ABSTRACT This work introduces a comprehensive dataset and framework for assessing the sustainability of high entropy alloys (HEAs) and other metallic alloys. The dataset includes nine

crafted indicators—raw material price, supply risk, normalized vulnerability to supply restriction, embodied energy, water usage, rock-to-metal ratio, human health damage, human rights

pressure, and labor rights pressure—for 18 elements: Al, Co, Cr, Cu, Fe, Hf, Mn, Mo, Nb, Ni, Re, Ru, Si, Ta, Ti, V, W, and Zr. This methodology evaluates economic viability, environmental

impact, and societal implications using alloy compositions as input. The Python package, AlloySustainability, streamlines indicator computation and enables users to benchmark alloys against

SIMILAR CONTENT BEING VIEWED BY OTHERS DATASET OF MECHANICAL PROPERTIES AND ELECTRICAL CONDUCTIVITY OF COPPER-BASED ALLOYS Article Open access 29 July 2023 CRITICAL RAW MATERIAL-FREE

MULTI-PRINCIPAL ALLOY DESIGN FOR A NET-ZERO FUTURE Article Open access 24 January 2025 EXPANDED DATASET OF MECHANICAL PROPERTIES AND OBSERVED PHASES OF MULTI-PRINCIPAL ELEMENT ALLOYS Article

Open access 08 December 2020 BACKGROUND & SUMMARY The development of alloys is increasingly shaped by societal needs and environmental regulations. For instance, the European Union’s

Restriction of Hazardous Substances (RoHS) Directive, specifically Directive 2011/65/EU (also known as RoHS 2) issued by the European Parliament and Council on June 8, 2011, which limits the

use of certain hazardous substances in electrical and electronic equipment, catalyzed the shift from Tin-Lead to Lead-free solders in electronic products, establishing Sn-Ag-Cu (SAC) alloys

as the new industry standard. Similarly, the EU’s REACH regulations, formally known as Regulation (EC) No. 1907/2006 of the European Parliament and of the Council of December 18, 2006, have

imposed strict controls on the use of hexavalent chromium. This has prompted the advancement and adoption of alternative surface treatments, such as zinc-nickel coatings and trivalent

chromium anodizing processes. Concerns over nickel’s health impacts have led to the innovation of nickel-free stainless steels, utilizing manganese (Mn) or nitrogen (N) as substitutes.

Additionally, recognizing the health risks posed by beryllium, the industry has seen a transition to beryllium-free copper alloys like Cu-Ni-Si. These shifts underline a critical insight:

materials science is integral to an ecosystem where societal and environmental considerations are not merely constraints but catalysts for innovation. This continuous evolution in materials

is propelled by the proactive integration of these considerations, reflecting a commitment to responsible development and the betterment of society. With High Entropy Alloys (HEAs) at the

forefront of research due to their unique properties and distinctive characteristics, it has become imperative to rigorously evaluate their societal impact since the concept was first

proposed in 20041,2. Reflecting on this, Cann _et al_.3. highlighted that “Significant efforts are needed to select economically and environmentally viable alloy combinations that meet

today’s technical demands.” This statement encapsulates the essence of our research and the critical need for a balanced approach to alloy design. Recent studies have begun to explore the

broader implications of HEAs beyond their technical performance. Fu _et al_.4 have examined resource efficiency, considering factors such as price, availability, and recyclability, while

emphasizing the need to address economic and environmental aspects of HEA design early in the development process to ensure their technological viability. Similarly, Wang _et al_.5 have

analyzed availability, prices, and chemical hazards, focusing specifically on refractory element-based HEAs. While these studies provide valuable initial insights, they remain limited in

scope, addressing only a subset of potential impacts and alloy compositions. Our work seeks to expand these efforts by developing a comprehensive set of indicators that evaluate economic,

environmental, and societal impacts. By examining these factors, we aim to provide a robust framework for guiding alloy design towards more sustainable and responsible practices. Drawing

inspiration from other professions where ethical considerations are deeply integrated—such as ESG principles in investment, bioethics in medicine, and ethical AI—we have crafted a similar

approach for alloy design. This method underscores the importance of making informed, conscientious choices in elemental composition. The indicators selected for this work were meticulously

chosen to align with the three pillars of sustainable development: economic viability, environmental impact, and human well-being. Our methodological approach to selecting these indicators

has been informed by the field’s demand for transparent, ethical, and sustainable practices. Presented to the scientific community during the Thermec’2023 conference in Vienna, our initial

findings have garnered significant interest, reinforcing the importance of sharing our comprehensive database. The set of nine indicators we introduce aims to guide alloy designers towards

compositions that are more economically viable, environmentally conscious, and socially responsible. This initiative led to the creation of a database, presented in this article, consisting

of nine indicators designed to efficiently evaluate the sustainability of alloy compositions containing any of the following elements: Al, Co, Cr, Cu, Fe, Hf, Mn, Mo, Nb, Ni, Re, Ru, Si, Ta,

Ti, V, W, and Zr. This database, freely available at the open repository figshare6 and on GitHub (https://github.com/sgorsse/AlloySustainability), was used to assess the impacts of 340

newly proposed high entropy alloy compositions, as detailed in a recently published study7. Furthermore, we have developed an installable Python package indexed in PyPI

(https://pypi.org/project/AlloySustainability/) that allows users to compute the nine indicators for alloys of their interest within the 18-compositional space defined above. Users can also

compare the computed indicators of their alloys against the median values for FCC HEAs, BCC HEAs, steels, and Ni-based superalloys, providing a benchmark for understanding how their designs

align with or differ from existing compositions. Additionally, we provide a single-use interactive web application, named the Alloy Societal Impact Calculator, hosted on the Model Warehouse

of the AI Machine Learning Platform at the High Entropy Materials Center. This application is freely accessible with registration at:

https://black-stone-0b1668410.3.azurestaticapps.net/#/materials_informatics. METHODS Our methodology evaluates the sustainability of alloy compositions using nine specific indicators, each

providing insights into a distinct category. Higher indicator values correspond to greater negative implications. A comprehensive explanation of these indicators and their application can be

found in ref. 7. ECONOMIC VIABILITY. * Raw Material Price (MP): Uses the price of pure alloying additions as a cost proxy. * Supply Risk (SR): Based on Graedel _et al_.‘s aggregated supply

risk indicator8, this metric evaluates the potential for supply disruptions from geopolitical and natural events. * Normalized Vulnerability to Supply Restriction (NVSR): Adjusts the

vulnerability to supply restriction (VSR)8 by the logarithmic production volume of the element, considering both scarcity and the potential for recycling. ENVIRONMENTAL IMPACT. * Embodied

Energy9 (EE): Reflects the total energy required for metal production, from extraction to transportation, correlating with greenhouse gas emissions. By assessing the energy required, we

aimed to provide an indirect yet robust evaluation of the environmental burdens, including carbon footprint, associated with alloying addition production. * Water Usage9 (WU): Measures the

total water consumption in alloying addition production. * Rock to Metal Ratio10 (RMR): Indicates land use intensity in mining operations, with higher ratios pointing to greater

environmental disturbances. HUMAN WELL-BEING. * Human Health Damage (HHD): An aggregate indicator from Graedel _et al_.8 measuring metals’ direct impacts on human health. * Human Rights

Pressure (HRP): Defined as a measure of potential human rights concerns associated with the sourcing of elements. It is derived from the Human Rights Index11 (HRI), which is defined at the

country level and reflects the extent of respect for human rights within each country (_c_). Since higher values of the Human Rights Index (HRI) indicate broader respect for human rights, we

inverted the scale to reflect pressure (higher values indicating greater concerns). The formula below ensures that HRP is normalized within the same range as HRI while maintaining

interpretability, where higher HRP values correspond to greater human rights pressures. $${{HRP}}_{c}={-{HRI}}_{c}+\min \left({{HRI}}_{c}\right)+\max \left({{HRI}}_{c}\right)$$ * Labor

Rights Pressure (LRP): Assesses labor rights concerns in extraction countries (\(c\)), derived from the Labor Rights Index12 (LRI). Using the same approach as HRP, we invert the LRI scale to

reflect pressure, where higher LRP values indicate greater concerns. The formula ensures normalization within the same range as LRI while maintaining interpretability.

$${{LRP}}_{c}={-{LRI}}_{c}+\min \left({{LRI}}_{c}\right)+\max \left({{LRI}}_{c}\right)$$ EVALUATION OF ELEMENT INDICATORS Table 1 lists element indicators, their units, value ranges, source

references, and calculation formulas where applicable. It offers a concise overview of metrics assessing element impacts. Elemental Human Rights Pressure (\({{HRP}}_{i}\)) and Labor Rights

Pressure (\({{LRP}}_{i}\)) are determined by the countries of origin for each element. These indicators can be calculated as a matrix product expressed as follows:

$$\begin{array}{l}{\left(\begin{array}{cc}{{HRP}}_{{Al}} & {{LRP}}_{{Al}}\\ \vdots & \vdots \\ {{HRP}}_{{Zr}} & {{LRP}}_{{Zr}}\end{array}\right)}_{18\times

2}{\mathbb{=}}{\mathbb{G}}{\mathbb{C}}{\mathbb{=}}{\left(\mathop{\sum }\limits_{k=1}^{180}{g}_{i,k}{C}_{k,j}\right)}_{18\times 2}\\ ={\left(\begin{array}{ccc}{g}_{{Al},{Afghanistan}} &

\cdots & {g}_{{Al},{Zimbabwe}}\\ \vdots & \ddots & \vdots \\ {g}_{{Zr},{Afghanistan}} & \cdots & {g}_{{Zr},{Zimbabwe}}\end{array}\right)}_{18\times

180}{\left(\begin{array}{cc}{{HRP}}_{{Afghanistan}} & {{LRP}}_{{Afghanistan}}\\ \vdots & \vdots \\ {{HRP}}_{{Zimbabwe}} & {{LRP}}_{{Zimbabwe}}\end{array}\right)}_{180\times

2}\end{array}$$ here \({\mathbb{G}}\) is the global supply matrix which gives the percentage of the production of raw materials of countries in the world13, and \({\mathbb{C}}\) the country

indicator matrix. STRATEGY FOR IMPUTING MISSING DATA When encountering missing data, such as in 28% of the countries for the Labor Rights Pressure, we addressed the issue by leveraging

correlations for data imputation. We compiled a dataset of 180 countries with 26 governance, social development, and sustainable competitiveness features7, which aided in imputing missing

values for country-dependent indicators. Using a K-Nearest Neighbours (KNN) technique14, adapted from Python’s Scikit-Learn library15, we imputed missing data by finding the mean or median

of the ‘k’ closest neighbors in our feature space. We standardized our imputation process with default Scikit-Learn settings, including using five neighbors to balance underfitting and

overfitting risks. To normalize data and minimize outliers’ impact, we applied ‘RobustScaler’ and ‘MinMaxScaler’ from the Python framework, ensuring equal feature contribution to KNN

distance calculations. This KNN methodology was similarly applied to missing element indicator values, supported by correlation analysis with additional elemental properties, ensuring a

consistent and accurate treatment of missing data across the study. DATA RECORDS Our dataset of impacts, named ‘gen_18element_imputed_v202412’, is available in both CSV and JSON formats and

has been deposited on GitHub and figshare6. In addition to the dataset, we have included detailed metadata in the machine-readable JSON format, and a README file providing documentation for

the dataset and indicators. The dataset includes values for nine crafted indicators for a palette of eighteen elements commonly used in HEAs: Al, Co, Cr, Cu, Fe, Hf, Mn, Mo, Nb, Ni, Re, Ru,

Si, Ta, Ti, V, W, and Zr. Figure 1 illustrates these indicators for the full palette of elements. TECHNICAL VALIDATION The data presented in this article were carefully collected, processed,

and thoroughly verified for accuracy by a team of experienced materials scientists who deeply understand metallurgy, alloy design, high entropy alloys, and materials sustainability. The

K-Nearest Neighbors14 (KNN) imputation method’s effectiveness was verified through self-validation. For this, part of our dataset was manipulated to create missing values, which were then

estimated using KNN for country-dependent indicators. This approach suits the 180-country dataset well, ensuring accurate imputations. Countries naturally form clusters sharing similar

characteristics (e.g., economic development, governance, or resource availability), which ensures that missing values are surrounded by relevant and similar neighbors, thereby enhancing the

reliability of KNN imputations. For element indicators, with a smaller element dataset, we used Leave-One-Out Cross-Validation (LOOCV), ideal for its size, by validating each data point

against the rest. The accuracy of our imputation was measured by comparing imputed values to actual ones, using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) for

quantification. This comparison, shown in Fig. 2, confirms the robustness of our KNN method for both datasets. For more detailed information, please refer to the following reference7. USAGE

NOTES EVALUATION OF ALLOY INDICATORS This database, which evaluates the societal implications of HEAs and conventional alloys, provides data to support more sustainable and responsible alloy

design practices that benefit both industry and society. The price (MP), normalized vulnerability to supply restriction (NVSR), embodied energy (EE), water usage (WU), rock-to-metal ratio

(RMR), human health damage (HHD), human rights pressure (HRP) and labor rights pressure (LRP) associated to an alloy composition are calculated as the weighted arithmetic average of its

elemental constituents expressed in mass fraction. It was implemented in a Python code as the following matrix product:

$$\begin{array}{l}{{\bf{I}}}_{{\bf{alloy}}}\left[{MP},{NVSR},{EE},{WU},{RMR},{HHD},{HRP},{LRP}\right]={\bf{W}}{\mathbb{E}}{\mathbb{=}}{\left(\mathop{\sum

}\limits_{k=1}^{18}{w}_{1,k}{E}_{k,j}\right)}_{1\times 8}\\ ={\left({w}_{1,{Al}},\ldots ,{w}_{1,{Zr}}\right)}_{1\times 18}{\left(\begin{array}{ccc}{{MP}}_{{Al}} & \cdots &

{{LRP}}_{{Al}}\\ \vdots & \ddots & \vdots \\ {{MP}}_{{Zr}} & \cdots & {{LRP}}_{{Zr}}\end{array}\right)}_{18\times 8}\end{array}$$ \({{\bf{I}}}_{{\bf{alloy}}}\) is the vector

of alloy indicators, \({\bf{W}}\) is the compositional vector storing the elemental compositions of alloys (in mass fraction), and \({\mathbb{E}}\) is the matrix of elemental constituents’

indicators (‘gen_18element_imputed_v202412’ deposited on GitHub). Supply Risk (_SR_) was computed uniquely, acknowledging that an alloy’s likelihood of supply disruption escalates with the

number of constituents. It’s defined as a disruption probability between 0 and 1, calculated by multiplying the probabilities of each constituent (_k_), as per the expression:

$${{\bf{I}}}_{{\bf{alloy}}}\left[{SR}\right]=1-\mathop{\prod }\limits_{k=1}^{18}\left(1-S{R}_{k}\right)$$ INSTALLABLE PYTHON PACKAGE To facilitate the calculation of sustainability

indicators for alloys, we provide an installable Python package, _AlloySustainability_. This tool allows users to compute sustainability metrics for their alloys by simply inputting the

chemical composition in terms of mass fractions for 18 elements (Table 2). The package automates the retrieval and processing of reference data, offering both numerical results and

comparative visualizations against established alloy classes (Fig. 3). _AlloySustainability_ is freely available on PyPI (https://pypi.org/project/AlloySustainability/) and can be installed

using the command: _pip install AlloySustainability_. CASE STUDY The dataset serves as a valuable tool for identifying promising compositional spaces while highlighting those with high

societal impacts to avoid. In a recent study7, it was used to evaluate HEAs against high-temperature Ni-based superalloys and steels for their room-temperature strength-ductility trade-offs.

Building on data from three prior studies16,17,18, we analyzed 340 HEA compositions and grouped them based on their potential applications. The first group consists of 225 grades for

high-temperature applications. This includes BCC-type HEAs inspired by Refractory HEAs (RHEAs) introduced by Senkov and Miracle19 composed of refractory metals such as MoNbTaVW. It also

encompasses BCC/B2 refractory superalloys (RSAs)20, and FCC/L12 high entropy superalloys (HESAs)21,22. The second group includes 115 FCC-type HEAs with a Face-Centered Cubic structure,

inspired by the Cantor alloy (CoCrFeMnNi). These alloys, known for their strength and ductility at room temperature, were compared to commonly used steels for load-bearing and

damage-tolerant applications. Figure 4 visualizes a set of 3 indicators among the 9 provided for a subset of 225 high entropy alloy (HEA) compositions that have appeared in the literature

and 29 leading commercial Ni-based superalloys used in turbine blades since 1946. CODE AVAILABILITY Data processing, validation, and plotting were performed using Excel and Jupyter

notebooks23 in a Python 3 environment using common scientific libraries. The Python package _AlloySustainability_, used for computing and visualizing alloy sustainability indicators, is

openly available on PyPI at https://pypi.org/project/AlloySustainability/. Furthermore, the Alloy Social Impact Calculator is located at the Model Warehouse of the AI Machine Learning

Platform of the High Entropy Materials Center (https://black-stone-0b1668410.3.azurestaticapps.net/#/materials_informatics); it can be accessed freely with registration. REFERENCES * Yeh, J.

W. _et al_. Nanostructured High‐Entropy Alloys with Multiple Principal Elements: Novel Alloy Design Concepts and Outcomes. _Advanced Engineering Materials_ 6, 299–303,

https://doi.org/10.1002/adem.200300567 (2004). Article  CAS  MATH  Google Scholar  * Cantor, B., Chang, I. T. H., Knight, P. & Vincent, A. J. B. Microstructural development in equiatomic

multicomponent alloys. _Materials Science and Engineering: A_ 375-377, 213–218, https://doi.org/10.1016/j.msea.2003.10.257 (2004). Article  CAS  Google Scholar  * Cann, J. L. _et al_.

Sustainability through alloy design: Challenges and opportunities. _Progress in Materials Science_ 117 https://doi.org/10.1016/j.pmatsci.2020.100722 (2021). * Fu, X., Schuh, C. A. &

Olivetti, E. A. Materials selection considerations for high entropy alloys. _Scripta Materialia_ 138, 145–150, https://doi.org/10.1016/j.scriptamat.2017.03.014 (2017). Article  CAS  MATH 

Google Scholar  * Wang, X., Kramer, A., Glaubensklee, C., He, H., Schoenung, J.M. Sustainability-Based Selection of Materials for Refractory High Entropy Alloys. _The Minerals, Metals &

Materials Series_ https://doi.org/10.1007/978-3-030-92563-5_38 (2022). * Gorsse, S., Langlois, T. & Barnett, M. R. _Sustainability indicators in high entropy alloy design: an economic,

environmental, and societal database_. _figshare_ https://doi.org/10.6084/m9.figshare.28235162 (2025). * Gorsse, S., Langlois, T. & Barnett, M. R. Considering sustainability when

searching for new high entropy alloys. _Sustainable Materials and Technologies_ 40, e00938, https://doi.org/10.1016/j.susmat.2024.e00938 (2024). Article  CAS  Google Scholar  * Graedel, T.

E., Harper, E. M., Nassar, N. T., Nuss, P. & Reck, B. K. Criticality of metals and metalloids. _Proc Natl Acad Sci USA_ 112, 4257–4262, https://doi.org/10.1073/pnas.1500415112 (2015).

Article  ADS  CAS  PubMed  PubMed Central  Google Scholar  * Ashby, M. F. _Materials and the Environment_. (2021). * Nassar, N. T., Lederer, G. W., Brainard, J. L., Padilla, A. J. &

Lessard, J. D. Rock-to-Metal Ratio: A Foundational Metric for Understanding Mine Wastes. _Environ Sci Technol_ 56, 6710–6721, https://doi.org/10.1021/acs.est.1c07875 (2022). Article  ADS 

CAS  PubMed  PubMed Central  MATH  Google Scholar  * Herre, B. The ‘_Varieties of Democraty’ Data: How Researchers Measure Human Rights?_, https://ourworldindata.org/vdem-human-rights-data

(2022). * Foundation, W. _The Labour Rights Index Database_, https://labourrightsindex.org/ (2022). * Ansys GRANTA EduPack software (ANSYS, Inc., Cambridge, UK, 2021). * Zhang, Z.

Introduction to machine learning: k-nearest neighbors. _Ann Transl Med_ 4, 218, https://doi.org/10.21037/atm.2016.03.37 (2016). Article  PubMed  PubMed Central  MATH  Google Scholar  *

Pedregosa, F. _et al_. Scikit-learn: Machine Learning in Python. _Journal of Machine Learning Research_ 12, 2825–2830 (2011). MathSciNet  MATH  Google Scholar  * Gorsse, S., Miracle, D. B.

& Senkov, O. N. Mapping the world of complex concentrated alloys. _Acta Materialia_ 135, 177–187, https://doi.org/10.1016/j.actamat.2017.06.027 (2017). Article  ADS  CAS  Google Scholar

  * Gorsse, S., Nguyen, M. H., Senkov, O. N. & Miracle, D. B. Database on the mechanical properties of high entropy alloys and complex concentrated alloys. _Data Brief_ 21, 2664–2678,

https://doi.org/10.1016/j.dib.2018.11.111 (2018). Article  CAS  PubMed  PubMed Central  Google Scholar  * Borg, C. K. H. _et al_. Expanded dataset of mechanical properties and observed

phases of multi-principal element alloys. _Sci Data_ 7, 430, https://doi.org/10.1038/s41597-020-00768-9 (2020). Article  CAS  PubMed  PubMed Central  MATH  Google Scholar  * Senkov, O. N.,

Wilks, G. B., Miracle, D. B., Chuang, C. P. & Liaw, P. K. Refractory high-entropy alloys. _Intermetallics_ 18, 1758–1765, https://doi.org/10.1016/j.intermet.2010.05.014 (2010). Article 

CAS  Google Scholar  * Senkov, O., Isheim, D., Seidman, D. & Pilchak, A. Development of a Refractory High Entropy Superalloy. _Entropy_ 18, https://doi.org/10.3390/e18030102 (2016). *

Yeh, A. C. _et al_. Developing New Type of High Temperature Alloys–High Entropy Superalloys. _International Journal of Metallurgical & Materials Engineering_ 1

https://doi.org/10.15344/2455-2372/2015/107 (2015). * Chen, Y.-T., Chang, Y.-J., Murakami, H., Gorsse, S. & Yeh, A.-C. Designing high entropy superalloys for elevated temperature

application. _Scripta Materialia_ 187, 177–182, https://doi.org/10.1016/j.scriptamat.2020.06.002 (2020). Article  CAS  MATH  Google Scholar  * Kluyver, T. _et al_. in _20th International

Conference on Electronic Publishing_. (ed and Loizides F. Schmidt B.). Download references ACKNOWLEDGEMENTS This work has benefited from a government grant managed by the Agence Nationale de

la Recherche under the France 2030 program; reference ANR-22-PEXD-0003. SG and MRB gratefully acknowledge the support of the CNRS through the IRP MALCOM initiative. The AI Machining

Learning Platform website for the Social Impact Calculator is supported by the “High Entropy Materials Center” from The Featured Areas Research Center Program within the framework of the

Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Univ. Bordeaux, CNRS, Bordeaux INP, ICMCB, UMR 5026, F-33600,

Pessac, France Stéphane Gorsse * Institute for Frontier Materials, Deakin University, Deakin, Australia Théo Langlois & Matthew R. Barnett * Department of Materials Science and

Engineering, National Tsing Hua University, R. O. C, Hsinchu, 30013, Taiwan An-Chou Yeh * High Entropy Materials Center, National Tsing Hua University, R. O. C, Hsinchu, 30013, Taiwan

An-Chou Yeh Authors * Stéphane Gorsse View author publications You can also search for this author inPubMed Google Scholar * Théo Langlois View author publications You can also search for

this author inPubMed Google Scholar * An-Chou Yeh View author publications You can also search for this author inPubMed Google Scholar * Matthew R. Barnett View author publications You can

also search for this author inPubMed Google Scholar CONTRIBUTIONS Stéphane Gorsse: Conceptualization; Methodology; Software; Validation; Formal analysis; Investigation; Data curation;

Writing - original draft; Visualization; Supervision. Matthew Barnett: Conceptualization; Methodology; Validation; Formal analysis; Writing - review & editing; Supervision. Théo

Langlois: Formal analysis; Investigation; Data curation; Writing - review & editing. An-Chou Yeh: Validation; Writing - review & editing. CORRESPONDING AUTHOR Correspondence to

Stéphane Gorsse. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to

jurisdictional claims in published maps and institutional affiliations. RIGHTS AND PERMISSIONS OPEN ACCESS This article is licensed under a Creative Commons

Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give

appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission

under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons

licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by

statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Gorsse, S., Langlois, T., Yeh, AC. _et al._ Sustainability indicators in high

entropy alloy design: an economic, environmental, and societal database. _Sci Data_ 12, 288 (2025). https://doi.org/10.1038/s41597-025-04568-x Download citation * Received: 28 March 2023 *

Accepted: 31 January 2025 * Published: 17 February 2025 * DOI: https://doi.org/10.1038/s41597-025-04568-x SHARE THIS ARTICLE Anyone you share the following link with will be able to read

this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative