Eremin et al., 2008 - Google Patents
Choice of the supercell with the optimum atomic configuration in simulation of disordered solid solutionsEremin et al., 2008
- Document ID
- 7793626014888209564
- Author
- Eremin N
- Deyanov R
- Urusov V
- Publication year
- Publication venue
- Glass Physics and Chemistry
External Links
Snippet
Different methods currently employed for choosing and analyzing model configurations of binary systems with isomorphous substitution in the sublattice were considered. A new more efficient algorithm was proposed for determining the most disordered atomic configuration of …
- 239000006104 solid solution 0 title abstract description 35
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/708—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for data visualisation, e.g. molecular structure representations, graphics generation, display of maps or networks or other visual representations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5086—Mechanical design, e.g. parametric or variational design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/702—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for analysis and planning of chemical reactions and syntheses, e.g. synthesis design, reaction prediction, mechanism elucidation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/701—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for molecular modelling, e.g. calculation and theoretical details of quantum mechanics, molecular mechanics, molecular dynamics, Monte Carlo methods, conformational analysis or the like
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Greplova et al. | Unsupervised identification of topological phase transitions using predictive models | |
Riebesell et al. | Matbench Discovery--A framework to evaluate machine learning crystal stability predictions | |
Abu-Odeh et al. | Efficient exploration of the high entropy alloy composition-phase space | |
Trumbore et al. | Radiocarbon nomenclature, theory, models, and interpretation: Measuring age, determining cycling rates, and tracing source pools | |
Machleidt | Nucleon-nucleon potentials in comparison: Physics or polemics? | |
Bhattacharya et al. | Efficient ab initio schemes for finding thermodynamically stable and metastable atomic structures: benchmark of cascade genetic algorithms | |
Thomas et al. | Simulating protein motions with rigidity analysis | |
Lahdelma et al. | Stochastic multicriteria acceptability analysis (SMAA) | |
Bai et al. | Phase mapper: Accelerating materials discovery with AI | |
Walsh et al. | Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networks | |
Seko et al. | Cluster expansion of multicomponent ionic systems with controlled accuracy: importance of long-range interactions in heterovalent ionic systems | |
Wales | Dynamical signatures of multifunnel energy landscapes | |
Eremin et al. | Choice of the supercell with the optimum atomic configuration in simulation of disordered solid solutions | |
Seko et al. | Grouping of structures for cluster expansion of multicomponent systems with controlled accuracy | |
Huang et al. | Estimating ferric iron content in clinopyroxene using machine learning models | |
Makarov et al. | Predictive modeling of physicochemical properties and ionicity of ionic liquids for virtual screening of novel electrolytes | |
Puchala et al. | CASM Monte Carlo: Calculations of the thermodynamic and kinetic properties of complex multicomponent crystals | |
Ober et al. | Thermodynamically informed priors for uncertainty propagation in first-principles statistical mechanics | |
Bigi | Could charm (& τ) transitions be the ‘poor princess’ providing a deeper understanding of fundamental dynamics?” or:“Finding novel forces | |
Lee et al. | Machine learning the spectral function of a hole in a quantum antiferromagnet | |
Xu et al. | Prediction of Molecular Conformation Using Deep Generative Neural Networks | |
Miloserdov | Classifying amorphous polymers for membrane technology basing on accessible surface area of their conformations | |
Szustakowski et al. | Less is more: towards an optimal universal description of protein folds | |
Hamer et al. | A method to project the rate kinetics of high dimensional barrier crossing problems onto a tractable 1D system | |
Rachel et al. | Quantum entanglement in condensed matter physics |