Abinit 2025: New Capabilities for the Predictive Modeling of Solids and Nanomaterials
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2025
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| author | Verstraete, Matthieu J. Abreu, Joao Allemand, Guillaume E. Amadon, Bernard Antonius, Gabriel Azizi, Maryam Baguet, Lucas Barat, Clementine Bastogne, Louis Bejaud, Romuald Beuken, Jean-Michel Bieder, Jordan Blanchet, Augustin Bottin, Francois Bouchet, Johann Bouquiaux, Julien Bousquet, Eric Boust, James Brieuc, Fabien Brousseau-Couture, Veronique Bruneval, Nils Brouwer Fabien Castellano, Alois Castiel, Emmanuel Charraud, Jean-Baptiste Clerouin, Jean Cote, Michel Duval, Clement Gallo, Alejandro Gendron, Frederic Geneste, Gregory Ghosez, Philippe Giantomassi, Matteo Gingras, Olivier Gomez-Ortiz, Fernando Gonze, Xavier Goudreault, Felix Antoine Gruneis, Andreas Gupta, Raveena Guster, Bogdan Hamann, Donald R. He, Xu Hellman, Olle Holzwarth, Natalie Jollet, Francois Kestener, Pierre Lygatsika, Ioanna-Maria Nadeau, Olivier MacEnulty, Lorien Marazzi, Enrico Mignolet, Maxime O'Regan, David D. Outerovitch, Robinson Paillard, Charles Petretto, Guido Ponce, Samuel Ricci, Francesco Rignanese, Gian-Marco Rodriguez-Mayorga, Mauricio Romero, Aldo H. Rostami, Samare Royo, Miquel Sarraute, Marc Sasani, Alireza Soubiran, Francois Stengel, Massimiliano Tantardini, Christian Torrent, Marc Trinquet, Victor Vasilchencko, Vasilii Waroquiers, David Zabalo, Asier Zadoks, Austin Zhang, Huazhang Zwanziger, Josef |
| author_facet | Verstraete, Matthieu J. Abreu, Joao Allemand, Guillaume E. Amadon, Bernard Antonius, Gabriel Azizi, Maryam Baguet, Lucas Barat, Clementine Bastogne, Louis Bejaud, Romuald Beuken, Jean-Michel Bieder, Jordan Blanchet, Augustin Bottin, Francois Bouchet, Johann Bouquiaux, Julien Bousquet, Eric Boust, James Brieuc, Fabien Brousseau-Couture, Veronique Bruneval, Nils Brouwer Fabien Castellano, Alois Castiel, Emmanuel Charraud, Jean-Baptiste Clerouin, Jean Cote, Michel Duval, Clement Gallo, Alejandro Gendron, Frederic Geneste, Gregory Ghosez, Philippe Giantomassi, Matteo Gingras, Olivier Gomez-Ortiz, Fernando Gonze, Xavier Goudreault, Felix Antoine Gruneis, Andreas Gupta, Raveena Guster, Bogdan Hamann, Donald R. He, Xu Hellman, Olle Holzwarth, Natalie Jollet, Francois Kestener, Pierre Lygatsika, Ioanna-Maria Nadeau, Olivier MacEnulty, Lorien Marazzi, Enrico Mignolet, Maxime O'Regan, David D. Outerovitch, Robinson Paillard, Charles Petretto, Guido Ponce, Samuel Ricci, Francesco Rignanese, Gian-Marco Rodriguez-Mayorga, Mauricio Romero, Aldo H. Rostami, Samare Royo, Miquel Sarraute, Marc Sasani, Alireza Soubiran, Francois Stengel, Massimiliano Tantardini, Christian Torrent, Marc Trinquet, Victor Vasilchencko, Vasilii Waroquiers, David Zabalo, Asier Zadoks, Austin Zhang, Huazhang Zwanziger, Josef |
| contents | Abinit is a widely used scientific software package implementing density functional theory and many related functionalities for excited states and response properties. This paper presents the novel features and capabilities, both technical and scientific, which have been implemented over the past 5 years. This evolution occurred in the context of evolving hardware platforms, high-throughput calculation campaigns, and the growing use of machine learning to predict properties based on databases of first principles results. We present new methodologies for ground states with constrained charge, spin or temperature; for density functional perturbation theory extensions to flexoelectricity and polarons; and for excited states in many-body frameworks including GW, dynamical mean field theory, and coupled cluster. Technical advances have extended abinit high-performance execution to graphical processing units and intensive parallelism. Second principles methods build effective models on top of first principles results to scale up in length and time scales. Finally, workflows have been developed in different community frameworks to automate \abinit calculations and enable users to simulate hundreds or thousands of materials in controlled and reproducible conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08578 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Abinit 2025: New Capabilities for the Predictive Modeling of Solids and Nanomaterials Verstraete, Matthieu J. Abreu, Joao Allemand, Guillaume E. Amadon, Bernard Antonius, Gabriel Azizi, Maryam Baguet, Lucas Barat, Clementine Bastogne, Louis Bejaud, Romuald Beuken, Jean-Michel Bieder, Jordan Blanchet, Augustin Bottin, Francois Bouchet, Johann Bouquiaux, Julien Bousquet, Eric Boust, James Brieuc, Fabien Brousseau-Couture, Veronique Bruneval, Nils Brouwer Fabien Castellano, Alois Castiel, Emmanuel Charraud, Jean-Baptiste Clerouin, Jean Cote, Michel Duval, Clement Gallo, Alejandro Gendron, Frederic Geneste, Gregory Ghosez, Philippe Giantomassi, Matteo Gingras, Olivier Gomez-Ortiz, Fernando Gonze, Xavier Goudreault, Felix Antoine Gruneis, Andreas Gupta, Raveena Guster, Bogdan Hamann, Donald R. He, Xu Hellman, Olle Holzwarth, Natalie Jollet, Francois Kestener, Pierre Lygatsika, Ioanna-Maria Nadeau, Olivier MacEnulty, Lorien Marazzi, Enrico Mignolet, Maxime O'Regan, David D. Outerovitch, Robinson Paillard, Charles Petretto, Guido Ponce, Samuel Ricci, Francesco Rignanese, Gian-Marco Rodriguez-Mayorga, Mauricio Romero, Aldo H. Rostami, Samare Royo, Miquel Sarraute, Marc Sasani, Alireza Soubiran, Francois Stengel, Massimiliano Tantardini, Christian Torrent, Marc Trinquet, Victor Vasilchencko, Vasilii Waroquiers, David Zabalo, Asier Zadoks, Austin Zhang, Huazhang Zwanziger, Josef Materials Science Abinit is a widely used scientific software package implementing density functional theory and many related functionalities for excited states and response properties. This paper presents the novel features and capabilities, both technical and scientific, which have been implemented over the past 5 years. This evolution occurred in the context of evolving hardware platforms, high-throughput calculation campaigns, and the growing use of machine learning to predict properties based on databases of first principles results. We present new methodologies for ground states with constrained charge, spin or temperature; for density functional perturbation theory extensions to flexoelectricity and polarons; and for excited states in many-body frameworks including GW, dynamical mean field theory, and coupled cluster. Technical advances have extended abinit high-performance execution to graphical processing units and intensive parallelism. Second principles methods build effective models on top of first principles results to scale up in length and time scales. Finally, workflows have been developed in different community frameworks to automate \abinit calculations and enable users to simulate hundreds or thousands of materials in controlled and reproducible conditions. |
| title | Abinit 2025: New Capabilities for the Predictive Modeling of Solids and Nanomaterials |
| topic | Materials Science |
| url | https://arxiv.org/abs/2507.08578 |