12 research outputs found
Levels of Metals in Kidney, Liver, and Muscle Tissue and their Influence on the Fitness for the Consumption of Wild Boar from Western Slovakia
High-Throughput Preparation and Properties Investigation of BNT Based Lead-Free Piezoelectric Ceramics
Interatomic bonds and the tensile anisotropy of trialuminides in the elastic limit: a density functional study for Al 3
Environmental study focused on the suitability of vehicle certifications using the new European driving cycle (NEDC) with regard to the affair “dieselgate” and the risks of NOx emissions in urban destinations
Stress Formulation in the All-Electron Full-Potential Linearized Augmented Plane Wave Method
Criteria of instability of copper and aluminium perfect crystals subjected to elastic deformation in the temperature range 0 – 400 K
Generalized-stacking-fault energy and twin-boundary energy of hexagonal close-packed Au: A first-principles calculation
Universal fragment descriptors for predicting properties of inorganic crystals
Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorganic crystalline material, reciprocating the available thermomechanical experimental data. The universality of the approach is attributed to the construction of the descriptors: Property-Labelled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules