High-throughput density functional theory (DFT) calculations are used to screen for new materials and conduct fundamental research in materials science and materials innovation. It involves computations on tens of thousands of compounds, and such a scale demands unique calculation and data management methodologies$^1$.

What are the different tools available for conducting high throughput density functional theory calculations?


  1. Jain, Anubhav, et al. "A high-throughput infrastructure for density functional theory calculations." Computational Materials Science 50.8 (2011): 2295-2310.

5 Answers 5


As previously stated, arguably the most mature and widely used set of tools is currently a combination of Pymatgen, FireWorks, Custodian, and Atomate (which is built upon the prior three Python packages). These tools were constructed as part of the Materials Project but have seen uses in other high-throughput DFT studies.

Another general workflow package for automating high-throughput DFT calculations is AFLOW, which has been used in constructing the AFLOWlib repository.

A similar package is qmpy, which has been used in constructing the Open Quantum Materials Database. As you can see, with each new database of DFT-computed properties, there are often specific workflow packages associated with them (typically because everyone's preferences and use-cases are different).

One of the benefits of the AiiDA package mentioned in the prior answer here is that it retains the calculation history of the entire workflow. Seeing as most robust workflows are somewhat dynamic in their settings (e.g. if an error appears, settings are changed), this can be useful for ensuring full transparency and reproducibility. AiiDA is what powers the data on the Materials Cloud.

There are also many field-specific packages that attempt to automate workflows specific to that field, oftentimes using one or more of the aforementioned packages. For instance, the Generalized Adsorption Simulation for Python (GASpy) code by the Ulissi group is well-suited for automating DFT calculations of inorganic surfaces, as outlined here. Rosen et al. have also developed a workflow for automating DFT calculations of metal–organic frameworks, as described here. Yet another is the MAterials Simulation Toolkit (MAST), which allows users to build up "recipes" for automated workflows in a similar manner as Atomate and was originally developed with a focus on simulating defects and diffusion in solids, as described here.

Edit #1: A new package, named [AMP$^2$], was just published for automating DFT calculations of crystals. It looks like it has several ease-of-use features, such as robust default settings for commonly computed properties, automatically testing if a hybrid functional should be used, and an algorithm to identify complex magnetic orderings in an automated fashion. The code is available to download here.

Edit #2: I somehow forgot to mention that the Atomic Simulation Environment (ASE), which some of the prior toolkits use, is an extremely useful and flexible resource that can be used to construct your own workflows with some Python scripting. It's not specifically meant for high-throughput but can be used that way.

  • 4
    $\begingroup$ "Rosen et al. have developed..." Great work! I'm impressed with the GitHub page too. $\endgroup$ Commented Jul 20, 2020 at 16:38
  • $\begingroup$ Why cant we use TensorFlow? How FireWorks different from TensorFlow which is most common for computation tasks $\endgroup$
    – gfdsal
    Commented Sep 11, 2021 at 22:06

I think these are some of the most popular:

pymatgen(+fireworks+custodian --> atomate)



  • seems relatively newer but the site has a ton of good documentation and tutorials

QMCPACK: is a modern high-performance open-source Quantum Monte Carlo (QMC) simulation code. QMCPACK is closely related to Nexus, which is another High Throughput Computing package for Quantum Chemistry calculations.

To add on to something Andrew Rosen said

"There are also many field-specific packages that attempt to automate workflows specific to that field"

I have been working on a Python packge for automating workflow for High Throughput Computing of Raman activities using GAMESS(US). Its called AutoGAMESS, funny enough Andrew was actually one of the reviewers on my JOSS paper on it.

There are also many other field specific packages that deserve some advertising here:

  1. AutoTST: A framework to perform automated transition state theory calculations related to reaction families common in combustion.
  2. ChemAlive: High Throughput Quantum Chemistry for Drug Discovery.
  3. ChemHTPS : A program suite to for conducting high-throughput computational screenings and generating chemical-modeling data.
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    $\begingroup$ Great answer @Cavenfish and good to see you here! My answer was very materials-specific, so I'm glad to see an answer focused more on molecular systems (and of course to see AutoGAMESS)! $\endgroup$ Commented Jul 21, 2020 at 7:06

The community-edited awesome materials informatics list has a section on "software frameworks", which includes many of the tools mentioned in the answers here & more.

Contributions welcome!

  • $\begingroup$ +1. Have you seen this question? mattermodeling.stackexchange.com/q/484/5 $\endgroup$ Commented Aug 14, 2020 at 23:35
  • $\begingroup$ +1 But while it's good to have a general resource, it would help if you could point to a specific example from the list that addresses the question. $\endgroup$
    – Tyberius
    Commented Aug 15, 2020 at 2:24
  • $\begingroup$ @NikeDattani Thanks, I hadn't seen the question but I also don't have a reference in mind that I could recommend (I simply haven't read any). $\endgroup$ Commented Aug 15, 2020 at 12:07
  • $\begingroup$ @Tyberius The question was what different tools are available for high-throughput DFT studies. I would say all the tools in this list address the question - I would not know which one to single out. $\endgroup$ Commented Aug 15, 2020 at 12:10


The MolSSI QCArchive project is designed for running, storing and accessing hundreds of millions of quantum chemistry calculations for individuals and groups of researchers at any scale. The project has been discussed in a recent article, WIREs e1491 (2020), which also has a chemRxiv preprint.


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