Abinit 2025: New capabilities for the predictive modeling of solids and nanomaterials

MJ Verstraete and J Abreu and GE Allemand and B Amadon and G Antonius and M Azizi and L Baguet and C Barat and L Bastogne and R Béjaud and JM Beuken and J Bieder and A Blanchet and F Bottin and J Bouchet and J Bouquiaux and E Bousquet and J Boust and F Brieuc and V Brousseau- Couture and N Brouwer and F Bruneval and A Castellano and E Castiel and JB Charraud and J Clérouin and M Côté and C Duval and A Gallo and F Gendron and G Geneste and P Ghosez and M Giantomassi and O Gingras and F Gómez-Ortiz and X Gonze and FA Goudreault and A Grüneis and R Gupta and B Guster and DR Hamann and X He and O Hellman and N Holzwarth and F Jollet and P Kestener and IM Lygatsika and O Nadeau and L Macenulty and E Marazzi and M Mignolet and DD O'Regan and R Outerovitch and C Paillard and G Petretto and S Poncé and F Ricci and GM Rignanese and M Rodriguez- Mayorga and AH Romero and S Rostami and M Royo and M Sarraute and A Sasani and F Soubiran and M Stengel and C Tantardini and M Torrent and V Trinquet and V Vasilchenko and D Waroquiers and A Zabalo and A Zadoks and HZ Zhang and J Zwanziger, JOURNAL OF CHEMICAL PHYSICS, 163, 164126 (2025).

DOI: 10.1063/5.0288278

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.

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