{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T22:11:32Z","timestamp":1769724692585,"version":"3.49.0"},"reference-count":67,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"vor","delay-in-days":30,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"tdm","delay-in-days":30,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000739","name":"University of Southampton","doi-asserted-by":"crossref","award":["IG"],"award-info":[{"award-number":["IG"]}],"id":[{"id":"10.13039\/501100000739","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2024,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Design of high entropy alloys (HEA) presents a significant challenge due to the large compositional space and composition-specific variation in their functional behavior. The traditional alloy design would include trial-and-error prototyping and high-throughput experimentation, which again is challenging due to large-scale fabrication and experimentation. To address these challenges, this article presents a computational strategy for HEA design based on the seamless integration of quasi-random sampling, molecular dynamics (MD) simulations and machine learning (ML). A limited number of algorithmically chosen molecular-level simulations are performed to create a Gaussian process-based computational mapping between the varying concentrations of constituent elements of the HEA and effective properties like Young\u2019s modulus and density. The computationally efficient ML models are subsequently exploited for large-scale predictions and multi-objective functionality attainment with non-aligned goals. The study reveals that there exists a strong negative correlation between Al concentration and the desired effective properties of AlCoCrFeNi HEA, whereas the Ni concentration exhibits a strong positive correlation. The deformation mechanism further shows that excessive increase of Al concentration leads to a higher percentage of face-centered cubic to body-centered cubic phase transformation which is found to be relatively lower in the HEA with reduced Al concentration. Such physical insights during the deformation process would be crucial in the alloy design process along with the data-driven predictions. As an integral part of this investigation, the developed ML models are interpreted based on Shapley Additive exPlanations, which are essential to explain and understand the model\u2019s mechanism along with meaningful deployment. The data-driven strategy presented here will lead to devising an efficient explainable ML-based bottom-up approach to alloy design for multi-objective non-aligned functionality attainment.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad55a4","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T22:42:21Z","timestamp":1717800141000},"page":"025082","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Explainable machine learning assisted molecular-level insights for enhanced specific stiffness exploiting the large compositional space of AlCoCrFeNi high entropy alloys"],"prefix":"10.1088","volume":"5","author":[{"given":"K K","family":"Gupta","sequence":"first","affiliation":[]},{"given":"S","family":"Barman","sequence":"additional","affiliation":[]},{"given":"S","family":"Dey","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0778-6515","authenticated-orcid":true,"given":"T","family":"Mukhopadhyay","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2024,7,1]]},"reference":[{"key":"mlstad55a4bib1","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1002\/adem.200300567","article-title":"Nanostructured high\u2010entropy alloys with multiple principal elements: novel alloy design concepts and outcomes","volume":"6","author":"Yeh","year":"2004","journal-title":"Adv. Eng. Mater."},{"key":"mlstad55a4bib2","doi-asserted-by":"publisher","first-page":"1302","DOI":"10.3390\/met11081302","article-title":"Review of recent research on AlCoCrFeNi high-entropy alloy","volume":"11","author":"Tokarewicz","year":"2021","journal-title":"Metals"},{"key":"mlstad55a4bib3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-27013-5_1","article-title":"Overview of high-entropy alloys","author":"Yeh","year":"2016","edition":"eds"},{"key":"mlstad55a4bib4","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1007\/s11837-013-0761-6","article-title":"Alloy design strategies and future trends in high-entropy alloys","volume":"65","author":"Yeh","year":"2013","journal-title":"JOM"},{"key":"mlstad55a4bib5","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1126\/science.1254581","article-title":"A fracture-resistant high-entropy alloy for cryogenic applications","volume":"345","author":"Gludovatz","year":"2014","journal-title":"Science"},{"key":"mlstad55a4bib6","doi-asserted-by":"publisher","first-page":"3481","DOI":"10.1016\/j.corsci.2010.06.025","article-title":"Pitting corrosion of the high-entropy alloy Co1.5CrFeNi1.5Ti0.5Mo0.1 in chloride-containing sulphate solutions","volume":"52","author":"Chou","year":"2010","journal-title":"Corros. Sci."},{"key":"mlstad55a4bib7","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.matlet.2014.05.134","article-title":"A refractory Hf25Nb25Ti25Zr25 high-entropy alloy with excellent structural stability and tensile properties","volume":"130","author":"Wu","year":"2014","journal-title":"Mater. Lett."},{"key":"mlstad55a4bib8","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.triboint.2015.06.013","article-title":"Tribological behavior of AlCoCrCuFeNi and AlCoCrFeNiTi0.5 high entropy alloys under hydrogen peroxide solution against different counterparts","volume":"92","author":"Yu","year":"2015","journal-title":"Tribol. Int."},{"key":"mlstad55a4bib9","article-title":"Microstructure and mechanical behavior of AlCoCuFeNi high-entropy alloy fabricated by selective laser melting","author":"Zhang","year":"2017"},{"key":"mlstad55a4bib10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jmst.2020.04.008","article-title":"Effects of AlCoCrFeNiTi high-entropy alloy on microstructure and mechanical properties of pure aluminum","volume":"52","author":"Li","year":"2020","journal-title":"J. Mater. Sci. Technol."},{"key":"mlstad55a4bib11","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1016\/j.jallcom.2017.08.067","article-title":"Understanding the mechanical behaviour and the large strength\/ductility differences between FCC and BCC AlxCoCrFeNi high entropy alloys","volume":"726","author":"Joseph","year":"2017","journal-title":"J. Alloys Compd."},{"key":"mlstad55a4bib12","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.1179\/1743284715Y.0000000032","article-title":"Effect of Al on structure and mechanical properties of AlxNbTiVZr (x= 0, 0.5, 1, 1.5) high entropy alloys","volume":"31","author":"Stepanov","year":"2015","journal-title":"Mater. Sci. Technol."},{"key":"mlstad55a4bib13","doi-asserted-by":"publisher","first-page":"2030","DOI":"10.1007\/s11837-014-1066-0","article-title":"Microstructure and properties of aluminum-containing refractory high-entropy alloys","volume":"66","author":"Senkov","year":"2014","journal-title":"JOM"},{"key":"mlstad55a4bib14","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.actamat.2016.08.081","article-title":"A critical review of high entropy alloys and related concepts","volume":"122","author":"Miracle","year":"2017","journal-title":"Acta Mater."},{"key":"mlstad55a4bib15","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1007\/s11669-023-01024-4","article-title":"Experimental and computational study of microstructure of Al2FeCoNiCu high-entropy alloy","volume":"44","author":"Kivy","year":"2023","journal-title":"J. Phase Equilib. Diffus."},{"key":"mlstad55a4bib16","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1080\/21663831.2014.985855","article-title":"A novel low-density, high-hardness, high-entropy alloy with close-packed single-phase nanocrystalline structures","volume":"3","author":"Youssef","year":"2015","journal-title":"Mater. Res. Lett."},{"key":"mlstad55a4bib17","author":"Campbell","year":"2012"},{"key":"mlstad55a4bib18","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/978-3-319-69743-7_5","article-title":"Magnesium and magnesium alloys","author":"Dieringa","year":"2018"},{"key":"mlstad55a4bib19","first-page":"4865","article-title":"Magnesium and aluminum alloys in automotive industry","volume":"8","author":"Musfirah","year":"2012","journal-title":"J. Appl. Sci. Res."},{"key":"mlstad55a4bib20","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1016\/j.jallcom.2014.09.073","article-title":"Microstructures and mechanical properties of AlxCrFeNiTi0.25 alloys","volume":"619","author":"Liu","year":"2015","journal-title":"J. Alloys Compd."},{"key":"mlstad55a4bib21","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.intermet.2012.03.005","article-title":"Effects of AL addition on microstructure and mechanical properties of AlxCoCrFeNi High-entropy alloy","volume":"648","author":"Yang","year":"2015","journal-title":"Mater. Sci. Eng."},{"key":"mlstad55a4bib22","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.msea.2008.12.053","article-title":"Microstructure and mechanical properties of CoCrFeNiTiAlx high-entropy alloys","volume":"508","author":"Zhang","year":"2009","journal-title":"Mater. Sci. Eng."},{"key":"mlstad55a4bib23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jallcom.2022.166273","article-title":"Enhanced thermal stability of nanocrystalline Cu-Al alloy by nanotwin and nanoprecipitate","volume":"922","author":"Sikdar","year":"2022","journal-title":"J. Alloys Compd."},{"key":"mlstad55a4bib24","doi-asserted-by":"publisher","DOI":"10.1016\/j.mtcomm.2022.104843","article-title":"A comparative investigation of shock response in high entropy cantor alloys by MEAM and LJ type potentials","volume":"33","author":"Sircar","year":"2022","journal-title":"Mater. Today Commun."},{"key":"mlstad55a4bib25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S2424913024500036","article-title":"Probing atomistic deformation behavior of graphene-coated Al0.3CoCrFeNi high-entropy alloy under nanoindentation","author":"Barman","year":"2024","journal-title":"J. Micromech. Mol. Phys."},{"key":"mlstad55a4bib26","doi-asserted-by":"publisher","DOI":"10.1088\/1361-651X\/ad2789","article-title":"Enhancing mechanical performance of Al0.3CoCrFeNi HEA films through graphene coating: insights from nanoindentation and dislocation mechanism analysis","volume":"32","author":"Barman","year":"2024","journal-title":"Modelling Simul. Mater. Sci. Eng."},{"key":"mlstad55a4bib27","doi-asserted-by":"publisher","DOI":"10.1016\/j.matchemphys.2023.128489","article-title":"Grain refinement induced by grain boundary segregation in FeNiCrCoCu high-entropy alloys using molecular dynamics simulation of nanoindentation","volume":"310","author":"Wang","year":"2023","journal-title":"Mater. Chem. Phys."},{"key":"mlstad55a4bib28","doi-asserted-by":"publisher","DOI":"10.1016\/j.matchemphys.2023.127556","article-title":"Atomic-scale analysis of deformation behavior of face-centered cubic nanocrystalline high-entropy alloys with different grain sizes at high strain rates","volume":"300","author":"Jiang","year":"2023","journal-title":"Mater. Chem. Phys."},{"key":"mlstad55a4bib29","doi-asserted-by":"publisher","DOI":"10.1016\/j.matchemphys.2022.126725","article-title":"Effects of crystal orientation and twin boundary distance on mechanical properties of FeNiCrCoCu high-entropy alloy under nanoindentation","volume":"291","author":"Doan","year":"2022","journal-title":"Mater. Chem. Phys."},{"key":"mlstad55a4bib30","doi-asserted-by":"publisher","DOI":"10.1016\/j.euromechsol.2022.104760","article-title":"Nano-sized single-asperity friction behavior: insight from molecular dynamics simulations","volume":"96","author":"Xie","year":"2022","journal-title":"Eur. J. Mech. A"},{"key":"mlstad55a4bib31","doi-asserted-by":"publisher","DOI":"10.1016\/j.apsusc.2023.156502","article-title":"Probing the molecular-level energy absorption mechanism and strategic sequencing of graphene\/Al composite laminates under high-velocity ballistic impact of nano-projectiles","volume":"629","author":"Gupta","year":"2023","journal-title":"Appl. Surf. Sci."},{"key":"mlstad55a4bib32","doi-asserted-by":"publisher","DOI":"10.1016\/j.mtcomm.2022.103932","article-title":"Probing the stochastic fracture behavior of twisted bilayer graphene: efficient ANN based molecular dynamics simulations for complete probabilistic characterization","volume":"32","author":"Gupta","year":"2022","journal-title":"Mater. Today Commun."},{"key":"mlstad55a4bib33","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/978-981-19-6278-3_7","article-title":"Ballistic performance of bi-layer graphene: artificial neural network based molecular dynamics simulations","author":"Gupta","year":"2022"},{"key":"mlstad55a4bib34","doi-asserted-by":"publisher","DOI":"10.1016\/j.euromechsol.2023.105021","article-title":"Trans-scale rough surface contact model based on molecular dynamics method: simulation, modeling and experimental verification","volume":"100","author":"Xie","year":"2023","journal-title":"Eur. J. Mech. A Solids"},{"key":"mlstad55a4bib35","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1080\/08927022.2023.2268184","article-title":"Probing the mechanical and deformation behaviour of CNT-reinforced AlCoCrFeNi high-entropy alloy\u2013a molecular dynamics approach","volume":"49","author":"Barman","year":"2023","journal-title":"Mol. Simul."},{"key":"mlstad55a4bib36","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-93272-y","article-title":"Microstructure and composition dependence of mechanical characteristics of nanoimprinted AlCoCrFeNi high-entropy alloys","volume":"11","author":"Doan","year":"2021","journal-title":"Sci. Rep."},{"key":"mlstad55a4bib37","doi-asserted-by":"publisher","first-page":"2071","DOI":"10.1016\/j.jmrt.2021.07.116","article-title":"Influences of strain rate, Al concentration and grain heterogeneity on mechanical behavior of CoNiFeAlxCu1-x high-entropy alloys: a molecular dynamics simulation","volume":"14","author":"Wang","year":"2021","journal-title":"J. Mater. Res. Technol."},{"key":"mlstad55a4bib38","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"mlstad55a4bib39","doi-asserted-by":"publisher","DOI":"10.1016\/j.euromechsol.2023.105175","article-title":"Prediction of nonlocal elasticity parameters using high-throughput molecular dynamics simulations and machine learning","volume":"103","author":"Lal","year":"2024","journal-title":"Eur. J. Mech. A"},{"key":"mlstad55a4bib40","doi-asserted-by":"publisher","DOI":"10.1016\/j.euromechsol.2023.105125","article-title":"Machine learning assisted approach to design lattice materials with prescribed band gap characteristics","volume":"102","author":"Shendy","year":"2023","journal-title":"Eur. J. Mech. A"},{"key":"mlstad55a4bib41","doi-asserted-by":"publisher","DOI":"10.1016\/j.euromechsol.2023.105180","article-title":"A machine learning approach to automate ductile damage parameter selection using finite element simulations","volume":"103","author":"O\u2019Connor","year":"2023","journal-title":"Eur. J. Mech. A"},{"key":"mlstad55a4bib42","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2020.109618","article-title":"Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys","volume":"175","author":"Dai","year":"2020","journal-title":"Comput. Mater. Sci."},{"key":"mlstad55a4bib43","doi-asserted-by":"publisher","DOI":"10.1016\/j.matchemphys.2023.127537","article-title":"Discovery of novel low modulus Nb\u2013Ti\u2013Zr biomedical alloys via combined machine learning and first principles approach","volume":"299","author":"Huang","year":"2023","journal-title":"Mater. Chem. Phys."},{"key":"mlstad55a4bib44","doi-asserted-by":"publisher","DOI":"10.1016\/j.jallcom.2023.171595","article-title":"Bayesian approach for inferrable machine learning models of process\u2013structure\u2013property linkages in complex concentrated alloys","volume":"967","author":"Thoppil","year":"2023","journal-title":"J. Alloys Compd."},{"key":"mlstad55a4bib45","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.actamat.2019.03.010","article-title":"Machine learning assisted design of high entropy alloys with desired property","volume":"170","author":"Wen","year":"2019","journal-title":"Acta Mater."},{"key":"mlstad55a4bib46","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2021.110389","article-title":"Improving phase prediction accuracy for high entropy alloys with machine learning","volume":"192","author":"Risal","year":"2021","journal-title":"Comput. Mater. Sci."},{"key":"mlstad55a4bib47","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmatsci.2022.101018","article-title":"Machine learning for high-entropy alloys: progress, challenges and opportunities","volume":"131","author":"Liu","year":"2023","journal-title":"Prog. Mater. Sci."},{"key":"mlstad55a4bib48","doi-asserted-by":"publisher","DOI":"10.1016\/j.actamat.2021.116917","article-title":"Modeling solid solution strengthening in high entropy alloys using machine learning","volume":"212","author":"Wen","year":"2021","journal-title":"Acta Mater."},{"key":"mlstad55a4bib49","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.actamat.2019.11.067","article-title":"Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models","volume":"185","author":"Zhang","year":"2020","journal-title":"Acta Mater."},{"key":"mlstad55a4bib50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmecsci.2022.107065","article-title":"Vacancy dependent mechanical behaviors of high-entropy alloy","volume":"218","author":"Peng","year":"2022","journal-title":"Int. J. Mech. Sci."},{"key":"mlstad55a4bib51","doi-asserted-by":"publisher","DOI":"10.1002\/smll.202102972","article-title":"Machine learning accelerated, high throughput, multi\u2010objective optimization of multiprincipal element alloys","volume":"17","author":"Guo","year":"2021","journal-title":"Small"},{"key":"mlstad55a4bib52","doi-asserted-by":"publisher","DOI":"10.1016\/j.commatsci.2022.111888","article-title":"Exploration of V\u2013Cr\u2013Fe\u2013Co\u2013Ni high-entropy alloys with high yield strength: a combination of machine learning and molecular dynamics simulation","volume":"217","author":"Chen","year":"2023","journal-title":"Comput. Mater. Sci."},{"key":"mlstad55a4bib53","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1039\/D1MA00880C","article-title":"Hybrid machine-learning-assisted quantification of the compound internal and external uncertainties of graphene: towards inclusive analysis and design","volume":"3","author":"Gupta","year":"2022","journal-title":"Mater. Adv."},{"key":"mlstad55a4bib54","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1177\/1099636216682533","article-title":"A multivariate adaptive regression splines based damage identification methodology for web core composite bridges including the effect of noise","volume":"20","author":"Mukhopadhyay","year":"2018","journal-title":"J. Sandwich Struct. Mater."},{"key":"mlstad55a4bib55","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2021.108171","article-title":"LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales","volume":"271","author":"Thompson","year":"2022","journal-title":"Comput. Phys. Commun."},{"key":"mlstad55a4bib56","doi-asserted-by":"publisher","first-page":"3031","DOI":"10.1557\/jmr.2020.294","article-title":"Model interatomic potentials for Fe\u2013Ni\u2013Cr\u2013Co\u2013Al high-entropy alloys","volume":"35","author":"Farkas","year":"2020","journal-title":"J. Mater. Res."},{"key":"mlstad55a4bib57","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/0927-0256(94)90109-0","article-title":"Systematic analysis of local atomic structure combined with 3D computer graphics","volume":"2","author":"Faken","year":"1994","journal-title":"Comput. Mater. Sci."},{"key":"mlstad55a4bib58","doi-asserted-by":"publisher","DOI":"10.1088\/0965-0393\/18\/1\/015012","article-title":"Visualization and analysis of atomistic simulation data with OVITO\u2013the open visualization tool","volume":"18","author":"Stukowski","year":"2009","journal-title":"Modelling Simul. Mater. Sci. Eng."},{"key":"mlstad55a4bib59","doi-asserted-by":"publisher","DOI":"10.1061\/JMCEE7.MTENG-17253","article-title":"Closure to \u2018probing the stochastic unconfined compressive strength of lime\u2013RHA mix treated clayey soil\u2019","volume":"36","author":"Gautam","year":"2024","journal-title":"J. Mater. Civ. Eng."},{"key":"mlstad55a4bib60","doi-asserted-by":"publisher","first-page":"529","DOI":"10.12989\/anr.2022.12.5.529","article-title":"High-velocity ballistics of twisted bilayer graphene under stochastic disorder","volume":"12","author":"Gupta","year":"2022","journal-title":"Adv. Nano Res."},{"key":"mlstad55a4bib61","first-page":"4768","article-title":"A unified approach to interpreting model predictions","volume":"vol 30","author":"Lundberg","year":"2017"},{"key":"mlstad55a4bib62","doi-asserted-by":"publisher","DOI":"10.1016\/j.mtcomm.2021.102525","article-title":"Microstructural evolution and mechanical properties of AlxCoCrFeNi high-entropy alloys under uniaxial tension: a molecular dynamics simulations study","volume":"28","author":"Jiang","year":"2021","journal-title":"Mater. Today Commun."},{"key":"mlstad55a4bib63","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.intermet.2017.08.004","article-title":"Dislocation dynamics in Al0.1CoCrFeNi high-entropy alloy under tensile loading","volume":"91","author":"Sharma","year":"2017","journal-title":"Intermetallics"},{"key":"mlstad55a4bib64","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1007\/s13239-016-0286-6","article-title":"Mechanical properties of high entropy alloy al0.1cocrfeni for peripheral vascular stent application","volume":"7","author":"Alagarsamy","year":"2016","journal-title":"Cardiovasc. Eng. Technol."},{"key":"mlstad55a4bib65","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruct.2023.117601","article-title":"Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of polymeric graphene-enhanced composites","volume":"327","author":"Liu","year":"2024","journal-title":"Compos. Struct."},{"key":"mlstad55a4bib66","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s40192-018-0108-9","article-title":"Machine learning prediction of heat capacity for solid inorganics","volume":"7","author":"Kauwe","year":"2018","journal-title":"Integr. Mater. Manuf. Innov."},{"key":"mlstad55a4bib67","doi-asserted-by":"publisher","first-page":"7324","DOI":"10.1021\/acs.chemmater.6b02724","article-title":"High-throughput machine-learning-driven synthesis of full-Heusler compounds","volume":"28","author":"Oliynyk","year":"2016","journal-title":"Chem. Mater."}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad55a4","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad55a4\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad55a4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad55a4\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad55a4\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad55a4\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad55a4\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad55a4\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T07:58:57Z","timestamp":1719820737000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ad55a4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,1]]},"references-count":67,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,7,1]]},"published-print":{"date-parts":[[2024,6,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ad55a4","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,1]]},"assertion":[{"value":"Explainable machine learning assisted molecular-level insights for enhanced specific stiffness exploiting the large compositional space of AlCoCrFeNi high entropy alloys","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2024 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2024-02-06","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-06-07","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-07-01","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}