Abinit 2025: New Capabilities for the Predictive Modeling of Solids and Nanomaterials
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 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.
Keywords
Cite
@article{arxiv.2507.08578,
title = {Abinit 2025: New Capabilities for the Predictive Modeling of Solids and Nanomaterials},
author = {Matthieu J. Verstraete and Joao Abreu and Guillaume E. Allemand and Bernard Amadon and Gabriel Antonius and Maryam Azizi and Lucas Baguet and Clementine Barat and Louis Bastogne and Romuald Bejaud and Jean-Michel Beuken and Jordan Bieder and Augustin Blanchet and Francois Bottin and Johann Bouchet and Julien Bouquiaux and Eric Bousquet and James Boust and Fabien Brieuc and Veronique Brousseau-Couture and Nils Brouwer Fabien Bruneval and Alois Castellano and Emmanuel Castiel and Jean-Baptiste Charraud and Jean Clerouin and Michel Cote and Clement Duval and Alejandro Gallo and Frederic Gendron and Gregory Geneste and Philippe Ghosez and Matteo Giantomassi and Olivier Gingras and Fernando Gomez-Ortiz and Xavier Gonze and Felix Antoine Goudreault and Andreas Gruneis and Raveena Gupta and Bogdan Guster and Donald R. Hamann and Xu He and Olle Hellman and Natalie Holzwarth and Francois Jollet and Pierre Kestener and Ioanna-Maria Lygatsika and Olivier Nadeau and Lorien MacEnulty and Enrico Marazzi and Maxime Mignolet and David D. O'Regan and Robinson Outerovitch and Charles Paillard and Guido Petretto and Samuel Ponce and Francesco Ricci and Gian-Marco Rignanese and Mauricio Rodriguez-Mayorga and Aldo H. Romero and Samare Rostami and Miquel Royo and Marc Sarraute and Alireza Sasani and Francois Soubiran and Massimiliano Stengel and Christian Tantardini and Marc Torrent and Victor Trinquet and Vasilii Vasilchencko and David Waroquiers and Asier Zabalo and Austin Zadoks and Huazhang Zhang and Josef Zwanziger},
journal= {arXiv preprint arXiv:2507.08578},
year = {2026}
}