Abinit 2025: New Capabilities for the Predictive Modeling of Solids and Nanomaterials

TL;DR

Abinit 2025 introduces advanced capabilities for predictive modeling of solids and nanomaterials, incorporating new methodologies, hardware optimizations, and automation tools to enhance accuracy, scalability, and usability in materials science research.

Contribution

The paper details novel features and technical improvements in Abinit over five years, including new methodologies, GPU acceleration, and automated workflows for large-scale materials simulations.

Findings

Extended abinit to GPU and parallel architectures

Developed new methods for excited states and response properties

Enabled high-throughput and automated materials calculations

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.