Material Database Research Articles

Utilization of IoT and database system of central warehouse project in the pharmaceutical industry

This study aims to develop an MRPII device that utilizes internet technology for inventory management control processes, using case studies from pharmacutical companies in Indonesia. The research was conducted using field observation methods and system development, then comparing the manual system and MRPII. An internet-based inventory management system was created by integrating the warehouse’s material database into each material code via a QR. code. The study found that implementing the MRP II system design increases work effectiveness and reduces the risk of errors that frequently occur in conventional systems. Moreover, using the MRPII can facilitate the audit process and establish transparency in the inventory management process. Data processed using IoT is generated faster and more accurately as company operations improve quality, reduce unnecessary activities, and make it easier to organize materials and run administrative and technical processes in inventory management; it is measured based on reach, workforce requirements, supply, and handling time. In the industrial world, the MRPII database system has the potential to evolve into several more complex and comprehensive systems. It has broad integration and can assist humans working across all lines, particularly in the data processing system.

Functional Carbon Springs Enabled Dynamic Tunable Microwave Absorption and Thermal Insulation.

Electromagnetic (EM) wave pollution and thermal damage pose serious hazards to delicate instruments. Functional aerogels offer a promising solution by mitigating EM interference and isolating heat. However, most of these materials struggle to balance thermal protection with microwave absorption (MA) efficiency due to a previously unidentified conflict between the optimizing strategies of the two properties. Herein, this study reports a solution involving the design of a carbon-based aerogel called functional carbon spring (FCS). Its unique long-range lamellar multi-arch microstructure enables tunable MA performance and excellent thermal insulation capability. Adjusting compression strain from 0% to 50%, the adjustable effective absorption bandwidth (EAB) spans up to 13.4GHz, covering 84% of the measured frequency spectrum. Notably, at 75% strain, the EAB drops to 0GHz, demonstrating a novel "on-off" switchability for MA performance. Its ultralow vertical thermal conductivity (12.7mW m-1 K-1) and unique anisotropic heat transfer mechanism endow FCS with superior thermal protection effectiveness. Numerical simulations demonstrate that FCS outperforms common honeycomb structures and isotropic porous aerogels in thermal management. Furthermore, an "electromagnetic-thermal" dual-protection material database is established, which intuitively demonstrates the superiority of the solution. This work contributes to the advancement of multifunctional MA materials with significant potential for practical applications.

The British Digital Press Archive as a source on the history of the collective memory of the First World War participants in the interwar period

Importance. The 110th anniversary of the First World War outbreak awakens in the public and scientific community not only discussions about the causes of the conflict, but also forces us to turn to the legacy of this process. In this regard, the work actualizes the theme of historical memory of the “Great War”. The purpose of the study is to reflect the complex of the legacy of the First World War in Great Britain in the 1930s by referring to the British Newspaper Archive.Materials and Methods. For the analysis of historical memory in the Great Britain, memoirs, monumental art, works of cinema, music, and many others are often involved. Methods of working with the British newspaper Archive are considered and, based on a specific historical method, we will reflect the perception of the First World War in British society and the situation of veterans in the 1930s. The method of quantitative content analysis is used. The necessary database of source material is selected by keywords, then the selected sources are grouped according to a certain topic. Memoirs, monumental art, works of cinema, music and much more are often used to analyze historical memory in Great Britain. In this work, we will look at the techniques of working with the British Newspaper Archive and, based on a specific historical method, reflect the perception of the First World War in British society and the situation of veterans in the 1930s. Due to the free access to a huge mass of English-language sources, this digital archive is of interest to researchers. The work uses the method of quantitative content analysis.Results and Discussion. During the study of press materials, it can be stated that the following agenda has been identified in the historical memory and legacy of the First World War in Great Britain in the 1930s: 1) the presence of narratives reflecting the pride of British society for the heroism of soldiers and sorrow for the many sacrifices they made in the name of victory in the war; 2) despite the discussions’ existence about the frontline brotherhood during the war period, after the war, veterans rallied into unions and organizations engaged in a wide variety of support (from mediation when applying for a job to appeals to the authorities about pension provision).Conclusion. Thus, despite the apparent immensity of the digital database of the British Newspaper Archive, the application of the well-known methodological principle of “Occam’s razor” makes it possible to compile a corpus of sources that meet the purpose of the study. Immersion in the press materials themselves allows the researcher to immerse himself more in the socio-cultural context of Great Britain in the 1930s. Due to the complexity of the information from the studied sources.

Next-generation cathodes for calcium-ion batteries: Leveraging NASICON structures for enhanced stability and energy density

This study focuses on developing a high-performance, stable cathode for calcium-ion batteries (CIBs) using a sodium superionic conductor (NASICON) structure to match the energy density and safety standards of current lithium- and sodium-ion batteries. Given the relatively sparse database of CIB materials compared with their lithium and sodium counterparts, expanding the range of new candidates is essential for developing high-performance batteries. To address this, we employed density functional theory (DFT) calculations, which provide a quantum-mechanical description of the electronic properties of materials, to construct a highly reliable database. To improve the accuracy and efficiency, we integrated machine learning interatomic potential with DFT to stabilize the NASICON-type structures, CaxNaV’yV’’2-yBzP3-zO12, where x = 0.8, 0.5, 0; y = 1, 0.5; z = 0.5, 0; V’ and V’’ are transition metals that support stable doped configurations at the V- and P-sites. From the initial 176 candidates, the top 10 materials that facilitate stable structures were identified based on selection criteria focusing on formation energy < 0 eV/atom, energy above hull = 0 eV/atom, gravimetric capacity ≥ 150 mAh/g, -1% ≤ volume change ≤ 1%, and 3 ≤ average voltage ≤ 4.5 V. This approach advances CIB technology and outlines effective strategies for dopant selection to optimize battery cathodes, configuring a framework for future advancements in battery technology.

Influences of the criminal justice system on use of medications for opioid use disorder: a qualitative study

BackgroundLegal-involved veterans with opioid use disorder (OUD) have lower receipt of medications for opioid use disorder (MOUD) than other veterans served at the Veterans Health Administration (VHA). This qualitative study examined the influence of the criminal justice system on access to MOUD for legal-involved veterans in the U.S.MethodsVHA facilities (n = 14) that varied in their provision of MOUD to legal-involved veterans were selected for qualitative interviews. Interviewees included legal-involved veterans (n = 18), VHA Veterans Justice Programs Specialists (n = 15), substance use disorder treatment providers (n = 5), and criminal justice staff (n = 12). Team members applied codes to meaningful units of analysis (quotations) in the transcribed interviews. Using a matrix approach, team members created a spreadsheet matrix with codes, facility rate of MOUD, and relevant quotations summarized for each participant. Themes and connections between individual participants and cross-interview concepts were explored. Participants were not asked to provide feedback on the findings.ResultsThemes identified were as follows: (1) Veterans Treatment Court policies both enhanced and limited MOUD treatment access and utilization among participants; (2) cross-system collaboration strengths and challenges existed; and (3) criminal justice system treatment preferences and policies both enhanced and limited MOUD in jails and prisons.ConclusionsThe influence of the criminal justice system on MOUD has led to variable access to MOUD for legal-involved veterans. Our findings can help inform recommendations to enhance access to MOUD for veterans within the criminal justice system, including the development of a national database of MOUD education materials for Veterans Treatment Courts, strengthening community-court relationships, allowing individuals to use their own healthcare coverage within jails and prisons and extend Medicaid coverage into criminal justice settings, and applying national quality measures for MOUD to criminal justice settings and develop a national system for tracking these quality measures.

PROFiT-Net: Property-Networking Deep Learning Model for Materials.

There is a growing need to develop artificial intelligence technologies capable of accurately predicting the properties of materials. This necessitates the expansion of material databases beyond the scope of density functional theory, and also the development of deep learning (DL) models that can be effectively trained with a limited amount of high-fidelity data. We developed a DL model utilizing a crystal structure representation based on the orbital field matrix (OFM), which was modified to incorporate information on elemental properties and valence electron configurations. This model, effectively capturing the interrelation between the elemental properties in the crystal, was coined the PRoperty-networking Orbital Field maTrix-convolutional neural Network (PROFiT-Net). Remarkably, PROFiT-Net demonstrated high accuracy in predicting the dielectric constant, experimental band gaps, and formation enthalpies compared with other leading DL models. Moreover, our model accurately identifies physical patterns, such as avoiding the prediction of unphysical negative band gaps and exhibiting a Penn-model-like trend while maintaining the scalability. We envision that PROFiT-Net will accelerate the development of functional materials.

A data-driven approach to predict the in vitro dissolution time of sustained-release tablets using raw material databases and machine learning algorithms

Tablets are the most typical dosage forms of pharmaceutical inventions. Sustained-release (SR) tablet formulations are designed to release the drug gradually in the bloodstream and often require less frequent dosing. Current strategies to optimize sustained-release tablet dissolution time still rely on the traditional approach, which is time-consuming and expensive. In the present context, we have demonstrated alternate machine learning and deep learning models through the TPOT AutoML platform. Six machine learning (ML) models were compared to improve the methodology for dissolution time prediction, particularly the decision tree regressor (DTR), gradient boost regressor (GBR), random forest regressor (RFR), extra tree regressor (ETR), XGBoost regressor (XGBR), and deep learning (DL). The obtained results indicated that machine learning methods are convincing in speculating the dissolution time, especially the random forest regressor, but upon hypertuning of the deep neural network, the deep learning model with a 10-fold cross-validation scheme demonstrated superior predictive performance with an NRMSE of 8% and an R2 of 0.92. The major essentials affecting the dissolution time of SR tablets were explained using the SHAP method.

Model-free chemomechanical interfaces: History-dependent damage under transient mass diffusion

This paper presents a data-driven framework based on distance functional for chemo-mechanical cohesive interfaces to capture transient diffusion and resulting interfacial damage evolution. The framework eliminates reliance on complex constitutive models and phenomenological assumptions, especially avoiding the coupling tangent terms with a given material database. The interfacial chemical potential and its jumps are used to expand the phase space for describing the chemical states of interfaces. Subsequently, a novel chemo-mechanical distance norm, including traction-separation pair, interfacial chemical potential-concentration pair and corresponding chemical potential jump-flux pair, is presented. Momentum conservation law for interfacial tractions and mass conservation law for interfacial concentration are enforced via Lagrange multipliers. For tracking the history-dependent state of the interface damage, an internal variable, termed interface integrity, is introduced to condition the interfacial database and manage the subsets mapping strategies, following physically motivated evolution constraints. Numerical examples are conducted to investigate the efficiency and capability of the present framework. A monotonic loading test validates a good numerical convergence relative to the number of data points. Subsequent cyclic loading simulations, compared with the reference solutions, show that the algorithm is well suitable to predict the history-dependent interface damage evolution under complex loading paths. Finally, the chemo-mechanically coupled examples capture phenomena such as interfacial swelling and interface degradation induced by transient diffusion and stress-driven diffusion. The current work provides a promising tool for understanding chemo-mechanical behaviors of interfaces within heterogeneous composites.

Osteoinductive biomaterials: Machine learning for prediction and interpretation

Biomaterials with osteoinductivity are widely used for bone defect repair due to their unique structures and functions. Machine learning (ML) is pivotal in analyzing osteoinductivity and accelerating new material design. However, challenges include creating a comprehensive database of osteoinductive materials and dealing with low-quality, disparate data. As a standard for evaluating the osteoinductivity of biomaterials, ectopic ossification has been used. This paper compiles research findings from the past thirty years, resulting in a robust database validated by experts. To tackle issues of limited data samples, missing data, and high-dimensional sparsity, a data enhancement strategy is developed. This approach achieved an area under the curve (AUC) of 0.921, a precision of 0.839, and a recall of 0.833. Model interpretation identified key factors such as porosity, bone morphogenetic protein-2 (BMP-2), and hydroxyapatite (HA) proportion as crucial determinants of outcomes. Optimizing pore structure and material composition through partial dependence plot (PDP) analysis led to a new bone area ratio of 14.7 ± 7 % in animal experiments, surpassing the database average of 10.97 %. This highlights the significant potential of ML in the development and design of osteoinductive materials. Statement of significanceThis study leverages machine learning to analyze osteoinductive biomaterials, addressing challenges in database creation and data quality. Our data enhancement strategy significantly improved model performance. By optimizing pore structure and material composition, we increased new bone formation rates, showcasing the vast potential of machine learning in biomaterial design.

Thermophysical properties of UO2-ZrO2 melt measured by aerodynamic levitation

This study addresses the measurement of thermophysical properties in molten core materials (corium), which is crucial for modeling severe accidents in light water reactors. Due to the measurement challenges at temperatures above 3000 K, there is a significant lack of experimental data for corium. Focusing on a UO2-ZrO2 melt (atomic ratio U/Zr=0.456), density and viscosity measurements were conducted using aerodynamic levitation, a non-contact method chosen to avoid interactions between the samples and container walls. Samples were prepared using the pressing sintering method and levitated by argon gas above a conical converging-diverging nozzle. Density was measured using the cooling traces method under the axisymmetric ellipsoid hypothesis, while viscosity was measured by inducing damped oscillations which occurred when the levitated droplet was forced to oscillate around its resonant frequency through acoustic excitation, followed by the cessation of the acoustic excitation. The provided data and derived equations for the density and viscosity of the UO2-ZrO2 melt contribute to enriching the thermophysical property database for materials in nuclear reactors.

Orbital magnetization in two-dimensional materials from high-throughput computational screening

Abstract We calculate the orbital magnetization of 822 two-dimensional magnetic materials from the Computational 2D Materials Database (C2DB). For compounds containing 5$d$ elements we find orbital moments of the order of 0.3--0.5 $\mu_\mathrm{B}$, which points to the necessity of including these in any type of magnetic modeling and comparison with experiments. It is also shown that the alignment of orbital moments with respect to the spin largely follows the predictions from Hund's rule and that deviations may be explained by the $d$-band splitting originating from the crystal field - for example in the important case of CrI$_3$. Finally, we show that for certain insulators, Hubbard corrections may lead to large and fully unquenched orbital moments that are pinned to the lattice rather than the spin and that these moments can lead to enormous magnetic anisotropies. Such unquenched ground states are only found from density functional theory calculations that include both Hubbard corrections and self-consistent spin--orbit coupling and largely invalidates the use of the magnetic force theorem for calculating magnetic anisotropies.

Liquid thermophysical properties of Ag-Si alloy based on deep learning potential

The knowledge of the thermophysical properties of liquid metals and alloys is essential for expanding the materials database and designing materials with good properties. In this work, we developed an interatomic potential using a deep neural network (DNN) algorithm for liquid Ag-Si alloys. Compared with ab initio molecular dynamics (AIMD) results, the DNN potential provided a good description of the information of energy, force, and structure features of the system in the simulated temperature range. Through this potential, we can obtain the thermophysical properties of different compositions of liquid alloys by simulation way. The computed thermophysical properties are in excellent agreement with the reported experimental data. The analysis of local structure indicates that the liquid ordering and stability strengthen upon cooling at the atomic level, eventually leading to an increase in thermophysical properties.

Self-supervised probabilistic models for exploring shape memory alloys

Recent advancements in machine learning (ML) have revolutionized the field of high-performance materials design. However, developing robust ML models to decipher intricate structure-property relationships in materials remains challenging, primarily due to the limited availability of labeled datasets with well-characterized crystal structures. This is particularly pronounced in materials where functional properties are closely intertwined with their crystallographic symmetry. We introduce a self-supervised probabilistic model (SSPM) that autonomously learns unbiased atomic representations and the likelihood of compounds with given crystal structures, utilizing solely the existing crystal structure data from materials databases. SSPM significantly enhances the performance of downstream ML models by efficient atomic representations and accurately captures the probabilistic relationships between composition and crystal structure. We showcase SSPM’s capability by discovering shape memory alloys (SMAs). Amongst the top 50 predictions, 23 have been confirmed as SMAs either experimentally or theoretically, and a previously unknown SMA candidate, MgAu, has been identified.

Data-Driven Material Exploration of High Conductive Fluorides By Multi-Element Substitution

Barium fluoride (BaF2) has a fluorite-type structure and is attracting high attention as a solid electrolyte material for fluoride ion batteries that can be used under high voltage. Previous research has reported that the ionic conductivity increases by 4 to 5 orders of magnitude by partially replacing Ba in BaF2 with La. This indicates that elemental substitution is effective in improving the ionic conductivity of BaF2. However, single element substitution only explore much limited experimental space in elements, so that it should be difficult to discover innovative conductive compositions. By replacing various elements at the same time, the experimental space can be largely expanded and the possibility of finding the optimal composition increases. But, the combination numbers of multi-element substituted compositions are much over 10000, and exhaustive exploration of them is unrealistic. Therefore, we aimed to efficiently search for multi-element substitution compositions with high conductivity by creating a material-conductivity correlated exploring system under data-driven approach using large number of fluoride material database. The purpose of this research was to discover solid solution compositions in the multi-element substituted fluorides and understanding of the relationship between substitutional elements and solid solution formation.Approximately 20 types of multi-element substitution composition candidates were initially picked up using the D-optimal design (Dopt). The target compositions were synthesized to collect their XRD data. Next, we extracted the "number of peaks" from the XRD data as a feature values to express the degree of solid solution in the sample. Furthermore, we made a regression model with the element types and addition ratios as explanatory variables and the "number of peaks" as the objective variable. Using the regression model, approximately 20 candidate were predicted to form solid solution, in which we called as data-driven design of experiments (DOE) approach. Finally, the materials were actually prepared according to the proposal in DOE. The identification results were projected using the principal component analysis (PCA) to draw the material space.The obtained material space shows the number of peaks in the samples prepared using DOE decreased compared to those using Dopt. In detail, the Dopt-derived sample had 41.83 peaks, while the DOE-derived sample had 19.65 peaks on average. This result indicates that we were able to efficiently find preferable compositions that are advantageous for solid solution formation by using the data-driven method.In the presentation, we will also report the high-throughput composition exploration, their conductivities, and also development of virtual X-ray diffraction simulation system for over 10,000 kinds of multi-substituted composition of the fluorides. Acknowledgement s This research was partially supported by Knowledge Hub Aichi, the GIMRT Program of the Institute for Materials Research, Tohoku University (Proposal No. 202303-CRKKE-0048).

Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials.

Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, we explore machine-learning (ML) methods as a means to predict the Curie temperature (Tc) of ferromagnetic materials by discerning patterns within materials databases. This study emphasizes the importance of feature analysis and selection in ML modeling and demonstrates the efficacy of our gradient-boosted statistical feature-selection workflow for training predictive models. The models are fine-tuned through Bayesian optimization, using features derived solely from the chemical compositions of the materials data, before the model predictions are evaluated against literature values. We have collated ca. 35,000 Tc values and the performance of our workflow is benchmarked against state-of-the-art algorithms, the results of which demonstrate that our methodology is superior to the majority of alternative methods. In a 10-fold cross-validation, our regression model realized an R2 of (0.92 ± 0.01), an MAE of (40.8 ± 1.9) K, and an RMSE of (80.0 ± 5.0) K. We demonstrate the utility of our ML model through case studies that forecast Tc values for rare-earth intermetallic compounds and generate magnetic phase diagrams for various chemical systems. These case studies highlight the importance of a systematic approach to feature analysis and selection in enhancing both the predictive capability and interpretability of ML models, while being devoid of potential human bias. They demonstrate the advantages of such an approach over a mere reliance on algorithmic complexity and a black-box treatment in ML-based modeling within the domain of computational materials science.

Magnetic order in the computational 2D materials database (C2DB) from high throughput spin spiral calculations

We report high throughput computational screening for magnetic ground state order in 2D materials. The workflow is based on spin spiral calculations and yields the magnetic order in terms of a two-dimensional ordering vector Q. We then include spin-orbit coupling to extract the easy and hard axes for collinear structures and the orientation of spiral planes in non-collinear structures. Finally, for all predicted ferromagnets we compute the Dzyaloshinskii-Moriya interactions and determine whether or not these are strong enough to overcome the magnetic anisotropy and stabilise a chiral spin spiral ground state. We find 58 ferromagnets, 21 collinear anti-ferromagnets, and 85 non-collinear ground states of which 15 are chiral spin spirals driven by Dzyaloshinskii-Moriya interactions. The results show that non-collinear order is in fact as common as collinear order in these materials and emphasise the need for detailed investigation of the magnetic ground state when reporting magnetic properties of new materials.

Web-CONEXS: an inroad to theoretical X-ray absorption spectroscopy.

Accurate analysis of the rich information contained within X-ray spectra usually calls for detailed electronic structure theory simulations. However, density functional theory (DFT), time-dependent DFT and many-body perturbation theory calculations increasingly require the use of advanced codes running on high-performance computing (HPC) facilities. Consequently, many researchers who would like to augment their experimental work with such simulations are hampered by the compounding of nontrivial knowledge requirements, specialist training and significant time investment. To this end, we present Web-CONEXS, an intuitive graphical web application for democratizing electronic structure theory simulations. Web-CONEXS generates and submits simulation workflows for theoretical X-ray absorption and X-ray emission spectroscopy to a remote computing cluster. In the present form, Web-CONEXS interfaces with three software packages: ORCA, FDMNES and Quantum ESPRESSO, and an extensive materials database courtesy of the Materials Project API. These software packages have been selected to model diverse materials and properties. Web-CONEXS has been conceived with the novice user in mind; job submission is limited to a subset of simulation parameters. This ensures that much of the simulation complexity is lifted and preliminary theoretical results are generated faster. Web-CONEXS can be leveraged to support beam time proposals and serve as a platform for preliminary analysis of experimental data.

Transfer learning across different chemical domains: virtual screening of organic materials with deep learning models pretrained on small molecule and chemical reaction data

Machine learning is becoming a preferred method for the virtual screening of organic materials due to its cost-effectiveness over traditional computationally demanding techniques. However, the scarcity of labeled data for organic materials poses a significant challenge for training advanced machine learning models. This study showcases the potential of utilizing databases of drug-like small molecules and chemical reactions to pretrain the BERT model, enhancing its performance in the virtual screening of organic materials. By fine-tuning the BERT models with data from five virtual screening tasks, the version pretrained with the USPTO–SMILES dataset achieved R2 scores exceeding 0.94 for three tasks and over 0.81 for two others. This performance surpasses that of models pretrained on the small molecule or organic materials databases and outperforms three traditional machine learning models trained directly on virtual screening data. The success of the USPTO–SMILES pretrained BERT model can be attributed to the diverse array of organic building blocks in the USPTO database, offering a broader exploration of the chemical space. The study further suggests that accessing a reaction database with a wider range of reactions than the USPTO could further enhance model performance. Overall, this research validates the feasibility of applying transfer learning across different chemical domains for the efficient virtual screening of organic materials.Scientific contributionThis study verifies the feasibility of applying transfer learning to large language models in different chemical fields to help organic materials perform virtual screening. Through the comparison of transfer learning from different chemical fields to a variety of organic material molecules, the high precision virtual screening of organic materials is realized.

Preparation and mechanical properties of the 2.5D carbon glass hybrid woven composite materials

Abstract The 2.5D carbon glass hybrid woven composite (CGHWC) is currently being utilized as a protective material for the external cables of solid rockets due to its extensive advantages in terms of structural load-bearing and integrated thermal insulation. This study reveals that 2.5D hybrid woven composites, prepared using T300 and T800, exhibit improved compression and tensile properties, respectively. T300 and T800 carbon fibers are preferred for the preparation of 2.5D CGHWC, with a volume fraction of carbon fibers along the weft yarn ranging from 20 to 60%. As the proportion of carbon fibers increases, the weft tensile and compressive strength of the composite material progressively improve, resulting in a maximum increase of 67% in tensile strength and 52% in compressive strength. The optimal volume fraction of carbon fibers was found to be 40%. The performance retention of low-cost hybrid composite materials was investigated, and it was found that even when the cost of carbon fibers decreased by 87.5%, the mechanical properties could still be maintained at above 85%. This article has expanded the mechanical performance database of hybrid composite materials, providing data support and theoretical foundation for the large-scale engineering application of composite materials.

Data-Driven Identification unravels multiaxial mechanical response of a carbon-black filled elastomer during ageing

Under environmental exposure, the mechanical properties of elastomers change due to ageing, all while enduring mechanical service loading conditions. The influence of ageing on the multiaxial mechanical response of elastomers remains an understudied question, lacking exploration in both experimental evidence and modelling proposals. The present study describes an experimental/numerical approach to characterize the multiaxial behaviour of elastomers with consideration of ageing. This technique associates complex experimental tests conducted with a hexapod device, with a Data-Driven Identification (DDI) algorithm. Practically, heterogeneous strain fields are measured by Digital Image Correlation (DIC), and the corresponding stress and energy fields are calculated by DDI. These fields are visualized through three-dimensional maps, encompassing kinematical quantities and strain energy density. These maps convincingly capture the stiffening induced by ageing, in different deformation modes. Finally, the coupling between ageing and multiaxiality is foregathered in a material database that can be fitted for further modelling purposes.