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Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
Here we present 1,000 structures each of a water monomer, water dimer, Zundel cation and bulk water used to train tensorial machine-learning models in Phys. Rev. Lett. 120,... -
Machine learning meets volcano plots: Computational discovery of cross-coupli...
The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the... -
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... -
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... -
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... -
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... -
Data-driven studies of magnetic two-dimensional materials
We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A2B2X6, based on... -
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... -
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... -
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... -
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... -
Surface reconstructions and premelting of the (100) CaF2 surface
In this work, surface reconstructions on the (100) surface of CaF2 are comprehensively investigated. The configurations were explored by employing the Minima Hopping Method... -
Mapping tick dynamics and tick bite risk using data-driven approaches and vol...
This deposit contains the materials used during the development of this PhD thesis. During this research, we applied machine learning methods to obtain new insights about tick... -
Sample data file with TOAR air quality data for machine learning excercise
This file has been obtained from the Tropospheric Ozone Assessment Report database described by Schultz, M.G. et al., Elementa Sci. Anthrop., 2017,... -
Acoustic Data Building Toolset
This folder contains data and software tools (in python) that can be used in experiments with phoneme recognition in speech samples recorder in Polish. Acoustic data used here... -
WMT18 Quality Estimation Shared Task Test Data
Test data for the WMT18 QE task. Train data can be downloaded from http://hdl.handle.net/11372/LRT-2619. This shared task will build on its previous six editions to further... -
WMT18 Quality Estimation Shared Task Training and Development Data
Training and development data for the WMT18 QE task. Test data will be published as a separate item. This shared task will build on its previous six editions to further examine... -
WMT16 APE Shared Task Data - Reference sentences
Training, development and test data consist in German sentences belonging to the IT domain and already tokenized. These sentences are the references of the data released for the... -
WMT17 Quality Estimation Shared Test Data
Test data for the WMT17 QE task. Train data can be downloaded from http://hdl.handle.net/11372/LRT-1974 This shared task will build on its previous five editions to further... -
WMT17 Quality Estimation Shared Task Training and Development Data
Training and development data for the WMT17 QE task. Test data will be published as a separate item. This shared task will build on its previous five editions to further examine...