RDL logo
About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User Guide
​
​
Sign inGet started
​
​

About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User Guide

Sign inGet started
RDL logo

Verified research datasets. Instant access. Built for collaboration.

Navigation

About

Aims and Scope

Advisory Board Members

More

Who We Are?

Add Raw Data

User Guide

Legal

Privacy Policy

Terms of Service

Support

Got an issue? Email us directly.

Email: info@rawdatalibrary.netOpen Mail App
​
​

© 2025 Raw Data Library. All rights reserved.
PrivacyTerms
  1. Raw Data Library
  2. /
  3. Publications
  4. /
  5. libMBD: A general-purpose package for scalable quantum many-body dispersion calculations

Verified authors • Institutional access • DOI aware
50,000+ researchers120,000+ datasets90% satisfaction
Article
en
2023

libMBD: A general-purpose package for scalable quantum many-body dispersion calculations

0 Datasets

0 Files

en
2023
Vol 159 (17)
Vol. 159
DOI: 10.1063/5.0170972

Get instant academic access to this publication’s datasets.

Create free accountHow it works

Frequently asked questions

Is access really free for academics and students?

Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.

How is my data protected?

Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.

Can I request additional materials?

Yes, message the author after sign-up to request supplementary files or replication code.

Advance your research today

Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.

Get free academic accessLearn more
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaboration
Access Research Data

Join our academic network to download verified datasets and collaborate with researchers worldwide.

Get Free Access
Institutional SSO
Secure
This PDF is not available in different languages.
No localized PDFs are currently available.
Jan Hermann
Jan Hermann

Institution not specified

Verified
Jan Hermann
Martin Stöhr
Szabolcs Góger
+4 more

Abstract

Many-body dispersion (MBD) is a powerful framework to treat van der Waals (vdW) dispersion interactions in density-functional theory and related atomistic modeling methods. Several independent implementations of MBD with varying degree of functionality exist across a number of electronic structure codes, which both limits the current users of those codes and complicates dissemination of new variants of MBD. Here, we develop and document libMBD, a library implementation of MBD that is functionally complete, efficient, easy to integrate with any electronic structure code, and already integrated in FHI-aims, DFTB+, VASP, Q-Chem, CASTEP, and Quantum ESPRESSO. libMBD is written in modern Fortran with bindings to C and Python, uses MPI/ScaLAPACK for parallelization, and implements MBD for both finite and periodic systems, with analytical gradients with respect to all input parameters. The computational cost has asymptotic cubic scaling with system size, and evaluation of gradients only changes the prefactor of the scaling law, with libMBD exhibiting strong scaling up to 256 processor cores. Other MBD properties beyond energy and gradients can be calculated with libMBD, such as the charge-density polarization, first-order Coulomb correction, the dielectric function, or the order-by-order expansion of the energy in the dipole interaction. Calculations on supramolecular complexes with MBD-corrected electronic structure methods and a meta-review of previous applications of MBD demonstrate the broad applicability of the libMBD package to treat vdW interactions.

How to cite this publication

Jan Hermann, Martin Stöhr, Szabolcs Góger, Shayantan Chaudhuri, Bálint Aradi, Reinhard J. Maurer, Alexandre Tkatchenko (2023). libMBD: A general-purpose package for scalable quantum many-body dispersion calculations. , 159(17), DOI: https://doi.org/10.1063/5.0170972.

Related publications

Why join Raw Data Library?

Quality

Datasets shared by verified academics with rich metadata and previews.

Control

Authors choose access levels; downloads are logged for transparency.

Free for Academia

Students and faculty get instant access after verification.

Publication Details

Type

Article

Year

2023

Authors

7

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1063/5.0170972

Join Research Community

Access datasets from 50,000+ researchers worldwide with institutional verification.

Get Free Access