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 ...
There are several such studies, particularly focusing on the machine-learning of critical temperatures.
"Machine learning modeling of superconducting critical temperature"
"An acceleration search method of higher T c superconductors by a machine learning algorithm"
"Can machine learning identify the next high-temperature superconductor? Examining ...
After a little research I found a great article , which provides a good overview to what I asked above in Figure 2. Summarising in a table:
| Method | effort | reliability | system size |
| Interatomic potentials | high | high* / low* | 10^8 |
| Linear-scaling DFT ...
I think these are some of the most popular:
pymatgen(+fireworks+custodian --> atomate)
these are all part of materials project, there are some good references available
this is a database like materials project but it's been used for high-throughput calculations
seems relatively newer but the site has a ton of good documentation and ...
tldr; There is not necessarily a "best" fingerprint method because there are many, many ways to generate fingerprints. ECFP4 or ECFP6 with >2048 bits is usually decent.
Generally speaking, a fingerprint is some large vector, typically binary, used for similarity-based screening and more recently machine learning. The elements of the fingerprint are some ...
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 ...
I am sure there are A LOT of authors publishing papers to answer this very question. Mainly because the theories employed have face a paradigm since "one-size-doesn't-fit-all".
Here are the variables that affect this use of a certain method:
(i) Molecular models or periodic solids
(ii) Chemical Accuracy (energies with accuracy of < 1kcal/mol) - e.g. ...
I recently made charge densities available for the MOFs and coordination polymers of the Quantum MOF (QMOF) Database. Please read the GitHub page for details on how to access the charge densities. That being said, given the large size of the files, I wish you luck downloading it in bulk.
Here you can find some materials (article reviews & books) about Materials Informatics:
Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science.
Machine learning in materials informatics: recent applications and prospects.
Materials Informatics: Methods, Tools, and Applications.
I don't think there is an universal answer: the fingerprint is probably highly property dependent, since there is no single property that fully characterizes a molecule. (This is also the likely reason for there being dozens of different definitions for fingerprints; the same situation exists in partial charge analysis that lack a unique definition.)
Basically, convex hull is a plot of formation energy with respect to the composition which connects phases that are lower in energy than any other phases or a linear combination of phases at that composition. Phases that lie on the convex hull are thermodynamically stable and the ones above it are either metastable or unstable. This plot can only give an ...
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.