Materials Project is an open database of computed materials properties aiming to accelerate materials science research. The resources in this OpenData dataset contain the raw, parsed, and build data products.

Journal of Materials Science Materials in Electronics FAQ - ResearchHelpDesk - The Journal of Materials Science: Materials in Electronics is an established refereed companion to the Journal of Materials Science. It publishes papers on materials and their applications in modern electronics, covering the ground between fundamental science, such as semiconductor physics, and work concerned specifically with applications. It explores the growth and preparation of new materials, as well as their processing, fabrication, bonding and encapsulation, together with the reliability, failure analysis, quality assurance and characterization related to the whole range of applications in electronics. The Journal presents papers in newly developing fields such as low dimensional structures and devices, optoelectronics including III-V compounds, glasses and linear/non-linear crystal materials and lasers, high Tc superconductors, conducting polymers, thick film materials and new contact technologies, as well as the established electronics device and circuit materials. Abstracted and indexed in BFI List CNKI Chemical Abstracts Service (CAS) Current Contents Collections / Electronics & Telecommunications Collection Current Contents/Engineering, Computing and Technology Current Contents/Physical, Chemical and Earth Sciences Dimensions EBSCO Applied Science & Technology Source EBSCO Computers & Applied Sciences Complete EBSCO Discovery Service EBSCO Engineering Source EBSCO OmniFile EBSCO STM Source EBSCO Science Full Text Select EI Compendex Google Scholar INIS Atomindex INSPEC Institute of Scientific and Technical Information of China Japanese Science and Technology Agency (JST) Journal Citation Reports/Science Edition Naver OCLC WorldCat Discovery Service ProQuest Abstracts in New Technologies and Engineering (ANTE) ProQuest Advanced Technologies & Aerospace Database ProQuest Central ProQuest Electronics and Communications Abstracts ProQuest Engineered Materials Abstracts ProQuest Engineering ProQuest METADEX (Metals Abstracts) ProQuest Materials Science and Engineering Database ProQuest SciTech Premium Collection ProQuest Technology Collection ProQuest-ExLibris Primo ProQuest-ExLibris Summon SCImago SCOPUS Science Citation Index Science Citation Index Expanded (SciSearch) Semantic Scholar TD Net Discovery Service UGC-CARE List (India) WTI Frankfurt eG

This is a companion repository for a paper published in Scientific Data with the title and authors given above, whose abstract is below:There has been an ongoing need for information-rich databases in the mechanical-engineering domain to aid in data-driven materials science. To address the lack of suitable property databases, this study employs the latest version of the chemistry-aware natural-language-processing (NLP) toolkit, ChemDataExtractor, to automatically curate a comprehensive materials database of key stress-strain properties. The database contains information about materials and their cognate properties: ultimate tensile strength, yield strength, fracture strength, Young’s modulus, and ductility values. 720,308 data records were extracted from the scientific literature and organized into machine-readable databases formats. The extracted data have an overall precision, recall and F-score of 82.03%, 92.13% and 86.79%, respectively. The resulting database has been made publicly available, aiming to facilitate data-driven research and accelerate advancements within the mechanical-engineering domain.

Polymer engineering and science Impact Factor 2024-2025 - ResearchHelpDesk - Polymer engineering and science - Every day, the Society of Plastics Engineers (SPE) takes action to help companies in the plastics industry succeed. How? By spreading knowledge, strengthening skills and promoting plastics. Employing these vital strategies, Polymer engineering and science - SPE has helped the plastics industry thrive for over 60 years. In the process, we've developed a 25,000-member network of leading engineers and other plastics professionals, including technicians, salespeople, marketers, retailers, and representatives from tertiary industries. For more than 30 years, Polymer Engineering & Science has been one of the most highly regarded journals in the field, serving as a forum for authors of treatises on the cutting edge of polymer science and technology. The importance of PE&S is underscored by the frequent rate at which its articles are cited, especially by other publications - literally thousands of times a year. Engineers, researchers, technicians, and academicians worldwide are looking to PE&S for the valuable information they need. There are special issues compiled by distinguished guest editors. These contain proceedings of symposia on such diverse topics as polyblends, mechanics of plastics and polymer welding. Abstracting and Indexing Information Academic ASAP (GALE Cengage) Advanced Technologies & Aerospace Database (ProQuest) Applied Science & Technology Index/Abstracts (EBSCO Publishing) CAS: Chemical Abstracts Service (ACS) CCR Database (Clarivate Analytics) Chemical Abstracts Service/SciFinder (ACS) Chemistry Server Reaction Center (Clarivate Analytics) ChemWeb (ChemIndustry.com) Chimica Database (Elsevier) COMPENDEX (Elsevier) Current Contents: Engineering, Computing & Technology (Clarivate Analytics) Current Contents: Physical, Chemical & Earth Sciences (Clarivate Analytics) Expanded Academic ASAP (GALE Cengage) InfoTrac (GALE Cengage) Journal Citation Reports/Science Edition (Clarivate Analytics) Materials Science & Engineering Database (ProQuest) PASCAL Database (INIST/CNRS) Polymer Library (iSmithers RAPRA) ProQuest Central (ProQuest) ProQuest Central K-462 Reaction Citation Index (Clarivate Analytics) Research Library (ProQuest) Research Library Prep (ProQuest) Science Citation Index (Clarivate Analytics) Science Citation Index Expanded (Clarivate Analytics) SciTech Premium Collection (ProQuest) SCOPUS (Elsevier) STEM Database (ProQuest) Technology Collection (ProQuest) Web of Science (Clarivate Analytics)

The zip file includes XYZ files and topology data for the stacked 2D structures and grain boundaries analyzed in the article "Automated Identification of Bulk Structures, Two-Dimensional Materials, and Interfaces Using Symmetry-Based Clustering".

The zip file is organized into two main folders: one for stacked 2D structures and another for grain boundaries. Each folder contains a subfolder with XYZ files that provide structural information and a topology subfolder that includes data such as atomic cluster assignments and unit cell information for each cluster.

For the stacked structures, the file names indicate the materials from which the structures are built. Materials are separated by underscores ( _ ), and their order in the file name corresponds to their order in the topology file. For the grain boundaries, the file names include the grain orientation, specified as Miller indices.

Detailed information on the methodology and analysis can be found in the “Methods” section of the article.

Slides of the talk "Using data mining to identify new research avenues", given by Adam Stevenson at the NSF Ceramics Workshop on September 12, 2016.

Structure-formation energy pairs from February 2022 version of MP database.

Journal of Materials Science Materials in Electronics CiteScore 2024-2025 - ResearchHelpDesk - The Journal of Materials Science: Materials in Electronics is an established refereed companion to the Journal of Materials Science. It publishes papers on materials and their applications in modern electronics, covering the ground between fundamental science, such as semiconductor physics, and work concerned specifically with applications. It explores the growth and preparation of new materials, as well as their processing, fabrication, bonding and encapsulation, together with the reliability, failure analysis, quality assurance and characterization related to the whole range of applications in electronics. The Journal presents papers in newly developing fields such as low dimensional structures and devices, optoelectronics including III-V compounds, glasses and linear/non-linear crystal materials and lasers, high Tc superconductors, conducting polymers, thick film materials and new contact technologies, as well as the established electronics device and circuit materials. Abstracted and indexed in BFI List CNKI Chemical Abstracts Service (CAS) Current Contents Collections / Electronics & Telecommunications Collection Current Contents/Engineering, Computing and Technology Current Contents/Physical, Chemical and Earth Sciences Dimensions EBSCO Applied Science & Technology Source EBSCO Computers & Applied Sciences Complete EBSCO Discovery Service EBSCO Engineering Source EBSCO OmniFile EBSCO STM Source EBSCO Science Full Text Select EI Compendex Google Scholar INIS Atomindex INSPEC Institute of Scientific and Technical Information of China Japanese Science and Technology Agency (JST) Journal Citation Reports/Science Edition Naver OCLC WorldCat Discovery Service ProQuest Abstracts in New Technologies and Engineering (ANTE) ProQuest Advanced Technologies & Aerospace Database ProQuest Central ProQuest Electronics and Communications Abstracts ProQuest Engineered Materials Abstracts ProQuest Engineering ProQuest METADEX (Metals Abstracts) ProQuest Materials Science and Engineering Database ProQuest SciTech Premium Collection ProQuest Technology Collection ProQuest-ExLibris Primo ProQuest-ExLibris Summon SCImago SCOPUS Science Citation Index Science Citation Index Expanded (SciSearch) Semantic Scholar TD Net Discovery Service UGC-CARE List (India) WTI Frankfurt eG

1. Image of the Alexandria database state corresponding to the paper "Improving machine-learning models in materials science through large datasets".

Static pbe calculations for 1D, 2D, 3D compounds can be found in 1D_pbe.tar.gz, 2D_pbe.tar.gz, 3D_pbe.tar.gz in batches of 100k materials. The latter also contains a separate convex hull pickle with all compounds on the pbe convex hull (convex_hull_pbe_2023.12.29.json.bz2) and a list of prototypes in the database (prototypes.json.bz2). The systematic 3D calculations performed for the article Improving machine-learning models in materials science through large datasets (in the paper referred to as round 2 and 3) can be found by the location keyword in the data dictionary of each ComputedStructureEntry containing "cgat_comp/quaternaries" (round 2) and "cgat_comp2/" (round 3). Round 1 (10.1002/adma.202210788) can be found under "cgat_comp/ternaries", ""cgat_comp/binaries".

Static pbesol calculations for 3D compounds can be found in 3D_ps.tar (still zip compressed) in batches of 100k materials. The folder also contains a separate convex hull pickle with all compounds on the pbesol convex hull (convex_hull_ps_2023.12.29.json.bz2).

Static scan calculations for 3D compounds can be found in 3D_scan.tar (still zip compressed) in batches of 100k materials. The folder also contains a separate convex hull pickle with all compounds on the scan convex hull (convex_hull_scan_2023.12.29.json.bz2).

Geometry relaxation curves for 1D and 2D and 3D compounds calculated with PBE can be found in geo_opt_1D.tar.gz, geo_opt_2D.tar.gz. and geo_opt_3D.tar. Each file in each folder contains a batch of up to 10k relaxation trajectories.

PBESOL relaxation trajectories for 3D compounds can be found in geo_opt_ps.tar

1. Crystal graph attention networks to predict the volume (volume_round_3.tar.gz) and distance to the convex hull (e_above_hull_round_3.tar.gz) trained for the paper "Improving machine-learning models in materials science through large datasets".

Can be used with the code at https://github.com/hyllios/CGAT/tree/main/CGAT.Note will predict the distance to the convex hull not normalized per atom when using the code on the github.

1. Alignn models as well as m3gnet and mace models corresponding to the publication can be found in alexandria_v2.tar.gz

2. scripts.tar.gz Some scripts used for generating CGAT input data/ performing parallel predictions and for relaxations with m3gnet/mace force fields

In addition to being the core quantity in density functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since it contains information about all the electrons in the system. With increasing utilization of machine-learning tools in materials sciences, a data-rich object like the charge density can be utilized in a wide range of applications. The database presented here provides a modern and user-friendly interface for a large and continuously updated collection of charge densities as part of the Materials Project. In addition to the charge density data, we provide the theory and code for changing the representation of the charge density which should enable more advanced machine-learning studies for the broader community.

TCSP 2.0 templte database, it includes the Materials Project (MP) database, Materials Cloud database (both 2D and 3D), The Computational 2D Materials Database (C2DB), and Graph Networks for Materials Science database(GNoME).

Materials Project Computed Properties Dataset

  Welcome to the Dataset!

Dive into the fascinating world of materials science with the Materials Project Computed Properties Dataset! This comprehensive collection features computed properties for a vast array of materials, sourced from the renowned Materials Project database. Whether you’re a researcher exploring new materials, a data scientist building predictive models, or a student curious about how materials behave, this… See the full description on the dataset page: https://huggingface.co/datasets/Allanatrix/Materials.

Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven ex- ploration and design of amorphous materials is hampered by the absence of a com- prehensive database covering a broad chemical space. In this work, we present the largest computed amorphous materials database to date, generated from sys- tematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductiv- ity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching amorphous materials provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in uni- versal machine learning potentials, impacting design beyond that of non-crystalline materials.

Effective mass and thermoelectric properties of 8924 compounds in The Materials Project database that are calculated by the BoltzTraP software package run on the GGA-PBE or GGA+U density functional theory calculation results. The properties are reported at the temperature of 300 Kelvin and the carrier concentration of 1e18 1/cm3.Available as Monty Encoder encoded JSON and as CSV. Recommended access method is with the matminer Python package using the datasets module. Note:* When doing machine learning, to avoid data leakage, one may want to only use the formula and structure data as features. For example, S_n is strongly correlated with PF_n and usually when one is available the other one is available too.* It is recommended that dos and bandstructure objects are retrieved from Materials Project and then use dos, bandstructure and composition featurizers to generate input features.Note on citations: If you found this dataset useful and would like to cite it in your work, please be sure to cite its original sources below rather than or in addition to this page.Dataset described in:Ricci, F. et al. An ab initio electronic transport database for inorganic materials. Sci. Data 4:170085 doi: 10.1038/sdata.2017.85 (2017).Data converted from json files available on Dryad (see references 3-4):Ricci F, Chen W, Aydemir U, Snyder J, Rignanese G, Jain A, Hautier G (2017) Data from: An ab initio electronic transport database for inorganic materials. Dryad Digital Repository. https://doi.org/10.5061/dryad.gn001

Nowadays, we are witnessing a tremendous increase in data generation enabled by advances in experimental techniques and theoretical and computational developments. This availability of data, associated with new tools and technologies capable of storing and processing that data, culminated in the so-called data science. One of the most prominent areas (machine learning), which aims to identify correlations and patterns in the data sets. These algorithms have been used for decades in different areas. Only recently the community introduced its application for materials, due to the creation, standardization, and consolidation of consistent databases. The use of these methodologies allows to extract knowledge and insights from the huge amount of raw data and information now available. The area presents several opportunities for solving challenges in physics, chemistry, and materials science. Specifically, machine learning methods are a powerful tool for discovering and designing new materials with desired and optimized properties and functionalities. In this article, we present the context of the emergence of machine learning, its foundations, and applications for the discovery and design of materials.

Low-temperature alloys are important for a wide spectrum of modern technologies ranging from liquid hydrogen, superconductivity to the quantum technology. These applications push the limit of material performance into extreme coldness, often demanding a combination of strength and toughness to address various challenges.Steel is one of the most widely used materials in cryogenic applications. With the deployment in aerospace liquid hydrogen storage and transportation, aluminum and titanium alloys are also gaining increasing attention. Emerging medium-entropy alloys (MEAs) and high-entropy alloys (HEAs) demonstrate excellent low-temperature mechanical performance with a much-expanded space of material design. A database of low-temperature metallic alloys is reported here by collecting the literature data published from 1991 to 2023, which is hosted in an open repository. The workflow of data collection includes automated extraction based on machine learning and natural language processing, supplemented by manual inspection and correction, to enhance data extraction efficiency and database quality. The product datasets cover key performance parameters including yield strength, tensile strength, elongation at fracture, Charpy impact energy, as well as detailed information on materials such as their types, chemical compositions, processing and testing conditions. Data statistics are analyzed to elucidate the research and development patterns and clarify the challenges in both scientific exploration and engineering deployment.

The Materials Project is a collection of chemical compounds labelled with different attributes. The labelling is performed by different simulations, most of them at DFT level of theory.

The dataset links:

MP 2018.6.1 (69,239 materials) MP 2019.4.1 (133,420 materials)

Advanced Materials Acceptance Rate - ResearchHelpDesk - Advanced Materials has been bringing you the latest progress in materials science every week for over 30 years. Read carefully selected, top-quality Reviews, Progress Reports, Communications, and Research News at the cutting edge of the chemistry and physics of functional materials. Advanced Materials has an Impact Factor of 25.809 (2018 Journal Impact Factor, Journal Citation Reports (Clarivate Analytics, 2019)). One key to the success of Advanced Materials is its pronounced interdisciplinarity. Keywords materials science, nanotechnology, liquid crystals, semiconductors, superconductors, optics, lasers, sensors, porous materials, light emitting materials, ceramics, biological materials, magnetic materials, thin films, colloids, energy materials, photovoltaics, solar cells, biomaterials, photonics, ferroelectrics, multiferroics, metamaterials, drug delivery, cancer therapy, tissue engineering, imaging, self-assembly, hierarchical materials, batteries, supercapacitors, thermoelectrics, polymers, nanomaterials, nanocomposites, nanotubes, nanowires, nanoparticles, carbon, diamond, fullerenes Abstracting and Indexing Information Cambridge Structural Database (Cambridge Crystallographic Data Centre) CAS: Chemical Abstracts Service (ACS) Chemical Abstracts Service/SciFinder (ACS) Chimica Database (Elsevier) COMPENDEX (Elsevier) Current Contents: Engineering, Computing & Technology (Clarivate Analytics) Current Contents: Physical, Chemical & Earth Sciences (Clarivate Analytics) ENERGY (FIZ Karlsruhe) INIS: International Nuclear Information System Database (IAEA) Journal Citation Reports/Science Edition (Clarivate Analytics) MEDLINE/PubMed (NLM) Polymer Library (iSmithers RAPRA) Reaction Citation Index (Clarivate Analytics) Science Citation Index (Clarivate Analytics) Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier) TEMA: Technik und Management (WTI-Frankfurt eG) The RECAL Legacy (National Centre for Prosthetics & Orthodontics) VINITI (All-Russian Institute of Science & Technological Information) Web of Science (Clarivate Analytics)

This is the original dataset for ID 1 Nb in Thermophysical Property Database (https://thermophys.nims.go.jp/thermophysicalproperty/experiments/1). The dataset was obtained at Japan Aerospace Exploration Agency (JAXA), and is a part of Thermophysical Property Original Datasets (https://doi.org/10.48505/nims.3877) as a collection of MDR.

The utilization of advanced structural materials, such as preplaced aggregate concrete (PAC), fiber-reinforced concrete (FRC), and FRC beams has revolutionized the field of civil engineering. Therefore, the current research titled "RAGN-R: A multi-subject ensemble machine-learning method for estimating mechanical properties of advanced structural materials" in Computers and Structures, introduces a novel RAGN-R approach for proposing a comprehensive predictive model. The dataset used for this research is published to be used by researchers, for more, please check the paper.