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Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep...
Output from electronic structure code (Quantum Espresso) that serves as training data for the machine-learning workflow of the related scientific publication... -
Teaching ML in Compact Courses
This talk summarizes the experiences made with teaching Machine Learning within compact events that stretch over several days to a week maximum. Both speakers explain pitfalls... -
Ranking the synthesizability of hypothetical zeolites with the sorting hat
Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided... -
Global free-energy landscapes as a smoothly joined collection of local maps
This repository contains the scripts that were used to run the calculations that present a new biasing technique, the Adaptive Topography of Landscape for Accelerated Sampling... -
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... -
Understanding the diversity of the metal-organic framework ecosystem
By combining metal nodes and organic linkers one can make millions of different metal-organic frameworks (MOFs). At present over 90,000 MOFs have been synthesized and there are... -
Accurate and scalable multi-element graph neural network force field and mole...
Data includes the the ab initio molecular dynamic simulation of Li7P3S11 that was used to measure the performance of the GNNFF. The data is divided into training and testing... -
Machine learning of superconducting critical temperature from Eliashberg theory
The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional electron-phonon superconductors, including the retardation of the interaction and... -
eQM7: a dataset for small molecules with Foster-Boys centers
The electron QM7 (eQM7) dataset is created with the purpose of training and validating polarizable (machine learning) force fields on non-equilibrium configurations of small... -
Ranking the synthesizability of hypothetical zeolites with the sorting hat
Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided... -
Raman spectra of 2D titanium carbide MXene from machine-learning force field ...
MXenes represent one of the largest class of 2D materials with promising applications in many fields and their properties tunable by the surface group composition. Raman... -
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics i...
Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and... -
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... -
Adsorbate chemical environment-based machine learning framework for heterogen...
Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate... -
A transferable force field for gallium nitride crystal growth from the melt u...
Atomic-scale simulations of reactive processes have been stymied by two factors: the general lack of a suitable semi-empirical force field on the one hand, and the impractically... -
Transferable Machine-Learning Model of the Electron Density
The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding... -
Symmetry-based computational search for novel binary and ternary 2D materials
We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a combinatorial engine' that constructs... -
SPAᴴM: the spectrum of approximated hamiltonian matrices representations
Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical... -
Shadow-light images of simulated 25 classes of surface roughness for automati...
Many relationships important to civil engineering depend on surface roughness (morphology). Examples are the bond strength between concrete layers, the adhesion of a wheel to... -
Data-driven simulation and characterisation of gold nanoparticles melting
We develop efficient, accurate, transferable, and interpretable machine learning force fields for Au nanoparticles, based on data gathered from Density Functional Theory...
