This repository has properties for different groups of material. The main idea is to provide accesible properties for comparison.

We utilized a dataset of Machine Design materials, which includes information on their mechanical properties. The dataset was obtained from the Autodesk Material Library and comprises 15 columns, also referred to as features/attributes. This dataset is a real-world dataset, and it does not contain any random values. However, due to missing values, we only utilized seven of these columns for our ML model. You can access the related GitHub Repository here: https://github.com/purushottamnawale/material-selection-using-machine-learning

To develop a ML model, we employed several Python libraries, including NumPy, pandas, scikit-learn, and graphviz, in addition to other technologies such as Weka, MS Excel, VS Code, Kaggle, Jupyter Notebook, and GitHub. We employed Weka software to swiftly visualize the data and comprehend the relationships between the features, without requiring any programming expertise.

My Problem statement is Material Selection for EV Chassis. So, if you have any specific ideas, be sure to implement them and add the codes on Kaggle.

A Detailed Research Paper is available on https://iopscience.iop.org/article/10.1088/1742-6596/2601/1/012014

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.

Dsm Engineering Materials Wsi Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Thai Viet Engineering Materials Company Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

(1) The MAIN Thermoelectric Materials Database Auto-Generated from the Scientific Literature using ChemDataExtractor, which is presented in three available formats: CSV, JSON and MongoDB.

(2) The INFERENCE Thermoelectric Materials Database Auto-Generated from the Scientific Literature using ChemDataExtractor, which is presented in three available formats: CSV, JSON and MongoDB.

(3) Associated code that provides thermoelectric-specific data-extraction capabilities with ChemDataExtractor.

Find details of Envalior Engineering Materials Tempo Buyer/importer data in US (United States) with product description, price, shipment date, quantity, imported products list, major us ports name, overseas suppliers/exporters name etc. at sear.co.in.

Envalior Engineering Materials Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Get Exam Question Paper Solutions of Mechanical Engineering Materials and many more.

A complete copy of the Materials Project database as of 10/18/2018. Mp_all files contain structure data for each material while mp_nostruct does not.Available as Monty Encoder encoded JSON and as CSV. Recommended access method for these particular files is with the matminer Python package using the datasets module. Access to the current Materials Project is recommended through their API (good), pymatgen (better), or matminer (best).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 discussed in:A. Jain*, S.P. Ong*, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson (*=equal contributions) The Materials Project: A materials genome approach to accelerating materials innovation APL Materials, 2013, 1(1), 011002.Dataset sourced from:https://materialsproject.org/Citations for specific material properties available here:https://materialsproject.org/citing

Find details of Dsm Engineering Materials Inc Buyer/importer data in US (United States) with product description, price, shipment date, quantity, imported products list, major us ports name, overseas suppliers/exporters name etc. at sear.co.in.

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

In this open database, Impulse Excitation Technique data are collected for qualitative analysis purposes. By expanding the database, automated systems for materials recognition can be developed or improved.

Instructions for contribution: - All data are reported in three versions: original audio file, transcribed audio file (text data) and fourier-transformed data. - Each sample or set of samples has to be described in a proper Data Article in order to provide each useful information (composition, dimensions, weight...) that can be used for automated recognition.

Mokarrar Engİneerİng Materİals Co Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Analyzed data for manuscript entitled "A multiscale investigation on the performance improvement of fiber-reinforced cementitious composites after exposure to high temperatures" in Cement and Concrete Composites, and co-submitted article entitled “Experimental dataset on the residual performance of fiber-reinforced cementitious composite subjected to high temperature” in Data in Brief.

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 document contains the comprehensive ‘one-stop’ material properties database developed by the GEOLAB consortium for the typical soils and constitutive models used in the GEOLAB facilities. The said database was developed to support the use and re-use of the quality experimental data from the GEOLAB Transnational Access projects.

(A) Three materials databases containing the properties: (i) yield strengths; (ii) grain size; (iii) combined yield strengths and grain size, (iv) engineering-ready-yield strengths; all autogenerated using ChemDataExtractor. (B) The associated ChemDataExtractor code that is specific to data auto-extraction for these databases.

Full and dummy snapshots (2022-06-04) of data for mp-time-split encoded via matminer convenience functions grabbed via the new Materials Project API. The dataset is restricted to experimentally verified compounds with no more than 52 sites. No other filtering criteria were applied. The snapshots were developed for sparks-baird/mp-time-split as a benchmark dataset for materials generative modeling. Compressed version of the files (.gz) are also available. dtypes python from pprint import pprint from matminer.utils.io import load_dataframe_from_json filepath = "insert/path/to/file/here.json" expt_df = load_dataframe_from_json(filepath) pprint(expt_df.iloc[0].apply(type).to_dict()) {'discovery': , 'energy_above_hull': , 'formation_energy_per_atom': , 'material_id': , 'references': , 'structure': , 'theoretical': , 'year': } index/mpids (just the number for the index). Note that material_id-s that begin with "mvc-" have the "mvc" dropped and the hyphen (minus sign) is left to distinguish between "mp-" and "mvc-" types while still allowing for sorting. E.g. mvc-001 -> -1.

{146: MPID(mp-146), 925: MPID(mp-925), 1282: MPID(mp-1282), 1335: MPID(mp-1335), 12778: MPID(mp-12778), 2540: MPID(mp-2540), 316: MPID(mp-316), 1395: MPID(mp-1395), 2678: MPID(mp-2678), 1281: MPID(mp-1281), 1251: MPID(mp-1251)}

Data created at the European Commission JRC during the H2020 project on multiscale modeling for fusion and fission materials (M4F), funded from the Euratom research and training programme 2014-2018 under grant agreement No. 755039.