pagina/e 246101, ISSN 0031-9007
Editore: American Physical Society
DOI: 10.48550/arxiv.2202.01193

Ab initio property characterisation of thousands of previously unknown 2D materials

Autori: Peder Lyngby, Kristian Sommer Thygesen
Pubblicato in: arxiv.org, 2024, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2402.02783

The FHI-aims Code: All-electron, ab initio materials simulations towards the exascale

Autori: Volker Blum, Mariana Rossi, Sebastian Kokott, Matthias Scheffler
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2208.12335

Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence.

Autori: L. Foppa, C. Sutton, L. M. Ghiringhelli, S. De, P. Löser, S.A. Schunk, A. Schäfer, and M. Scheffler
Pubblicato in: ACS Catalysis, Numero 12, 2022, Pagina/e 2223, ISSN 2155-5435
Editore: American Chemical Society
DOI: 10.1021/acscatal.1c04793

Electronic Properties of Functionalized Diamanes for Field-Emission Displays

Autori: Christian Tantardini*, Alexander G. Kvashnin*, Maryam Azizi, Xavier Gonze*, Carlo Gatti, Tariq Altalhi, and Boris I. Yakobson*
Pubblicato in: ACS Appl. Mater. Interfaces, Numero 15, 2023, ISSN 1944-8244
Editore: American Chemical Society
DOI: 10.1021/acsami.3c01536

Benchmark of GW Methods for Core-Level Binding Energies

Autori: J. Li, Y. Jin, P. Rinke, W. Yang, D. Golze
Pubblicato in: J. Chem. Theory Comput., 2022, ISSN 1549-9618
Editore: American Chemical Society
DOI: 10.1021/acs.jctc.2c00617

Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models

Autori: L. Zhang, B. Onat, G. Dusson, G. Anand, R. J. Maurer, C. Ortner, and J.R. Kermode
Pubblicato in: npj Comp. Mater., Numero 20573960, 2022, Pagina/e 158, ISSN 2057-3960
Editore: npj Comp. Mater.
DOI: 10.48550/arxiv.2111.13736

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

Autori: Matthew L. Evans, Johan Bergsma, Andrius Merkys, Casper W. Andersen, Oskar B. Andersson, Daniel Beltrán, Evgeny Blokhin, Tara M. Boland, Rubén Castañeda Balderas, Kamal Choudhary, Alberto Díaz Díaz, Rodrigo Domínguez García, Hagen Eckert, Kristjan Eimre, María Elena Fuentes Montero, Adam M. Krajewski, Jens Jørgen Mortensen, José Manuel Nápoles Duarte, Jacob Pietryga, Ji Qi, Felipe de Je
Pubblicato in: arxiv.org, 2024, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2402.00572

Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet

Autori: Pierre-Paul De Breuck; Matthew Evans; Gian-Marco Rignanese
Pubblicato in: Journal of Physics: Condensed Matter, Numero 33, 2021, Pagina/e 404002, ISSN 0953-8984
Editore: Institute of Physics Publishing
DOI: 10.1088/1361-648x/ac1280

Advancing Critical Chemical Processes for a Sustainable Future: Challenges for Industry and the Max Planck–Cardiff Centre on the Fundamentals of Heterogeneous Catalysis (FUNCAT)

Autori: Michael Bowker, Serena DeBeer, Nicholas F. Dummer, Graham J. Hutchings, Matthias Scheffler, Ferdi Schüth, Stuart H. Taylor, Harun Tüysüz
Pubblicato in: Angewandte Chemie., Numero e202209016, 2022, ISSN 1433-7851
Editore: John Wiley & Sons Ltd.
DOI: 10.1002/ange.202209016

Materials Genes of Heterogeneous Catalysis from Clean Experiments and Artificial Intelligence

Autori: Lucas Foppa; Luca Ghiringhelli; Frank Girgsdies; Maike Hashagen; Pierre Kube; Michael Hävecker; Spencer J. Carey; Andrey Tarasov; Peter Kraus; Frank Rosowski; Robert Schlögl; Annette Trunschke; Matthias Scheffler
Pubblicato in: MRS Bulletin, Numero 46, 2021, Pagina/e 1-11, ISSN 0883-7694
Editore: Materials Research Society
DOI: 10.1557/s43577-021-00165-6

CELL: a Python package for cluster expansion with a focus on complex alloys

Autori: Santiago Rigamonti, Maria Troppenz, Martin Kuban, Axel Hübner, Claudia Draxl
Pubblicato in: arXiv.org, 2023, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2310.18223

Representing individual electronic states for machine learning GW band structures of 2D materials

Autori: N. R. Knosgaard and K. S. Thygesen
Pubblicato in: Nature Communications, Numero 13, 2022, Pagina/e 468, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-022-28122-0

Data-centric heterogeneous catalysis: identifying rules and materials genes of alkane selective oxidation

Autori: Lucas Foppa, Frederik Rüther, Michael Geske, Gregor Koch, Frank Girgsdies, Pierre Kube, Spencer Carey, Michael Hävecker, Olaf Timpe, Andrey Tarasov, Matthias Scheffler, Frank Rosowski, Robert Schlögl, and Annette Trunschke
Pubblicato in: ChemRxiv, 2022, ISSN 2573-2293
Editore: ChemRxiv
DOI: 10.26434/chemrxiv-2022-xmg75

Towards fully automatized GW band structure calculations: What we can learn from 60.000 self-energy evaluations.

Autori: A. Rasmussen, T. Deilmann, and K. S. Thygesen
Pubblicato in: npj Computational Materials, Numero 7(22), 2021, Pagina/e 1-9, ISSN 2057-3960
Editore: Nature Publishing Group
DOI: 10.1038/s41524-020-00480-7

High-throughput computational stacking reveals emergent properties in natural van der Waals bilayers.

Autori: Pakdel, S., Rasmussen, A., Taghizadeh, A. et al.
Pubblicato in: Nat Commun, Numero 15, 2024, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-024-45003-w

Computational exfoliation of atomically thin 1D materials with application to Majorana bound states

Autori: H. Moustafa, P.M. Larsen, M.N. Gjerding, J.J. Mortensen, K.S. Thygesen, K.W. Jacobsen
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2204.00472

Materials Genes of CO2 Hydrogenation on Supported Cobalt Catalysts: An Artificial Intelligence Approach Integrating Theoretical and Experimental Data

Autori: Ray Miyazaki*, Kendra S Belthle, Harun Tüysüz, Lucas Foppa*, and Matthias Scheffler
Pubblicato in: J. Am. Chem. Soc., 2024, ISSN 0002-7863
Editore: American Chemical Society
DOI: 10.1021/jacs.3c12984

Advancing descriptor search in materials science: feature engineering and selection strategies

Autori: B. Hoock, S. Rigamonti, and C. Draxl
Pubblicato in: New J. Phys., Numero 24, 2022, Pagina/e 113049, ISSN 1367-2630
Editore: Institute of Physics Publishing
DOI: 10.1088/1367-2630/aca49c

A simple denoising approach to exploit multi-fidelity data for machine learning materials properties

Autori: Liu, X., De Breuck, PP., Wang, L. et al.
Pubblicato in: npj Comput Mater, Numero 8, 2022, ISSN 2057-3960
Editore: Nature
DOI: 10.1038/s41524-022-00925-1

From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery

Autori: Mario Boley, Felix Luong, Simon Teshuva, Daniel F Schmidt, Lucas Foppa, Matthias Scheffler
Pubblicato in: arXiv.org, 2023, ISSN 2331-8422
Editore: arXiv.org
DOI: 10.48550/arxiv.2311.15549

Quantum point defects in 2D materials: The QPOD database

Autori: Fabian Bertoldo, Sajid Ali, Simone Manti, Kristian S. Thygesen
Pubblicato in: npj Comput Mater, Numero 8, 2021, Pagina/e 56, ISSN 2057-3960
Editore: npj Computational Materials
DOI: 10.48550/arxiv.2110.01961

ACEpotentials.jl: A Julia Implementation of the Atomic Cluster Expansion

Autori: William C. Witt, Cas van der Oord, Elena Gelžinytė, Teemu Järvinen, Andres Ross, James P. Darby, Cheuk Hin Ho, William J. Baldwin, Matthias Sachs, James Kermode, Noam Bernstein, Gábor Csányi, Christoph Ortner
Pubblicato in: arXiv.org, 2023, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2309.03161

Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework

Autori: Sheng Bi, Christian Carbogno, Igor Ying Zhang, Matthias Scheffler
Pubblicato in: arxiv.org, 2024, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2401.11696

Indirect Band Gap Semiconductors for Thin-Film Photovoltaics: High-Throughput Calculation of Phonon-Assisted Absorption

Autori: Jiban Kangsabanik, Mark Kamper Svendsen, Alireza Taghizadeh, Andrea Crovetto, and Kristian S. Thygesen
Pubblicato in: J. Am. Chem. Soc., Numero 144, 2022, Pagina/e 19872, ISSN 1520-5126
Editore: ACS Publications
DOI: 10.1021/jacs.2c07567

Many-core acceleration of the first-principles all-electron quantum perturbation calculations

Autori: H. Shang, X. Duan, F. Li, L. Zhang, Z. Xu, K. Liu, H. Luo, Y. Ji, W. Zhao, W. Xue, L. Chen, and Y. Zhang
Pubblicato in: Computer Physics Communications, Numero 00104655, 2021, Pagina/e 108045, ISSN 0010-4655
Editore: Elsevier BV
DOI: 10.1016/j.cpc.2021.108045

Leveraging genetic algorithms to maximise the predictive capabilities of the SOAP descriptor

Autori: Trent Barnard, Steven Tseng, James P. Darby, Albert P. Bartók, Anders Broo and Gabriele C. Sosso
Pubblicato in: Molecular Systems Design & Engineering, 2022, ISSN 2058-9689
Editore: Royal Society of Chemistry
DOI: 10.1039/d2me00149g

TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions.

Autori: Regler, B., Scheffler, M. & Ghiringhelli, L.M
Pubblicato in: Data Min Knowl Disc, Numero 36, 2022, ISSN 1573-756X
Editore: Springer Nature
DOI: 10.1007/s10618-022-00847-y

Towards a Multi-Objective Optimization of Subgroups for the Discovery of Materials with Exceptional Performance

Autori: Lucas Foppa, Matthias Scheffler
Pubblicato in: arXiv.org, 2023, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2311.10381

Interpretable Machine Learning for Materials Design.

Autori: J. Dean, M. Scheffler, T. A. R. Purcell, S. V. Barabash, R. Bhowmik, T. Bazhirov
Pubblicato in: Journal of Materials Research, Numero 38, 2023, ISSN 2044-5326
Editore: Springer Nature
DOI: 10.1557/s43578-023-01164-w

Tensor-reduced atomic density representations

Autori: James P. Darby, Dávid P. Kovács, Ilyes Batatia, Miguel A. Caro, Gus L. W. Hart, Christoph Ortner, Gábor Csányi
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2210.01705

Shared Metadata for Data-Centric Materials Science

Autori: L.M. Ghiringhelli et al.
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2205.14774

Limits to Hole Mobility and Doping in Copper Iodide

Autori: Joe Willis, Romain Claes, Qi Zhou, Matteo Giantomassi, Gian-Marco Rignanese, Geoffroy Hautier*, and David O. Scanlon*
Pubblicato in: Chem. Mater., Numero 35, 2023, ISSN 0897-4756
Editore: American Chemical Society
DOI: 10.1021/acs.chemmater.3c01628

Identifying Outstanding Transition‑Metal‑Alloy Heterogeneous Catalysts for the Oxygen Reduction and Evolution Reactions via Subgroup Discovery

Autori: Foppa, Lucas; Ghiringhelli, Luca M.
Pubblicato in: Topics in Catalysis, 2021, Pagina/e 1-11, ISSN 1022-5528
Editore: Baltzer Science Publishers B.V.
DOI: 10.1007/s11244-021-01502-4

Fermionic Quantum Turbulence: Pushing the Limits of High-Performance Computing

Autori: Gabriel Wlazlowski, Michael McNeil Forbes, Saptarshi Rajan Sarkar, Andreas Marek, Maciej Szpindler
Pubblicato in: arXiv.org, 2024, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2310.03341

Autori: M. Kuban, S. Gabaj, W. Aggoune, C. Vona, S. Rigamonti, and C. Draxl
Pubblicato in: MRS Bulletin Impact, Numero 08837694, 2022, Pagina/e 1, ISSN 0883-7694
Editore: Materials Research Society
DOI: 10.48550/arxiv.2204.04056

Interface to high-performance periodic coupled-cluster theory calculations with atom-centered, localized basis functions

Autori: E. Moerman, F. Hummel, A. Grüneis, A. Irmler, M. Scheffler
Pubblicato in: Journal of Open-Source Software, Numero 24759066, 2022, Pagina/e 4040, ISSN 2475-9066
Editore: Journal of Open-Source Software
DOI: 10.21105/joss.04040

Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework

Autori: Francisco A. Delesma, Moritz Leucke, Dorothea Golze, Patrick Rinke
Pubblicato in: arXiv.org, 2024, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2310.11058

Time-frequency component of the GreenX library: minimax grids for efficient RPA and GW calculations

Autori: Azizi, Maryam; Wilhelm, Jan; Golze, Dorothea; Giantomassi, Matteo; Panadés-Barrueta, Ramón L; Delesma, Francisco A; Buccheri, Alexander; Gulans, Andris; Rinke, Patrick; Draxl, Claudia; Gonze, Xavier
Pubblicato in: Journal of Open Source Software, Numero 8, 2023, Pagina/e 5570, ISSN 2475-9066
Editore: Journal of Open Source Software
DOI: 10.21105/joss.05570

Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

Autori: Langer, M.F., Goeßmann, A. & Rupp, M.
Pubblicato in: npj Comput Mater, Numero 8, 2022, ISSN 2057-3960
Editore: Nature
DOI: 10.1038/s41524-022-00721-x

FAIR data enabling new horizons for materials research

Autori: M. Scheffler, M. Aeschlimann, M. Albrecht, T. Bereau, H.-J. Bungartz, C.Felser, M. Greiner, A. Groß, C. Koch, K. Kremer, W. E. Nagel, M- Scheidgen, C. Wöll, and C. Draxl
Pubblicato in: Nature, Numero 00280836, 2022, Pagina/e 635, ISSN 0028-0836
Editore: Nature Publishing Group
DOI: 10.48550/arxiv.2204.13240

Accurate and efficient treatment of spin-orbit coupling via second variation employing local orbitals

Autori: Cecilia Vona, Sven Lubeck, Hannah Kleine, Andris Gulans, and Claudia DraxlCecilia Vona, Sven Lubeck, Hannah Kleine, Andris Gulans, and Claudia Draxl
Pubblicato in: Phys. Rev. B, Numero 108, 2023, ISSN 2469-9950
Editore: APS
DOI: 10.1103/physrevb.108.235161

Roadmap on Electronic Structure Codes in the Exascale Era

Autori: Vikram Gavini, Stefano Baroni, Volker Blum, David R. Bowler, Alexander Buccheri, James R. Chelikowsky, Sambit Das, William Dawson, Pietro Delugas, Mehmet Dogan, Claudia Draxl, Giulia Galli, Luigi Genovese, Paolo Giannozzi, Matteo Giantomassi, Xavier Gonze, Marco Govoni, Andris Gulans, François Gygi, John M. Herbert, Sebastian Kokott, Thomas D. Kühne, Kai-Hsin Liou, Tsuyoshi Miyazaki, Phani Motam
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2209.12747

Massively Parallel Fitting of Gaussian Approximation Potentials

Autori: S. Klawohn, J. R. Kermode, and A. P. Bartók
Pubblicato in: ArXiv.org, 2022, ISSN 2057-3960
Editore: ArXiv.org
DOI: 10.48550/arxiv.2207.03803

Electronic Impurity Doping of a 2D Hybrid Lead Iodide Perovskite by Bi and Sn

Autori: Haipeng Lu, Gabrielle Koknat, Yi Yao, Ji Hao, Xixi Qin, Chuanxiao Xiao, Ruyi Song, Florian Merz, Markus Rampp, Sebastian Kokott, Christian Carbogno, Tianyang Li, Glenn Teeter, Matthias Scheffler, Joseph J. Berry, David B. Mitzi, Jeffrey L. Blackburn, Volker Blum, and Matthew C. Beard
Pubblicato in: PRX Energy, Numero 2, 2023, ISSN 2768-5608
Editore: APS
DOI: 10.1103/prxenergy.2.023010

High-Throughput Search for Triplet Point Defects with Narrow Emission Lines in 2D Materials

Autori: Sajid Ali*, Fredrik Andreas Nilsson, Simone Manti, Fabian Bertoldo, Jens Jørgen Mortensen, and Kristian Sommer Thygesen
Pubblicato in: ACS Nano, Numero 17, 2023, ISSN 1936-0851
Editore: American Chemical Society
DOI: 10.1021/acsnano.3c04774

Artificial-intelligence-driven discovery of catalyst “genes” with application to CO2 activation on semiconductor oxides.

Autori: A. Mazheika, Y. Wang, R. Valero, F. Vines, F. Illas, L. Ghiringhelli, S. Levchenko, and M. Scheffler
Pubblicato in: Nature Communications, Numero 13, 2022, Pagina/e 416, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-022-28042-z

Numerical Quality Control for DFT-based Materials Databases

Autori: C. Carbogno, K.S. Thygesen, B. Bieniek, C. Draxl, L.M. Ghiringhelli, A. Gulans, O. T. Hofmann, K. W. Jacobsen, S. Lubeck, J. J. Mortensen, M. Strange, E. Wruss, and M. Scheffler
Pubblicato in: npj Computational Materials, Numero 8, 2022, Pagina/e 69, ISSN 2057-3960
Editore: Nature
DOI: 10.1038/s41524-022-00744-4

Ab initio Green-Kubo simulations of heat transport in solids: method and implementation

Autori: F. Knoop, M. Scheffler, and C. Carbogno
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2209.01139

Recent progress of the Computational 2D Materials Database (C2DB).

Autori: M. N. Gjerding, A. Taghizadeh, A. Rasmussen, S. Ali, F. Bertoldo, T. Deilmann, N. R. Knøsgaard, M. Kruse, A. H. Larsen, S. Manti, T. G. Pedersen, U. Petralanda, T. Skovhus, M. K. Svendsen, J. J. Mortensen, T. Olsen and K. S. Thygesen
Pubblicato in: 2D Materials, Numero 8, 2021, Pagina/e 044002, ISSN 2053-1583
Editore: IO
DOI: 10.11583/dtu.14616660

Gaussian Approximation Potentials: theory, software implementation and application examples

Autori: Sascha Klawohn, Gábor Csányi, James P. Darby, James R. Kermode, Miguel A. Caro, Albert P. Bartók
Pubblicato in: arxiv.org, 2023, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2310.03921

Improved Uncertainty Quantification for Gaussian Process Regression Based Interatomic Potentials

Autori: P. Bartók and J. R. Kermode
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2206.08744

Anharmonicity in Thermal Insulators – An Analysis from First Principles

Autori: F. Knoop, T.A.R. Purcell, M. Scheffler, and C. Carbogno
Pubblicato in: ArXiv.org, 2022, ISSN 2057-3960
Editore: ArXiv.org
DOI: 10.48550/arxiv.2209.12720

Surface science using coupled cluster theory via local Wannier functions and in-RPA-embedding: The case of water on graphitic carbon nitride

Autori: T. Schäfer, A. Gallo, A. Irmler, F. Hummel, and A. Grüneis
Pubblicato in: J. Chem. Phys., Numero 00219606, 2021, Pagina/e 244103, ISSN 0021-9606
Editore: American Institute of Physics
DOI: 10.1063/5.0074936

Automatic Identification of Crystal Structures and Interfaces via Artificial-Intelligence-based Electron Microscopy

Autori: Andreas Leitherer, Byung Chul Yeo, Christian H. Liebscher, Luca M. Ghiringhelli
Pubblicato in: arxiv.org, 2023, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2303.12702

High-throughput analysis of Fröhlich-type polaron models

Autori: Pedro Miguel M. C. de Melo, Joao C. de Abreu, Bogdan Guster, Matteo Giantomassi, Zeila Zanolli, Xavier Gonze, Matthieu J. Verstraete
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2207.00364

Influence of spin-orbit coupling on chemical bonding

Autori: A. Gulans and C. Draxl
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2204.02751

Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning.

Autori: A. Leitherer, A. Ziletti, and L.M. Ghiringhelli
Pubblicato in: Nature Communications, Numero 12, 2021, Pagina/e 6234, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-021-26511-5

Updates to the DScribe Library: New Descriptors and Derivatives

Autori: Jarno Laakso, Lauri Himanen, Henrietta Homm, Eiaki V. Morooka, Marc O. J. Jäger, Milica Todorović, Patrick Rinke
Pubblicato in: arXiv.org, 2023, ISSN 2331-8422
Editore: arxiv.org
DOI: 10.48550/arxiv.2303.14046

DFT Exchange: Sharing Perspectives on the Workhorse of Quantum Chemistry and Materials Science

Autori: M. Teale et al.
Pubblicato in: Phys. Chem. Chem. Phys., Numero Advance Article, 2022, ISSN 1463-9076
Editore: Royal Society of Chemistry
DOI: 10.26434/chemrxiv-2022-13j2v

Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites

Autori: L. Foppa, T. A. R. Purcell, S. V. Levchenko, M. Scheffler, and L. M. Ghiringhelli
Pubblicato in: Phys. Rev. Lett., Numero 129, 2022, Pagina/e 055301, ISSN 0031-9007
Editore: American Physical Society
DOI: 10.1103/physrevlett.129.055301

Hybrid Materials: Still Challenging for Ab Initio Theory?

Autori: Ignacio Gonzalez Oliva; Benedikt Maurer; Ben Alex; Sebastian Tillack; Maximilian Schebek; Claudia Draxl
Pubblicato in: Phys. Status Solidi A, Numero 221, 2023, ISSN 1862-6319
Editore: Wiley
DOI: 10.1002/pssa.202300170

An AI-toolkit to develop and share research into new materials

Autori: L. M. Ghiringhelli
Pubblicato in: Nature Review Physics, Numero 25225820, 2021, Pagina/e 724, ISSN 2522-5820
Editore: Nature Reviews Physics
DOI: 10.1038/s42254-021-00373-8

Exploring and machine learning structural instabilities in 2D materials

Autori: Manti, S., Svendsen, M.K., Knøsgaard, N.R. et al.
Pubblicato in: npj Comput Mater, Numero 9, 2023, ISSN 2057-3960
Editore: Nature
DOI: 10.1038/s41524-023-00977-x

Jobflow: Computational Workflows Made Simple.

Autori: Rosen et al.
Pubblicato in: Journal of Open Source Software, 2024, ISSN 2475-9066
Editore: JOSS
DOI: 10.21105/joss.05995

Density-of-states similarity descriptor for unsupervised learning from materials data

Autori: M. Kuban, S. Rigamonti, M. Scheidgen, and C. Draxl
Pubblicato in: Sci. Data, Numero 20524463, 2022, Pagina/e 646, ISSN 2052-4463
Editore: Sci. Data
DOI: 10.48550/arxiv.2201.02187

Roadmap: Organic-inorganic hybrid perovskite semiconductors and devices.

Autori: L. Schmidt-Mende, V. Dyakonov, S. Olthof, F. Ünlü, K. Moritz, T. Lê, S. Mathur, A. D. Karabanov, D. C. Lupascu, L. Herz, A. Hinderhofer, F. Schreiber, A. Chernikov, D. A. Egger, O. Shargaieva, C. Cocchi, E. Unger, M. Saliba, M. Malekshahi Byranvand, M. Kroll, F. Nehm, K. Leo, A. Redinger, J. Höcker, T. Kirchartz, J. Warby, E. Gutierrez-Partida, D. Neher, M. Stolterfoht, U. Würfel, M. Unmüssi
Pubblicato in: APL Materials, Numero 9, 2021, Pagina/e 109202, ISSN 2166-532X
Editore: American Institute of Physics
DOI: 10.1063/5.0047616

matscipy: materials science at the atomic scale with Python

Autori: Grigorev, P.; Frérot, L.; Birks, F.; Gola, A.; Golebiowski, J.; Grießer, J.; Hörmann, J.; Klemenz, A.; Moras, G.; Nöhring, W.; Oldenstaedt, J.; Patel, P.; Reichenbach, T.; Shenoy, L.; Walter, M.; Wengert, S. ; https://orcid.org/0000-0002-8008-1482; Kermode, J.; Pastewka, L.
Pubblicato in: The Journal of Open Source Software (JOSS), Numero 9, 2024, Pagina/e 5668, ISSN 2475-9066
Editore: The Journal of Open Source Software (JOSS)
DOI: 10.21105/joss.05668

Accelerating Materials-Space Exploration by Mapping Materials Properties via Artificial Intelligence: The Case of the Lattice Thermal Conductivity

Autori: T. Purcell, M. Scheffler, L. M. Ghiringhelli, C. Carbogno
Pubblicato in: Arxiv.org, 2022, ISSN 2331-8422
Editore: Arxiv
DOI: 10.48550/arxiv.2204.12968

Enhancing Metallicity and Basal Plane Reactivity of 2D Materials via Self-Intercalation

Autori: Stefano Americo*, Sahar Pakdel, and Kristian Sommer Thygesen
Pubblicato in: ACS Nano, Numero 18, 2024, ISSN 1936-0851
Editore: American Chemical Society
DOI: 10.1021/acsnano.3c08117

Hundreds of new, stable, one-dimensional materials from a generative machine learning model

Autori: Hadeel Moustafa, Peder Meisner Lyngby, Jens Jørgen Mortensen, Kristian S. Thygesen, Karsten W. Jacobsen
Pubblicato in: ArXiv.org, 2022, ISSN 2331-8422
Editore: ArXiv.org
DOI: 10.48550/arxiv.2210.08878

The NOMAD Artificial-Intelligence Toolkit: Turning materials-science data into knowledge and understanding

Autori: Luigi Sbailò, Ádám Fekete, Luca M. Ghiringhelli, Matthias Scheffler
Pubblicato in: npj Computational Materials, Numero 8, 2022, Pagina/e 250, ISSN 2057-3960
Editore: Nature Research
DOI: 10.1038/s41524-022-00935-z

excitingtools: An exciting Workflow Tool

Autori: Alexander Buccheri; Fabian Peschel; Benedikt Maurer; Mara Voiculescu; Daniel T. Speckhard; Hannah Kleine; Elisa Stephan; Martin Kuban; Claudia Draxl
Pubblicato in: Journal of Open Source Software, Numero 8, 2023, Pagina/e 5148, ISSN 2475-9066
Editore: Journal of Open Source Software
DOI: 10.21105/joss.05148

Compressing Local Atomic Neighbourhood Descriptors

Autori: J. P. Darby, J. R. Kermode and G. Csányi
Pubblicato in: Npj Computational Materials, Numero 8, 2022, Pagina/e 166, ISSN 2057-3960
Editore: Nature
DOI: 10.48550/arxiv.2112.13055

Beyond the Fourth Paradigm — the Rise of AI

Autori: Andreas Marek; Markus Rampp; Klaus Reuter; Erwin Laure
Pubblicato in: 2023 IEEE 19th International Conference on e-Science (e-Science), 2023, Pagina/e 1-4
Editore: IEEE
DOI: 10.1109/e-science58273.2023.10254904

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