Abinit 2025: New capabilities for the predictive modeling of solids and nanomaterials
Matthieu J. Verstraete, João Abreu, Guillaume E. Allemand, Bernard Amadon, Gabriel Antonius, Maryam Azizi, Lucas Baguet, Clémentine Barat, Louis Bastogne, Romuald Béjaud, Jean-Michel Beuken, Jordan Bieder, Augustin Blanchet, François Bottin, J. Bouchet, Julien Bouquiaux, Éric Bousquet, James Boust, Fabien Brieuc, Véronique Brousseau-Couture, Nils Brouwer, Fabien Bruneval, Aloïs Castellano, Emmanuel Castiel, Jean-Baptiste Charraud, Jean Clérouin, Michel Côté, Clément Duval, Alejandro Gallo, Frédéric Gendron, Grégory Geneste, Philippe Ghosez, Matteo Giantomassi, Olivier Gingras, Fernando Gómez‐Ortiz, Xavier Gonze, Félix Antoine Goudreault, Andreas Grüneis, Raveena Gupta, Bogdan Guster, D. R. Hamann, Xu He, Olle Hellman, N. A. W. Holzwarth, F. Jollet, Pierre Kestener, Ioanna-Maria Lygatsika, Olivier Nadeau, Lórien MacEnulty, Enrico Marazzi, Maxime Mignolet, David D. O’Regan, Robinson Outerovitch, Charles Paillard, Guido Petretto, Samuel Poncé, Francesco Ricci, Gian‐Marco Rignanese, Mauricio Rodríguez‐Mayorga, A. Romero, Samare Rostami, Miquel Royo, Marc Sarraute, Alireza Sasani, François Soubiran, Massimiliano Stengel, Christian Tantardini, Marc Torrent, Victor Trinquet, Vasilii Vasilchenko, David Waroquiers, Asier Zabalo, Austin Zadoks, Huazhang Zhang, Josef W. Zwanziger
Abstract
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-principle 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-principle 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.