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Data for: ML-TWiX: A Machine Learning approach for Total Water storage anomal...
The ML-TWiX dataset provides a globally gridded reconstruction of Total Water Storage Anomalies (TWSA) from January 1980 to December 2012. This dataset is designed to extend the... -
Decoding fairness motivations - repository
Repository of Data, Scripts and Files Neural Data: Files: Neural_data_D1.zip - the neural data from study 1 Neural_data_D2.zip - the neural data from study 2 ROIs.zip -... -
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... -
The rule of four: anomalous stoichiometries of inorganic compounds
Why are materials with specific characteristics more abundant than others? This is a fundamental question in materials science and one that is traditionally difficult to tackle,... -
The rule of four: anomalous stoichiometries of inorganic compounds
Why are materials with specific characteristics more abundant than others? This is a fundamental question in materials science and one that is traditionally difficult to tackle,... -
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... -
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-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... -
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... -
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... -
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-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... -
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... -
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... -
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... -
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... -
A FEM dataset of Ge film profiles and elastic energies for machine learning a...
Machine Learning (ML) can be conveniently applied to continuum materials simulations, allowing for the investigation of larger systems and longer timescales, pushing the limits... -
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 the exciton properties of azo-dyes
The ab initio determination of the character and properties of electronic excited states (ES) is the cornerstone of modern theoretical photochemistry. Yet, traditional ES...
