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Materials Informatics
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/qsar.200420057openalex-snapshot
Virtual Screening and QSAR Formulations for Crystal Chemistry
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inferredcites →https://doi.org/10.1016/s0022-4596(03)00343-8openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0079-6425(00)00013-xopenalex-snapshot
Selection of manufacturing processes in design and the role of process modelling
inferredcites →https://doi.org/10.1016/s0169-4332(03)00918-8openalex-snapshot
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inferredcites →https://doi.org/10.1016/s0169-7439(02)00014-xopenalex-snapshot
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FactSage thermochemical software and databases
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Environmental biotechnology informatics
inferredcites →https://doi.org/10.1016/s1359-6454(99)00133-0openalex-snapshot
Non-generic concentrations for shape-memory alloys; the case of CuZnAl1a1aDedicated to Professor R. Fosdick on the occasion of his 60th birthday.
inferredcites →https://doi.org/10.1016/s1367-5931(00)00091-0openalex-snapshot
Functional diversity of compound libraries
inferredcites →https://doi.org/10.1016/s1389-0344(01)00099-5openalex-snapshot
Bioinformatics in proteomics
inferredcites →https://doi.org/10.1021/jp010450topenalex-snapshot
High Dimensional Model Representations
inferredcites →https://doi.org/10.1023/a:1008762206967openalex-snapshot
Calculation of bulk modulus of titanium alloys by first principles electronic structure theory
inferredcites →https://doi.org/10.1038/35051054openalex-snapshot
Computational design of direct-bandgap semiconductors that lattice-match silicon
inferredcites →https://doi.org/10.1038/35076576openalex-snapshot
Computational analysis of microarray data
inferredcites →https://doi.org/10.1038/46995openalex-snapshot
The inverse band-structure problem of finding an atomic configuration with given electronic properties
inferredcites →https://doi.org/10.1038/nature02011openalex-snapshot
Zero thermal expansion in YbGaGe due to an electronic valence transition
inferredcites →https://doi.org/10.1038/nrd941openalex-snapshot
Integration of virtual and high-throughput screening
inferredcites →https://doi.org/10.1098/rspa.1998.0208openalex-snapshot
Checks and estimates for material properties. I. Ranges and simple correlations
inferredcites →https://doi.org/10.1098/rspa.1998.0209openalex-snapshot
Estimates for material properties. II. The method of multiple correlations
inferredcites →https://doi.org/10.1103/physrevlett.82.3304openalex-snapshot
Deliberately Designed Materials for Optoelectronics Applications
inferredcites →https://doi.org/10.1103/physrevlett.91.135503openalex-snapshot
Predicting Crystal Structures with Data Mining of Quantum Calculations
inferredcites →https://doi.org/10.1126/science.1060512openalex-snapshot
Restricting Dislocation Movement in Transition Metal Carbides by Phase Stability Tuning
inferredcites →https://doi.org/10.1126/science.1081957openalex-snapshot
Multifunctional Alloys Obtained via a Dislocation-Free Plastic Deformation Mechanism
inferredcites →https://doi.org/10.1126/science.1083759openalex-snapshot
The More Elements, the Merrier
inferredcites →https://doi.org/10.1126/science.280.5366.1099openalex-snapshot
Predicting Properties from Scratch
inferredcites →https://doi.org/10.2481/dsj.1.19openalex-snapshot
The application of Principal Component Analysis to materials science data
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Materials Informatics
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Data Mining in Materials Science and Engineering
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Ceramic Genomics: Total Bond Order Density
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Alloy Design Approaches
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Machine learning in materials modeling—fundamentals and the opportunities in 2D materials
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Data Science, Machine Learning and Artificial Intelligence Applied to Metals and Alloys Research: Past, Present, and Future
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Rapid alloying in additive manufacturing using integrated computational materials engineering
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Modeling of processing–microstructure–properties relationships
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Alloy design approaches
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Machine learning for materials developments in metals additive manufacturing
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Critical Path Issues in Materials Design
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Strategy for the maximum extraction of information generated from combinatorial experimentation of Co-doped ZnO thin films
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Quantitative prediction of the aged state of Ni-base superalloys using PCA and tensor regression
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Materials informatics: From the atomic-level to the continuum
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Modern Data Analytics Approach to Predict Creep of High-Temperature Alloys
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Missing information and data fidelity in digital microstructure acquisition
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Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning
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Self-assembly of vertically orientated graphene nanostructures: Multivariate characterisation by Minkowski functionals and fractal geometry
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A statistical perspective for predicting the strength of metals: Revisiting the Hall–Petch relationship using machine learning
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Alloy informatics through ab initio charge density profiles: Case study of hydrogen effects in face-centred cubic crystals
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Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research
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Processing parameter correlations in material extrusion additive manufacturing
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Invited review: Machine learning for materials developments in metals additive manufacturing
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Data mining and knowledge discovery in materials science and engineering: A polymer nanocomposites case study
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Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advances
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Machine learning and robot-assisted synthesis of diverse gold nanorods via seedless approach
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Data-driven discovery of novel synthesizable MAX phase materials
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Approaches for strong electron correlation beyond DFT: Conventional and neural network quantum Monte Carlo
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Coupling statistical indentation and microscopy to evaluate micromechanical properties of materials: Application to viscoelastic behavior of irradiated mortars
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Data-driven computational mechanics
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Data Driven Computing with noisy material data sets
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A physics-constrained data-driven approach based on locally convex reconstruction for noisy database
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AI-driven bandgap engineering of Mg–Ca Co-doped HgS semiconductors: A hybrid ML–DL–DFT+U framework for next-generation solar energy materials
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A graph-theoretic approach for characterization of precipitates from atom probe tomography data
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Comprehensive mechanical property classification of vapor-grown carbon nanofiber/vinyl ester nanocomposites using support vector machines
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Image driven machine learning methods for microstructure recognition
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Multi-fidelity machine learning models for accurate bandgap predictions of solids
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Prediction of interstitial diffusion activation energies of nitrogen, oxygen, boron and carbon in bcc, fcc, and hcp metals using machine learning
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Aimsgb: An algorithm and open-source python library to generate periodic grain boundary structures
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Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications
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Application of materials informatics to vapor-grown carbon nanofiber/vinyl ester nanocomposites through self-organizing maps and clustering techniques
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MatCALO: Knowledge-enabled machine learning in materials science
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Machine learning models for the prediction of energy, forces, and stresses for Platinum
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A data science approach for advanced solid polymer electrolyte design
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A machine learning framework for the temporal evolution of microstructure during static recrystallization of polycrystalline materials simulated by cellular automaton
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Inverse design of composite metal oxide optical materials based on deep transfer learning and global optimization
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Artificial neural network potential for pure zinc
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Machine learning elastic constants of multi-component alloys
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Machine learning aided first-principles studies of structure stability of Co3(Al, X) doped with transition metal elements
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Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review
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Discovery of direct band gap perovskites for light harvesting by using machine learning
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Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review
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Graph theory-based structural analysis on density anomaly of silica glass
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Element-configuration dependent first-principles machine learning studies of multiple alloying effects on the structure stability of Co3(Al, W)
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A survey on knowledge representation in materials science and engineering: An ontological perspective
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Recent progress in machine learning strategies in designing, synthesizing and characterizing new age metal matrix composites
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A highly interpretable materials informatics approach for predicting microstructure-property relationship in fabric composites
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A triplet attention-enhanced deep learning approach to predict full-field stress of unidirectional CFRP composites with microvoids
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A morphology and porosity aware generative surrogate model for stress field prediction in CFRP composites with random void defects
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Bandgap prediction on small thermoelectric material dataset via instance-based transfer learning
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Degradation science: Mesoscopic evolution and temporal analytics of photovoltaic energy materials
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Atomistic calculations and materials informatics: A review
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Industrial materials informatics: Analyzing large-scale data to solve applied problems in R&D, manufacturing, and supply chain
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A multiscale model for the synthesis of thermosetting resins: From the addition reaction to cross-linked network formation
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Data analysis of multi-dimensional thermophysical properties of liquid substances based on clustering approach of machine learning
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Big-data-accelerated aperiodic Si/Ge superlattice prediction for quenching thermal conduction via pattern analysis
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Different applications of machine learning approaches in materials science and engineering: Comprehensive review
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