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The manifest and store data of 870,515 Android mobile applications
We built a crawler to collect data from the Google Play store including the application's metadata and APK files. The manifest files were extracted from the APK files and then... -
Malaria Prevalence in Large Densely-Populated Urban Holoendemic sub-Saharan W...
The Ibadan, Nigeria malaria-prevalence dataset 1996 to 2017. When using the dataset please also cite: Brown, B.J., Manescu, P., Przybylski, A.A. et al. Data-driven malaria... -
Sampling enhancement by metadynamics driven by machine learning and de novo p...
Folding of villin miniprotein was studied by parallel tempering metadynamics driven by machine learning. To obtain a training set for machine learning, we generated a large... -
Mining the C-C Cross-Coupling Genome using Machine Learning
Applications of machine-learning (ML) techniques to the study of catalytic processes have begun to appear in the literature with increasing frequency. The computational speed up... -
Machine Learning Majorite barometer
A machine learning barometer (using Random Forest Regression) to calculate equilibration pressure for majoritic garnets Updated 04/02/21 (21/01/21) (10/12/20): ** The... -
Structure-property maps with kernel principal covariates regression
Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building... -
Machine learning for metallurgy V: A neural-network potential for zirconium d...
The mechanical performance—including deformation, fracture, and radiation damage—of zirconium is determined at the atomic scale. With Zr and its alloys extensively used in the... -
Geometric landscapes for material discovery within energy-structure-function ...
Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from... -
Deep learning the slow modes for rare events sampling
The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational... -
Learning local equivariant representations for large-scale atomistic dynamics
A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural... -
Self-Perceived Loneliness and Depression During the COVID-19 Pandemic: a Two-...
This is the README file for the scripts of the preprint "Self-Perceived Loneliness and Depression During the COVID-19 Pandemic: a Two-Wave Replication Study" by Carollo et al.... -
Diversifying databases of metal organic frameworks for high-throughput comput...
By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. When making new databases of such hypothetical... -
Incorporating long-range physics in atomic-scale machine learning
The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a... -
EPISURG: a dataset of postoperative magnetic resonance images (MRI) for quant...
EPISURG is a clinical dataset of T1-weighted magnetic resonance images (MRI) from 430 epileptic patients who underwent resective brain surgery at the National Hospital of... -
Fast Bayesian force fields from active learning: study of inter-dimensional t...
Gaussian process (GP) regression is one promising technique of constructing machine learning force fields with built-in uncertainty quantification, which can be used to monitor... -
Reaction-based machine learning representations for predicting the enantiosel...
Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a... -
Structure determination of an amorphous drug through large-scale NMR predictions
Knowledge of the structure of amorphous solids can direct, for example, the optimization of pharmaceutical formulations, but atomic-level structure determination in amorphous... -
Predicting hot-electron free energies from ground-state data
Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation... -
On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomist...
Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training... -
Simulating solvation and acidity in complex mixtures with first-principles ac...
Set of inputs to perform the calculations reported in the paper. The i-pi input enables to perform molecular dynamics / metadynamics / REMD / PIMD simulations, with adequate...
