<?xml version="1.0" encoding="UTF-8"?><doi_batch version="4.3.7" xmlns="http://www.crossref.org/schema/4.3.7" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd">
		<head>
		<doi_batch_id>iamrp.net-bWlD-1788905500-g344350134</doi_batch_id>
		<timestamp>1788905500</timestamp>
		<depositor>
			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
			<email_address>info@iamrp.net</email_address>
		</depositor>
		<registrant>Institute for Advanced Materials Research Press</registrant>
	</head>
	<body>
		<journal>
			<journal_metadata>
				<full_title>Journal of Artificial Intelligence for Materials Science</full_title>
				<abbrev_title>J. Artif. Intell. Mater. Sci.</abbrev_title>
				<issn>3149-8957</issn>
			</journal_metadata>
			<journal_issue>
				<publication_date>
					<year>2024</year>
				</publication_date>
				<journal_volume>
					<volume>3</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>A Theory of Multi-Objective Trade-Offs for Sustainable Materials Optimization with AI</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Daniel</given_name>
            <surname>Fischer</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Laura</given_name>
            <surname>Meier</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Thomas</given_name>
            <surname>Braun</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Stefan</given_name>
            <surname>Koch</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Felix</given_name>
            <surname>Roth</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2024</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/g344350134</doi>
					<resource>https://iamrp.net/pub/journal/1/article/g344350134</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/g344350134-e68fc93d-4fea-434b-8723-190e1d9fe8f7">
					  <unstructured_citation>Deb K, Jain H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints. IEEE Trans Evol Comput. 2020;24(2):238–52.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-5739e74a-5ff0-466a-9df3-5b07069af48c">
					  <unstructured_citation>Miettinen K, Ruiz F, Wierzbicki AP. Introduction to multi-objective optimization: interactive approaches. In: Multi-objective Optimization; 2020. p. 1–29.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-9604f415-5594-457b-9168-b934ff7c4a46">
					  <unstructured_citation>Barbhuiya S, Barbhuiya S. Life Cycle Assessment of construction materials: A review. J Build Eng. 2023;74:106806.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-81b0c101-cc0d-4124-a427-65db0d495af2">
					  <unstructured_citation>Sala S, Benini L, Castellani V, Vidal-Legaz B. The need for integrated sustainability assessment in product and materials decision-making. Sustain Prod Consum. 2021;25:310–21.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-d68428bc-2b75-4e99-beaf-8099830e2876">
					  <unstructured_citation>Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science: Recent directions and sustainability-linked opportunities. npj Comput Mater. 2021;7:1–15.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-c91ccde8-1ed4-4b82-83ca-59c3260ee5b8">
					  <unstructured_citation>Jablonka KM, Ongari D, Moosavi SM, Smit B. Big-data science in porous materials: machine learning and multi-objective design. Chem Rev. 2020;120(16):8066–129.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-ca15f29b-57e2-4f4e-bf30-0ada726722bc">
					  <unstructured_citation>Dignum V. Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer; 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-f7fc783c-7411-4dcc-a24f-64338808169e">
					  <unstructured_citation>Theodorou A, Dignum V. Towards ethical and socio-legal governance in AI. AI Soc. 2020;35:379–86.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-841b72b6-bf9f-4c6f-985b-173ca66db3bc">
					  <unstructured_citation>Zhang P, Chen X, Shen L. Multi-objective optimization for sustainable design integrating LCA and decision-making. J Clean Prod. 2021;314:128095.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-8c3f4104-1e0a-4cde-b6af-819d9218b187">
					  <unstructured_citation>ISO. ISO 14040:2020 Environmental management—Life cycle assessment—Principles and framework. 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-9e17c715-3e83-49e5-ab1c-70943f5da8b5">
					  <unstructured_citation>Watari T, Nansai K, Nakajima K. Review of criticality assessment and implications for materials substitution and sustainability. Resour Conserv Recycl. 2020;162:105031.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-5161abed-2d2a-41fa-8bf5-2d938509e875">
					  <unstructured_citation>Tavazza F, DeCost BL, Choudhary K. Uncertainty prediction for machine learning models of materials properties. ACS Omega. 2021;6(7):4374–82.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-71bd38c1-fca3-453a-93cc-9ffe5b9d8352">
					  <unstructured_citation>Musil F, Willatt MJ, Langovoy M, Ceriotti M. Fast and accurate uncertainty-aware machine learning for molecular and materials science. Chem Rev. 2021;121(16):9759–815.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-b29facdc-d018-46ed-87ea-53bcd8993bc9">
					  <unstructured_citation>Zhang Y, Ling C. A strategy to apply machine learning to small datasets in materials science. npj Comput Mater. 2020;6:25.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-9dd61b2c-c0a1-4560-997a-dc08d7ca84f9">
					  <unstructured_citation>Chen C, Ye W, Zuo Y, Zheng C, Ong SP. Graph networks as a universal machine learning framework for molecules and crystals. Chem Mater. 2020;32(9):3564–75.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-b519b76f-6c59-463c-a63d-e488d1a27d4d">
					  <unstructured_citation>Moosavi SM, Jablonka KM, Ongari D, Smit B. Generative models for inverse design of materials. Nat Rev Mater. 2022;7:1–18.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-d44f5182-43fa-4fb4-8c44-b7bb9f24daa5">
					  <unstructured_citation>Laakso J, Himanen L, Pouillon Y, Jäger MOJ, Foster AS. Updates to the DScribe library: New descriptors and derivatives. J Chem Phys. 2023;158(23):234802.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-47b93dc2-bb28-4000-a1c4-d8c842ab7c1a">
					  <unstructured_citation>Himanen L, Jäger MOJ, Morooka EV, Federici Canova F, Ranawat YS, Gao DZ, et al. DScribe: Library of descriptors for machine learning in materials science. Comput Phys Commun. 2020;247:106949.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-6c384d66-3f69-443f-843d-fd9bf06f61e7">
					  <unstructured_citation>Shi B, Huang Y, Li X. Multi-objective optimization and its application in materials science: A review. Mater Genet Eng Adv. 2023;1:14.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-17acb3ce-2e30-42e9-97de-6c8b3c933139">
					  <unstructured_citation>Zhang P, Liu Y, Zhang J. Multi-objective optimization for materials design with improved NSGA-II. Mater Today Commun. 2021;29:102858.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-10c59e30-e51d-4bf4-8991-ccee38362a30">
					  <unstructured_citation>Jain A, Ong SP, Hautier G, Chen W, Persson KA. Materials Project: accelerating materials design through open databases and AI (2020 update). npj Comput Mater. 2020;6:1–10.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-c31dfbf0-5495-4b50-9fee-2bbf91b99a5a">
					  <unstructured_citation>Chen Y, Li Y, Zhang H. Multi-objective optimization method for sustainable material design using LCA and LCC. Sustainability. 2023;16(1):168.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-50df401d-1c22-4564-99c4-9677135a6e62">
					  <unstructured_citation>Seifi M, Salem A, Satko D, Shao S. The role of sustainability metrics in materials and manufacturing decisions: A 2020–2023 synthesis. Mater Des. 2022;223:111119.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-5cfebb2b-4a2e-4d54-9878-6d41894ec3e1">
					  <unstructured_citation>Himeur Y, Ghanem K, Alsalemi A, Bensaali F. Multi-objective decision-making under uncertainty for sustainability: A 2022 review. Renew Sustain Energy Rev. 2022;160:112287.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-d8a51f2a-f78e-45b1-92a6-8065d04a3ce0">
					  <unstructured_citation>Allwood JM, Cullen JM. Sustainable Materials: With Both Eyes Open (updated 2020 edition). 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-dc985ae2-991c-44c9-ae70-224e382a30f0">
					  <unstructured_citation>European Commission Joint Research Centre. Product Environmental Footprint (PEF) method: 2021 update. 2021.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-ad52ae80-07c9-45ce-a73d-80e8828e1f4b">
					  <unstructured_citation>Hellweg S, Milà i Canals L. Emerging approaches to LCA under uncertainty and scenario dependence. Science. 2020;370(6517):eabc8747.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-603b52b2-42ca-4e17-9363-cf621ca8ebb4">
					  <unstructured_citation>Guinée JB, Heijungs R, Huijbregts MAJ. Lifecycle impact assessment: recent methodological advances. Int J Life Cycle Assess. 2021;26:1–12.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-bf98274d-e51c-421d-a707-eabc231e671a">
					  <unstructured_citation>Arvidsson R, Molander S. Prospective LCA for emerging technologies and materials: Pitfalls and principles. J Ind Ecol. 2020;24(4):820–32.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-36cc3c87-7fd8-4f5a-b9a5-b44a27002921">
					  <unstructured_citation>Marler RT, Arora JS. Survey of multi-objective optimization methods for engineering. Struct Multidisc Optim. 2020;62:1–18.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-a45d0a90-af99-4c93-b8d4-ef8bf7ed2e0b">
					  <unstructured_citation>Keeney RL, Raiffa H. Decisions with Multiple Objectives: Preferences and Value Trade-offs (revisited 2021 commentary edition). 2021.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-0458c90a-1b27-46ae-a821-b5a6bbbc5301">
					  <unstructured_citation>Roy B. Multicriteria decision aiding: Recent advances for sustainability decisions. Eur J Oper Res. 2020;283(2):409–23.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-3f8a400d-7798-4676-b349-910b6108c4a4">
					  <unstructured_citation>Floridi L, Cowls J. A unified framework of AI principles. Nat Mach Intell. 2020;2:370–3.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-cc26929f-e44d-417f-9933-91f2d08590c2">
					  <unstructured_citation>Raji ID, Smart A, White RN, Mitchell M, Gebru T, Hutchinson B, et al. Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. In: FAT* 2020 Proceedings; 2020. p. 33–44.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/g344350134-0f1744e1-8842-4b0a-ae89-12408287f4a6">
					  <unstructured_citation>Tomašev N, Cornebise J, Hutter F, Mohamed S, Picciariello A, Connelly B, et al. AI for social good: Unlocking the opportunity for impact. Nat Commun. 2020;11:2468.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
