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QUARXiv by IJQC -- Supplementary Materials for Quantum Chemistry
IJQC Special Issue

This Special Issue of the Int. Journal of Quantum Chemistry features interactive
and executive elements aiming to bring Theoretical and Computational Chemistry
publishing in the 21th century, and to enhance reproducibility and transparency
in the presented research. 

https://onlinelibrary.wiley.com/journal/1097461x
Group Admins: Matteo Cavalleri, Alberto Pepe
 * Public Documents (5)
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Acceleration of Catalyst Discovery with Easy, Fast, and Reproducible
Computational Al...

Charles Griego

and 2 more

January 10, 2020
The expense of quantum chemistry calculations significantly hinders the search
for novel catalysts. Here, we provide a tutorial for using an easy and highly
cost-efficient calculation scheme called alchemical perturbation density
functional theory (APDFT) for rapid predictions of binding energies of reaction
intermediates and reaction barrier heights based on Kohn-Sham density functional
theory reference data. We outline standard procedures used in computational
catalysis applications, explain how computational alchemy calculations can be
carried out for those applications, and then present bench marking studies of
binding energy and barrier height predictions. Using a single OH binding energy
on the Pt(111) surface as a reference case, we use computational alchemy to
predict binding energies of 32 variations of this system with a mean unsigned
error of less than 0.05 eV relative to single-point DFT calculations. Using a
single nudged elastic band calculation for CH4 dehydrogenation on Pt(111) as a
reference case, we generate 32 new pathways with barrier heights having mean
unsigned errors of less than 0.3 eV relative to single-point DFT calculations.
Notably, this easy APDFT scheme brings no appreciable computational cost once
reference calculations are done, and this shows that simple applications of
computational alchemy can significantly impact DFT-driven explorations for
catalysts. To accelerate computational catalysis discovery and ensure
computational reproducibility, we also include Python modules that allow users
to perform their own computational alchemy calculations.Keywords ---
Computational catalysis, density functional theory (DFT), adsorption energies,
nudged elastic band calculations, binding energies, barrier heights 
Fitting Elephants in the Density Functionals Zoo: Statistical Criteria for the
Evalua...

Roberto Peverati

December 12, 2019
Counting parameters has become customary in the density functional theory
community as a way to infer the transferability of popular approximations to the
exchange–correlation functionals. Recent work in data science, however, has
demonstrated that the number of parameters of a fitted model is not related to
the complexity of the model itself, nor to its eventual overfitting. Using
similar arguments, we show here that it is possible to represent every modern
exchange–correlation functional approximation using just one single parameter.
This procedure proves the futility of the number of parameters as a measure of
transferability. To counteract this shortcoming, we introduce and analyze the
performance of three statistical criteria for the evaluation of the
transferability of exchange–correlation functionals. The three criteria are
called Akaike information criterion (AIC), Vapnik–Chervonenkis criterion (VCC),
and cross-validation criterion (CVC) and are used in a preliminary assessment to
rank 60 exchange–correlation functional approximations using the ASCDB database
of chemical data.
Turning chemistry into information for heterogeneous catalysis

Sergio Pablo-García

and 2 more

August 28, 2019
The growing generation of data and their wide availability has led to the
development of tools to produce, analyze and store this information.
Computational chemistry studies and especially catalytic applications often
yield a vast amount of chemical information that can be analyzed and stored
using these tools. In this manuscript we present a framework that automatically
performs a full automated procedure consisting in the transfer of an adsorbate
from a known metal slab to a new metal slab with similar packing. Our method
generates the new geometry and also performs the required calculations and
analysis to finally upload the processed data to an online database (ioChem-BD).
Two different implementations have been built, one to relocate minimum energy
point structures and the second to transfer transition states. Our framework
shows good performance for the minimum point location and a decent performance
for the transition state identification. Most of the failures occurred during
the transition state searches needed additional steps to fully complete the
process. Further improvements of our framework are required to increase the
performance of both implementations. These results point to the _avoidhuman_
path as a feasible solution for studies on very large systems that require a
significant amount of human resources and in consequence are prone to human
errors.
Open Chemistry, JupyterLab, REST, and Quantum Chemistry

Marcus D. Hanwell

and 7 more

June 07, 2019
Quantum chemistry must evolve if it wants to fully leverage the benefits of the
internet age, where the world wide web offers a vast tapestry of tools that
enable users to communicate and interact with complex data at the speed and
convenience of a button press. The Open Chemistry project has developed an open
source framework that offers an end-to-end solution for producing, sharing, and
visualizing quantum chemical data interactively on the web using an array of
modern tools and approaches. These tools build on some of the best open source
community projects such as Jupyter for interactive online notebooks, coupled
with 3D accelerated visualization, state-of-the-art computational chemistry
codes including NWChem and Psi4 and emerging machine learning and data mining
tools such as ChemML and ANI. They offer flexible formats to import and export
data, along with approaches to compare computational and experimental data.
Assessing Conformer Energies using Electronic Structure and Machine Learning
Methods

Dakota Folmsbee

and 1 more

January 16, 2019
We have performed a large-scale evaluation of current computational methods,
including conventional small-molecule force fields, semiempirical, density
functional, ab initio electronic structure methods, and current machine learning
(ML) techniques to evaluate relative single-point energies. Using up to 10 local
minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with
single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single
points to compare the correlation between different methods for both relative
energies and ordered rankings of minima. We find promise from current ML methods
and recommend methods at each tier of the accuracy-time tradeoff, particularly
the recent GFN2 semiempirical method, the B97-3c density functional
approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML
methods shows promise, particularly the ANI-1ccx variant trained in part on
coupled-cluster energies. Multiple methods suggest continued improvements should
be expected in both performance and accuracy.
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