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Revised MD17 dataset
The original MD17 dataset (http://quantum-machine.org/datasets/#md-datasets) [Chemiela et al. Sci. Adv. 3(5), e1603015, 2017] contains numerical noise. Thus, any numbers... -
Simulating the ghost: quantum dynamics of the solvated electron
The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of... -
Learning the energy curvature versus particle number in approximate density f...
The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions... -
Local kernel regression and neural network approaches to the conformational l...
The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of... -
I’m alone but not lonely. U-shaped pattern of perceived loneliness during the...
In the past months, many countries have adopted varying degrees of lockdown restrictions to control the spread of the COVID-19 virus. According to the existing literature, some... -
Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Co...
This work combines a machine learning potential energy function with a modular enhanced sampling scheme to obtain statistically converged thermodynamical properties of flexible... -
Electron density learning of non-covalent systems
Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the... -
Learning on-top: regressing the on-top pair density for real-space visualizat...
The on-top pair density [Π(r)] is a local quantum chemical property, which reflects the probability of two electrons of any spin to occupy the same position in space. Simplest... -
Replication Package for "MEG: Multi-objective Ensemble Generation for Defect ...
This is a replication package for the paper "MEG: Multi-objective Ensemble Generation for Defect Prediction", accepted at ESEM 2022. The compressed package is ~42MB and the... -
FireNet
FireNet FireNet is an open ML training dataset for visual recognition of fire safety equipment. Our dataset directly links the objects to their respective Uniclass, the... -
Machine learning for metallurgy: a neural network potential for Al-Cu
High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting... -
Impact of quantum-chemical metrics on the machine learning prediction of elec...
Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain... -
Machine learning for metallurgy: a neural network potential for Al-Cu-Mg
High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting... -
Novel techniques for characterising graphene nanoplatelets using Raman spectr...
A significant challenge for graphene nanoplatelet (GNP) suppliers is the meaningful characterisation of platelet morphology in an industrial environment. This challenge is... -
Machine learning for metallurgy: neural network potentials for Al-Cu-Mg and A...
Most metallurgical properties, e.g., dislocation propagation, precipitate formation, can only be fully understood atomistically but most phenomena and quantities of interest... -
A New Kind of Atlas of Zeolite Building Blocks
We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whether the structural diversity found in this database can be well-represented... -
Machine learning for metallurgy: a neural network potential for Al-Mg-Si
High-strength metal alloys achieve their performance via careful control of the nucleation, growth, and kinetics of precipitation. Alloy mechanical properties are then... -
Machine learning for metallurgy: a neural network potential for Al-Cu
High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting... -
Machine learning for metallurgy: a neural network potential for Al-Cu
High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting... -
GLips - German Lipreading Dataset
The German Lipreading dataset consists of 250,000 publicly available videos of the faces of speakers of the Hessian Parliament, which was processed for word-level lip reading...
