Machine learning transforms the landscape of 2D materials design, particularly in accelerating discovery, optimization, and screening processes. This review has delved into the historical and ongoing integration of machine learning in 2D materials for electrochemical energy applications, using the Knowledge Discovery in Databases (KDD) approach to guide the research through data mining from the Scopus database using analysis of citations, keywords, and trends. The topics will first focus on a “macro” scope, where hundreds of literature reports are computer analyzed for key insights, such as year analysis, publication origin, and word co-occurrence using heat maps and network graphs. Afterward, the focus will be narrowed down into a more specific “micro” scope obtained from the “macro” overview, which is intended to dive deep into machine learning usage. From the gathered insights, this work highlights how machine learning, density functional theory (DFT), and traditional experimentation are jointly advancing the field of materials science. Overall, the resulting review offers a comprehensive analysis, touching on essential applications such as batteries, fuel cells, supercapacitors, and synthesis processes while showcasing machine learning techniques that enhance the identification of critical material properties.

As semiconductor devices are miniaturized, the importance of atomic layer deposition (ALD) technology is growing. When designing ALD precursors, it is important to consider the melting point, because the precursors should have melting points lower than the process temperature. However, obtaining melting point data is challenging due to experimental sensitivity and high computational costs. As a result, a comprehensive and well-organized database for the melting point of the OMCs has not been fully reported yet. Therefore, in this study, we constructed a database of melting points for 1,845 OMCs, including 58 metal and 6 metalloid elements. The database contains CAS numbers, molecular formulas, and structural information and was constructed through automatic extraction and systematic curation. The melting point information was extracted using two methods: 1) 1,434 materials from 11 chemical vendor databases and 2) 411 materials identified through natural language processing (NLP) techniques with an accuracy of 86.3%, based on 2,096 scientific papers published over the past 29 years. In our database, the OMCs contain up to around 250 atoms and have melting points that range from −170 to 1610 °C. The main source is the Chemsrc database, accounting for 607 materials (32.9%), and Fe is the most common central metal or metalloid element (15.0%), followed by Si (11.6%) and B (6.7%). To validate the utilization of the constructed database, a multimodal neural network model was developed integrating graph-based and feature-based information as descriptors to predict the melting points of the OMCs but moderate performance. We believe the current approach reduces the time and cost associated with hand-operated data collection and processing, contributing to effective screening of potentially promising ALD precursors and providing crucial information for the advancement of the semiconductor industry.

A key technical challenge inhibiting fossil-free energy generation, which is especially pronounced in solar plants, is the inherent intermittency. Since thermochemical energy storage represents a solution that has yet to see large-scale utilization, the goal of the study at hand has been to find suitable materials using a hybrid high-throughput approach, encompassing both data mining and first principles calculations. Through these efforts, thermal properties have been estimated for more than 50 000 mono-, bi-, and trimetallic oxides, as well as alloys, retrieved from the Materials Project database.Thereafter, close to 18 000 convex hulls were created, each representing a unique metal ratio, in order to determine the most stable phases at different oxygen chemical potentials. In turn, this allowed almost 300 000 potential transitions to be identified. After filtering out potentially toxic and rare compositions, a little over 51 000 remained, which were further reduced, to about 3000, by requiring that the phase transformation must occur at a temperature between 400 °C and 1300 °C.A thorough analysis of the results led to the discovery of several promising energy storage materials. In addition, Chromium; manganese; calcium; and magnesium were found to be associated with high reaction enthalpies. When the sensible heat was taken into account, however, light elements such as lithium; boron; iron; and sodium dominated among the top-ranking candidates. This study therefore demonstrates how high-throughput data mining can be efficiently enhanced through first-principles calculations, enabled by machine-learning interatomic potentials, to facilitate material discovery.

This dataset package is focused on U.S construction materials and three construction companies: Cemex, Martin Marietta & Vulcan.

In this package, SpaceKnow tracks manufacturing and processing facilities for construction material products all over the US. By tracking these facilities, we are able to give you near-real-time data on spending on these materials, which helps to predict residential and commercial real estate construction and spending in the US.

The dataset includes 40 indices focused on asphalt, cement, concrete, and building materials in general. You can look forward to receiving country-level and regional data (activity in the North, East, West, and South of the country) and the aforementioned company data.

SpaceKnow uses satellite (SAR) data to capture activity and building material manufacturing and processing facilities in the US.

Data is updated daily, has an average lag of 4-6 days, and history back to 2017.

The insights provide you with level and change data for refineries, storage, manufacturing, logistics, and employee parking-based locations.

SpaceKnow offers 3 delivery options: CSV, API, and Insights Dashboard

Available Indices Companies: Cemex (CX): Construction Materials (covers all manufacturing facilities of the company in the US), Concrete, Cement (refinery and storage) indices, and aggregates Martin Marietta (MLM): Construction Materials (covers all manufacturing facilities of the company in the US), Concrete, Cement (refinery and storage) indices, and aggregates Vulcan (VMC): Construction Materials (covers all manufacturing facilities of the company in the US), Concrete, Cement (refinery and storage) indices, and aggregates

USA Indices:

Aggregates USA Asphalt USA Cement USA Cement Refinery USA Cement Storage USA Concrete USA Construction Materials USA Construction Mining USA Construction Parking Lots USA Construction Materials Transfer Hub US Cement - Midwest, Northeast, South, West Cement Refinery - Midwest, Northeast, South, West Cement Storage - Midwest, Northeast, South, West

Why get SpaceKnow's U.S Construction Materials Package?

Monitor Construction Market Trends: Near-real-time insights into the construction industry allow clients to understand and anticipate market trends better.

Track Companies Performance: Monitor the operational activities, such as the volume of sales

Assess Risk: Use satellite activity data to assess the risks associated with investing in the construction industry.

Index Methodology Summary Continuous Feed Index (CFI) is a daily aggregation of the area of metallic objects in square meters. There are two types of CFI indices; CFI-R index gives the data in levels. It shows how many square meters are covered by metallic objects (for example employee cars at a facility). CFI-S index gives the change in data. It shows how many square meters have changed within the locations between two consecutive satellite images.

How to interpret the data SpaceKnow indices can be compared with the related economic indicators or KPIs. If the economic indicator is in monthly terms, perform a 30-day rolling sum and pick the last day of the month to compare with the economic indicator. Each data point will reflect approximately the sum of the month. If the economic indicator is in quarterly terms, perform a 90-day rolling sum and pick the last day of the 90-day to compare with the economic indicator. Each data point will reflect approximately the sum of the quarter.

Where the data comes from SpaceKnow brings you the data edge by applying machine learning and AI algorithms to synthetic aperture radar and optical satellite imagery. The company’s infrastructure searches and downloads new imagery every day, and the computations of the data take place within less than 24 hours.

In contrast to traditional economic data, which are released in monthly and quarterly terms, SpaceKnow data is high-frequency and available daily. It is possible to observe the latest movements in the construction industry with just a 4-6 day lag, on average.

The construction materials data help you to estimate the performance of the construction sector and the business activity of the selected companies.

The foundation of delivering high-quality data is based on the success of defining each location to observe and extract the data. All locations are thoroughly researched and validated by an in-house team of annotators and data analysts.

See below how our Construction Materials index performs against the US Non-residential construction spending benchmark

Each individual location is precisely defined to avoid noise in the data, which may arise from traffic or changing vegetation due to seasonal reasons.

SpaceKnow uses radar imagery and its own unique algorithms, so the indices do not lose their significance in bad weather conditions such as rain or heavy clouds.

→ Reach out to get free trial

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This is a condensed version of HTEM database downloaded via HTEM API from National Renewable Energy Laboratory. Due to network constraints, all entries without XRD entries are discarded.

Dataset Overview

The index file contains experiment conditions of 1400+ experiments performed by the high-throughput experiment platform in NREL. Each experiments contains 44 samples, whose associated data are stored in the samples folder. The 44 samples in each experiment all have different thin film thickness and composition. Depending on the experiment setup, the sample data files may contain data from X-ray Fluorescence (thin film composition), X-ray Diffraction (crystalline structure), electronic measurement (thin film conductivity), and optical spectra (light absorption).

This dataset provides a complete record of experimental condition, structural characterization, and properties measurement, making it a valuable resource for data-mining for a better understanding of complex process-structure-property relationships in thin film materials.

Please cite: A. Zakutayev, N. Wunder, M. Schwarting, J. D. Perkins, R. White, K. Munch, W. Tumas and C. Phillips, Sci Data 5, 180053 (2018).

As semiconductor devices are miniaturized, the importance of atomic layer deposition (ALD) technology is growing. When designing ALD precursors, it is important to consider the melting point, because the precursors should have melting points lower than the process temperature. However, obtaining melting point data is challenging due to experimental sensitivity and high computational costs. As a result, a comprehensive and well-organized database for the melting point of the OMCs has not been fully reported yet. Therefore, in this study, we constructed a database of melting points for 1,845 OMCs, including 58 metal and 6 metalloid elements. The database contains CAS numbers, molecular formulas, and structural information and was constructed through automatic extraction and systematic curation. The melting point information was extracted using two methods: 1) 1,434 materials from 11 chemical vendor databases and 2) 411 materials identified through natural language processing (NLP) techniques with an accuracy of 86.3%, based on 2,096 scientific papers published over the past 29 years. In our database, the OMCs contain up to around 250 atoms and have melting points that range from −170 to 1610 °C. The main source is the Chemsrc database, accounting for 607 materials (32.9%), and Fe is the most common central metal or metalloid element (15.0%), followed by Si (11.6%) and B (6.7%). To validate the utilization of the constructed database, a multimodal neural network model was developed integrating graph-based and feature-based information as descriptors to predict the melting points of the OMCs but moderate performance. We believe the current approach reduces the time and cost associated with hand-operated data collection and processing, contributing to effective screening of potentially promising ALD precursors and providing crucial information for the advancement of the semiconductor industry.

As semiconductor devices are miniaturized, the importance of atomic layer deposition (ALD) technology is growing. When designing ALD precursors, it is important to consider the melting point, because the precursors should have melting points lower than the process temperature. However, obtaining melting point data is challenging due to experimental sensitivity and high computational costs. As a result, a comprehensive and well-organized database for the melting point of the OMCs has not been fully reported yet. Therefore, in this study, we constructed a database of melting points for 1,845 OMCs, including 58 metal and 6 metalloid elements. The database contains CAS numbers, molecular formulas, and structural information and was constructed through automatic extraction and systematic curation. The melting point information was extracted using two methods: 1) 1,434 materials from 11 chemical vendor databases and 2) 411 materials identified through natural language processing (NLP) techniques with an accuracy of 86.3%, based on 2,096 scientific papers published over the past 29 years. In our database, the OMCs contain up to around 250 atoms and have melting points that range from −170 to 1610 °C. The main source is the Chemsrc database, accounting for 607 materials (32.9%), and Fe is the most common central metal or metalloid element (15.0%), followed by Si (11.6%) and B (6.7%). To validate the utilization of the constructed database, a multimodal neural network model was developed integrating graph-based and feature-based information as descriptors to predict the melting points of the OMCs but moderate performance. We believe the current approach reduces the time and cost associated with hand-operated data collection and processing, contributing to effective screening of potentially promising ALD precursors and providing crucial information for the advancement of the semiconductor industry.

Complete dataset: Mining & Hydraulic Supplies's 0 incidents, security score trends, compliance status, and comparative benchmarks against 0 industry peers.

Data were collected by Geological Survey of the Republic of Srpska, Bosnia and Herzegovina, in frame of RESEERVE project.

Dataset of FDM 3D Printing Process Parameters and Mechanical Properties for Multi-Material Characterization Description This dataset contains comprehensive experimental data on the fused deposition modeling (FDM) 3D printing process, focusing on the relationship between manufacturing parameters and the resulting physical and mechanical properties of printed parts. It comprises 500 unique experimental runs/samples and covers a diverse range of polymer materials and printing configurations. The dataset is highly valuable for researchers in additive manufacturing, materials science, and machine learning practitioners looking to predict printed part quality, optimize manufacturing processes, or build surrogate models for mechanical behavior. Dataset Structure & Parameters: 1. Input / Process Parameters: • Printer Type: Open vs. Closed chamber printers. • Material Types (10 different filaments): PLA+, PLA, PLA phosphor, PLA-CF (carbon fiber), PLA-transparent , PETG, rPET (recycled PET), PP, TPU, and ABS. • Layer Thickness (mm): Variable layer resolutions. • Infill Density (%): Variable infill percentages. • Infill Pattern (15 different patterns): Grid, Triangles, Zigzag, Lines, Cubic, Quarter Cubic, Cross, Gyroid, Cubic Subdivision, Tri-hexagon, Lightning, Cross3D, Concentric, and Octet. • Printing Speed (mm/s): Controlled extrusion speeds. • Specimen Dimensions: Width (mm) and Thickness (mm) of each printed sample. 2. Output / Characterization Results: • Surface Roughness (Roughness AVG - µm): Average surface roughness measurements. • Hardness (Hardness AVG): Shore hardness characterization. • Tensile Properties: Peak Load (N), Peak Stress (kPa), Strain at Break (mm/mm), and Elastic Modulus (MPa). Potential Applications: • Optimizing 3D printing parameters for specific mechanical and surface quality performance targets. • Comparative analysis of different filament types (e.g., standard PLA vs. carbon fiber reinforced PLA-CF or recycled rPET). • Training and developing machine learning, deep learning, and regression models (such as ensemble learning models) to predict mechanical strength, surface roughness, or elasticity. Associated Publication: If you use this dataset in your research, please cite our corresponding paper: Aktepe, E., & Ergün, U. (2026). HMQ-ES-Stack-GBR: A Hybrid Ensemble Learning Model for Mechanical and Physical Quality Prediction in FDM 3D Printing. Micromachines. Acknowledgments: This research was supported by Afyon Kocatepe University Scientific Research Projects, project number 25.FEN.BİL.11. Furthermore, this article is derived from the doctoral thesis entitled “Development of a Hybrid Artificial Intelligence-Based Quality Prediction and Defect Detection System in 3D Printing Processes”. Thanks are extended to the Izmir Katip Çelebi University Central Research Laboratory for providing support in tensile strength test analyses.

CN: Construction Material Production Special Machinery: YoY: Cost of Sales: ytd data was reported at 1.422 % in Oct 2015. This records a decrease from the previous number of 2.975 % for Sep 2015. CN: Construction Material Production Special Machinery: YoY: Cost of Sales: ytd data is updated monthly, averaging 25.198 % from Jan 2006 (Median) to Oct 2015, with 89 observations. The data reached an all-time high of 43.470 % in Feb 2010 and a record low of 0.437 % in Mar 2015. CN: Construction Material Production Special Machinery: YoY: Cost of Sales: ytd data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BHX: Mining, Metallurgy, Construction Special Equipment: Construction Material Production Special Machinery.

Federal contract award data for 56-2023 Construction Materials BPA, awarded by INTERIOR, DEPARTMENT OF THE in the Crushed and Broken Granite Mining and Quarrying sector. Sourced from SAM.gov federal procurement database.

Manufacturing Industry: Plastic Raw Materials and Synthetic Resin: Other Amino Resin (Aminoplast) data was reported at 20.799 IDR bn in 2013. This stayed constant from the previous number of 20.799 IDR bn for 2012. Manufacturing Industry: Plastic Raw Materials and Synthetic Resin: Other Amino Resin (Aminoplast) data is updated yearly, averaging 101.088 IDR bn from Dec 2008 (Median) to 2013, with 6 observations. The data reached an all-time high of 182.847 IDR bn in 2010 and a record low of 20.799 IDR bn in 2013. Manufacturing Industry: Plastic Raw Materials and Synthetic Resin: Other Amino Resin (Aminoplast) data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Mining and Manufacturing Sector – Table ID.BAD018: Manufacturing Industry: by Product: Plastic Raw Materials and Synthetic Resin.

Since the end of the 1980ies the geological, areal and production data of operating mining sites have been collected systematically by LGRB. The periodic update of this information is carried out every four or five years. Main reasons are 1) the preparation of the periodic follow-up of the 12 regional development plans, 2) the work on the near-surface mineral raw material maps published by LGRB, and 3) the periodical editing of the state report for near-surface mineral raw materials published by LGRB at the start of each new election period. The geological data include a detailed documentation of the thickness, petrography and quality of mined rock(s) and the overburden as well as geochemical data gained from rock samples. The areal data refer both to the permitted mining area (zones of recultivation, work and expansion) and to possible areas for the mine expansion (the latter are confidential). Due to the quick spatiotemporal variability of these data, here all mining sites are shown as point data. The confidential annual production data are the basis for the periodic raw material report. In addition, another data are collected, e.g. for the mining permission, the delivery area and the subsequent land use. All these data are stored in the mining site database of the LGRB (Rohstoffgewinnungs-stellendatenbank = RGDB). This one comprises also the data for abandoned mining sites and mines. In total, actual (2021) about 14.000 data records are stored. The name of each mining site (e.g. RG 6826-3) consists of three parts. RG is the abbreviation for "Rohstoffgewinnungsstelle". the following four-digit number means the number of the relevant topographic map 1 : 25.000. The last number means the serial number of the mining site; serial numbers 1-99 mark operating mining sites gathered since the end of the 1980ies ( (today partially already closed) , such > 100 mark abandoned mining sites collected before 1980 and such > 300 mark data of mining sites and mines collected in the course of actual raw material mapping.
The mintell4eu data set comprises all mining sites with serial numbers 1-99. In addition, the most important abandoned mines of former or probably still ongoing economic importance.

The failures of engineered dams that retain freshwater or mining waste (tailings) have led to socioeconomic and environmental consequences. However, the global magnitude-frequency statistics of these occurrences remain poorly quantified, out-of-date and/or limited in scope. Addressing this gap would give insight into how the hazard-risk of water-retention (reservoir) dams and mine tailings impoundments has evolved over time, and would provide quantitative benchmarks for estimating likelihoods of facility failures and their societal impacts to support risk assessments. In this journal publication, we analyze new datasets and estimates of the construction and failures of large reservoir facilities (LRFs) and tailings storage facilities (TSFs) worldwide in the period 1965-2020. We address long-standing data gaps on LRF failures in China, and subsequently worldwide, and on constructed TSFs worldwide by adopting multiple estimation/extrapolation approaches to illustrate the range of uncertainty in our results. Our results are applicable broadly on a global scale and are conditioned by uncertainties in the data and the methods used to address data gaps. This Borealis Dataset, created under the CanBreach Data Archive umbrella, contains supplementary materials to the Related Publication in the journal Earth-Science Reviews. The files include the following: (i) A new database of TSF failures in the period 1965-2020 including new information such as location coordinates, altitude, and surface area; (ii) A detailed calculation sheet that outlines the conversion process from global mineral commodity production data to the estimated volume of tailings produced worldwide in the period 1965-2020 to the estimated number of TSFs constructed worldwide over the same period based on an upper- and lower-bound extrapolation approach; (ii) A new, incomplete database of historical LRF failures, including a summary calculation sheet that outlines how the upper- and lower-bound estimates of the number of LRF failures worldwide in the period 1965-2020 were developed; (iii) A supplementary article that describes the supplementary databases, includes the supplementary figures relevant to the journal publication, and includes the link to the open-access Google Drive folder that contains GIS-processed satellite and aerial images for many historical TSF and LRF failure cases; and (iv) Google Earth KMZ files that identifies the locations of historically failed TSFs and maps the impoundment surface areas.

Background knowledge

Metal-Organic Frameworks (MOF) are a class of crystalline materials connected by coordination bonds between metal ions (or metal clusters) and organic ligands. MOF materials have porous structure, highly tunable and huge specific surface area, which makes them have wide application potential in fields such as adsorption, gas storage, separation, catalysis and so on. Prediction synthesis refers to the prediction and design of synthetic routes and conditions of new MOF materials through computer simulation and machine learning methods.

Structural information of MOF materials (input): MOF is determined by metal, organic ligand, connection mode (topology) and can be given as input data representing structural information by CIF (Crystallographic Information File) file through algorithm (we will provide). This dataset will provide two kinds of data, Fingerprint and RAC, respectively, according to which the players need to make corresponding task predictions.

Synthesis conditions of MOF materials (output): According to the data mining of the corresponding articles on the synthesis of MOF materials, four representative synthetic conditions of materials are obtained: temperature (T), time (t), organic solvent used, and additives.

Data format specification

Fingerprint-based features

The data files are presented in the finger_train.csv and finger_test.csv files and contain 560 training data and 140 test data, respectively.

The fields of sample characteristics are described as follows:

linker1smi is a structural expression and a string characteristic. Optionally, the molecular fingerprint of linker can be calculated using the chem.rdkfingerprint function in the chem module of the rdkit library, converting it into a digital vector feature.

Based on Fingerprint characteristics, the following two regression tasks are set:

• temperature Prediction task: Predicts the temperature field
• time Prediction task: Predicts the value of the time field.

RAC features

The data files are given in the RAC_train.csv and RAC_test.csv folders and contain 537 training data and 134 test data, respectively.

The RAC feature data combines the pore geometry of the MOF with the chemical composition (such as metal nodes, ligands, and functional groups) to obtain the feature vector.

The fields of sample characteristics are described as follows:

Among them, the five related properties of the solvent are: octanol/water partition coefficient, hydrogen bond donor number, hydrogen bond acceptor number, local charge maximum absolute value, boiling point.

Based on RAC features, the following three regression tasks and one classification task are set:

(1) Regression tasks:

• temperature Prediction task: Predicts the temperature field.
• time Prediction task: Predicts the value of the time field
• solvent prediction task: Predict the values of param1, param2, param3, param4, and param5.

(2) Classification tasks:

• additive prediction task: predicts the additive_category field values.

Seawater represents a potential resource for raw materials extraction. Although NaCl is the most representative mineral extracted other valuable compounds such as Mg, Li, Sr, Rb and B and elements at trace level (Cs, Co, In, Sc, Ga and Ge) are also contained in this “liquid mine”. Most of them are considered as Critical Raw Materials by the European Union. Solar saltworks, providing concentration factors of up-to 20 to 40, offer a perfect platform for the development of minerals and metal recovery schemes taking benefit of the concentration and purification achieved along the evaporation saltwork ponds. However, the geochemistry of these elements in this environment has not been yet thoroughly evaluated. Their knowledge could enable the deployment of technologies capable to achieve the recovery of valuable minerals. The high ionic strengths expected (0.5–7 mol/kg) and the chemical complexity of the solutions imply that only numerical geochemical codes, as PHREEQC, and the use of Pitzer model to estimate the activity coefficients of the different species in solution can be adopted to provide valuable description of the systems. In the present work, for the first time, PHREEQC Pitzer code database was extended to include the target minor and trace elements using Trapani saltworks (Sicily, Italy) as a case study system. The model was able to predict: i) the purity in halite and the major impurities contained, mainly Ca,Mgand sulphate species; ii) the fate of minor components as B, Sr, Cs, Co, Ge and Ga along the evaporation ponds. The results obtained pose a fundamental step in critical raw materials mining from seawater brine, for process intensification and combination with desalination.

A key technical challenge inhibiting fossil-free energy generation, which is especially pronounced in solar plants, is the inherent intermittency. Since thermochemical energy storage represents a solution that has yet to see large-scale utilization, the goal of the study at hand has been to find suitable materials using a hybrid high-throughput approach, encompassing both data mining and first principles calculations. Through these efforts, thermal properties have been estimated for more than 50 000 mono-, bi-, and trimetallic oxides, as well as alloys, retrieved from the Materials Project database.Thereafter, close to 18 000 convex hulls were created, each representing a unique metal ratio, in order to determine the most stable phases at different oxygen chemical potentials. In turn, this allowed almost 300 000 potential transitions to be identified. After filtering out potentially toxic and rare compositions, a little over 51 000 remained, which were further reduced, to about 3000, by requiring that the phase transformation must occur at a temperature between 400 °C and 1300 °C.A thorough analysis of the results led to the discovery of several promising energy storage materials. In addition, Chromium; manganese; calcium; and magnesium were found to be associated with high reaction enthalpies. When the sensible heat was taken into account, however, light elements such as lithium; boron; iron; and sodium dominated among the top-ranking candidates. This study therefore demonstrates how high-throughput data mining can be efficiently enhanced through first-principles calculations, enabled by machine-learning interatomic potentials, to facilitate material discovery.

2020 Data for Material Flows of critical minerals used in Electric Vehicle Battery Cathode Materials, across steps of the upstream and midstream battery supply chains (extraction/mining, refining, and cathode production) Generated from Intracen's TradeMap database.

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