AFLOW-ML: A RESTful API for machine-learning predictions of materials properties
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Summary
AFLow-ML (AFLOW Machine underlineLearning) overcomes the problem by streamlining the use of the machine learning methods developed within the AFLOW consortium by providing an open RESTful API to directly access the continuously updated algorithms.
- Type
- preprint
- Published
- 2017-11-29
- Cited by
- 106
- References
- 68
- Access
- Open access
- OpenAlex
- https://openalex.org/W2768281325
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:36352803
Keywords
Workflow, Computer science, Unicode, Cloud computing, Python (programming language)
References
- The AFLOW standard for high-throughput materials science calculations
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- Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints
- Machine Learning Energies of 2 Million Elpasolite (ABC_2D_6) Crystals.
- AiiDA: Automated Interactive Infrastructure and Database for Computational Science
- High-throughput computational screening of thermal conductivity, Debye temperature, and Grüneisen parameter using a quasiharmonic Debye model
- Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
- An object-oriented scripting interface to a legacy electronic structure code
- Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
- ISIDA Property‐Labelled Fragment Descriptors
- Uncovering compounds by synergy of cluster expansion and high-throughput methods.
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- NIST materials science databases
- On the Prediction of Ternary Semiconductor Properties by Artificial Intelligence Methods
- Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
- Performance of neural networks in materials science
- Using support vector regression for the prediction of the band gap and melting point of binary and ternary compound semiconductors
- The Computational Materials Repository
- Ab initio molecular dynamics for liquid metals.
- Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling
Cited by
- Autonomous data-driven design of inorganic materials with AFLOW.
- Automated Computation of Materials Properties
- AFLOW-QHA3P: Robust and automated method to compute thermodynamic properties of solids
- Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery
- Machine Learning, Phase Stability, and Disorder with the Automatic Flow Framework for Materials Discovery
- Exploring materials band structure space with unsupervised machine learning
- Experimental Investigation and Thermodynamic Calculation of Ni–Al–La Ternary System in Nickel-Rich Region: A New Intermetallic Compound Ni2AlLa
- DLHub: Model and Data Serving for Science
- Applying a fuzzy interval ordered weighted averaging aggregation fusion to nondestructive determination of retained austenite phase in D2 tool steel
- From DFT to machine learning: recent approaches to materials science–a review
- Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models.
- Linking plastic heterogeneity of bulk metallic glasses to quench-in structural defects with machine learning
- Machine Learning for Computational Heterogeneous Catalysis
- Predicting superhard materials via a machine learning informed evolutionary structure search
- Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries.
- PANNA: Properties from Artificial Neural Network Architectures
- Artificial intelligence for materials discovery
- Publishing and Serving Machine Learning Models with DLHub
- Integration of machine learning approaches for accelerated discovery of transition-metal dichalcogenides as Hg0 sensing materials
- The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research
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