Many-body perturbation theory calculations using the yambo code (1902.03837v2)

Abstract: yambo is an open source project aimed at studying excited state properties of condensed matter systems from first principles using many-body methods. As input, yambo requires ground state electronic structure data as computed by density functional theory codes such as quantum-espresso and abinit. yambo's capabilities include the calculation of linear response quantities (both independent-particle and including electron-hole interactions), quasi-particle corrections based on the GW formalism, optical absorption, and other spectroscopic quantities. Here we describe recent developments ranging from the inclusion of important but oft-neglected physical effects such as electron-phonon interactions to the implementation of a real-time propagation scheme for simulating linear and non-linear optical properties. Improvements to numerical algorithms and the user interface are outlined. Particular emphasis is given to the new and efficient parallel structure that makes it possible to exploit modern high performance computing architectures. Finally, we demonstrate the possibility to automate workflows by interfacing with the yambopy and AiiDA software tools.

• The paper demonstrates new MBPT algorithms in Yambo, focusing on GW and BSE methods for precise excited state predictions.
• It highlights improved numerical efficiency with enhanced parallelism using MPI, OpenMP, and optimized numerical libraries.
• It emphasizes enhanced usability and reproducibility through automated Python workflows and a comprehensive testing suite.

Many-body Perturbation Theory Calculations using the Yambo Code

The paper presents an extensive overview of the Yambo software, an open-source project dedicated to studying excited state properties of condensed matter systems with first principles through many-body perturbation theory (MBPT). The main focus is on computational techniques such as the GW approximation and Bethe-Salpeter equation (BSE) for quasiparticle and neutral excitations, respectively. The Yambo code uses input from electronic structure data provided by density functional theory (DFT) packages like Quantum Espresso (QE) and ABINIT.

Key Developments and Features

1. Algorithms and Methods: Yambo has incorporated several important physical phenomena often neglected in simpler models. These include electron-phonon interactions, real-time propagation for evaluating both linear and non-linear optical properties, spin-orbit coupling, and temperature effects on electronic spectra. These advances have been implemented through a combination of new algorithms and refinements to existing ones, such as improved dipole matrix element calculations and the inclusion of plasmon-pole models for electronic screening.
2. Numerical Efficiency and Parallelism: Given the large computational demands of MBPT, Yambo emphasizes optimized performance on high-performance computing systems. Improvements include finer-grained parallelism using MPI and OpenMP paradigms and optimized use of numerical libraries such as ScaLAPACK for dense linear algebra tasks.
3. Usability and Interface: The Yambo project has improved its user interface, making the code more accessible and easier to use. The ability to automate workflows is highlighted through interfaces with Python-based tools like Yambopy and the AiiDA framework. These tools enable complex calculations and data management workflows to be specified, automated, and shared more efficiently.
4. Strong Focus on Reproducibility: Emphasis is placed on rigorous testing, supported by a comprehensive test suite ensuring consistency and validation of results across different computations and platforms.

Implications and Future Directions

The developments in the Yambo code have significant implications for both theoretical and practical applications in computational materials science. The ability to accurately predict electronic and optical properties of materials opens up extensive opportunities for the design and analysis of novel materials, including semiconductors, nanostructures, and other emerging technologies. The integration with modern infrastructure and parallel computing architectures facilitates large-scale calculations previously considered infeasible.

Theoretical Implications: The enriched model capabilities (e.g., handling of electron-phonon interactions and temperature effects) support deeper investigations into complex phenomena such as exciton-phonon coupling and dynamic screening effects. Yambo's users can expect more reliable predictions which can be directly validated against experimental data, thus helping calibrate and further refine theoretical models.

Practical Applications: Materials characterization going beyond simple ground-state properties to include excited states is crucial in fields such as photovoltaics, optoelectronics, and condensed matter physics, where understanding light-material interactions is vital. The Yambo code's comprehensive functionality makes it an indispensable tool for researchers in these areas.

Future Developments

Looking forward, ongoing work is anticipated to enhance the code's capability and performance further. This includes addressing current limitations in handling complex systems (e.g., incorporating ultrasoft pseudopotentials), refining large-scale parallelism, and possibly extending its applicability to quantum computing frameworks. The consistent community-driven development model ensures that Yambo stays at the frontier of computational research, equipped to tackle upcoming challenges in material science simulations.