<?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-Yagb-1792542733-y074219531</doi_batch_id>
		<timestamp>1792542733</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>2022</year>
				</publication_date>
				<journal_volume>
					<volume>1</volume>
				</journal_volume>
				<issue>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Inference Without Ground Truth: A Conceptual Theory of Validation in Materials AI</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Michael</given_name>
            <surname>Turner</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Daniel</given_name>
            <surname>Brooks</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Olivia</given_name>
            <surname>Grant</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2022</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/y074219531</doi>
					<resource>https://iamrp.net/pub/journal/1/article/y074219531</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/y074219531-96ed4469-8745-485a-87ce-9e97f80e362c">
					  <unstructured_citation>Madika B, Saha A, Kang C, Buyantogtokh B, Agar J, Wolverton CM, et al. Artificial intelligence for materials discovery, development, and optimization. ACS Nano. 2025;19(30):27116-58.</unstructured_citation>
						 <doi>10.1021/acsnano.5c04200</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-34737c7f-d4ae-4077-8a5c-ebd9c5bf0c49">
					  <unstructured_citation>Cheetham AK, Seshadri R. Artificial intelligence driving materials discovery? Perspective on the article: Scaling deep learning for materials discovery. Chem Mater. 2024.</unstructured_citation>
						 <doi>10.1021/acs.chemmater.4c00643</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-e945edba-5ad1-4c9f-8323-d2bfd3edf466">
					  <unstructured_citation>Handoko AD, Made RI. Artificial intelligence and generative models for materials discovery: A review. arXiv. 2025;arXiv:2508.03278</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/y074219531-2527b5dc-f534-4aa9-85ca-49844552df47">
					  <unstructured_citation>Gregoire JM, Zhou L, Haber JA. Combinatorial synthesis for ai-driven materials discovery. Nat Synth. 2023;2:493-504.</unstructured_citation>
						 <doi>10.1038/s44160-023-00251-4</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-e0f3aeff-8b46-436a-834e-c0f2e9440305">
					  <unstructured_citation>Li J, Lim K, Yang H, Ren Z, Raghavan S, Chen PY. Ai applications through the whole life cycle of material discovery. Matter. 2020;3(2):393-432.</unstructured_citation>
						 <doi>10.1016/j.matt.2020.06.011</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-7d0a08cb-8144-4983-a2ca-8353fa06206e">
					  <unstructured_citation>Pyzer-Knapp EO, Pitera JW, Staar PWJ. Accelerating materials discovery using artificial intelligence, high performance computing and robotics. Npj Comput Mater. 2022;8(1):84.</unstructured_citation>
						 <doi>10.1038/s41524-022-00765-z</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-72999920-c931-4c1f-9a24-d249343d6bf4">
					  <unstructured_citation>Nematov D, Hojamberdiev M. Machine learning-driven materials discovery: Unlocking next-generation functional materials—a minireview. SSRN. 2025.</unstructured_citation>
						 <doi>10.2139/ssrn.5219988</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-a6184db7-980e-4c36-b5dc-0cff564f4182">
					  <unstructured_citation>Fuhr AS, Sumpter BG. Deep generative models for materials discovery and machine learning-accelerated innovation. Front Mater. 2022;9:865270.</unstructured_citation>
						 <doi>10.3389/fmats.2022.865270</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-f2f2b6f8-143a-46cf-9199-65bf04b7890d">
					  <unstructured_citation>Li C, Zheng K. Methods, progresses, and opportunities of materials informatics. InfoMat. 2023;5(6):e12425.</unstructured_citation>
						 <doi>10.1002/inf2.12425</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-c513f485-de0e-40d5-8d78-8910b8577c36">
					  <unstructured_citation>Hart M, Idanwekhai K, Alves VM, Miller AJM. Trust not verify? The critical need for data curation standards in materials informatics. Chem Mater. 2024.</unstructured_citation>
						 <doi>10.1021/acs.chemmater.4c00981</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-06ac52ae-efba-43dc-a797-c2e4778b72cc">
					  <unstructured_citation>Frydrych K, Karimi K, Pecelerowicz M, Alvarez R. Materials informatics for mechanical deformation: A review of applications and challenges. Materials. 2021;14(19):5764.</unstructured_citation>
						 <doi>10.3390/ma14195764</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-22495ce1-2b4f-4033-a735-c36136d6ed2b">
					  <unstructured_citation>Moran M. An information oriented approach to materials informatics. Liverpool: University of Liverpool Repository; 2025.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/y074219531-339ef412-e50f-4539-b9d2-5cd0458e15f2">
					  <unstructured_citation>Agrawal A, Gopalakrishnan K. Materials image informatics using deep learning. Singapore: World Scientific; 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/y074219531-a18dc193-15e1-40a6-8a8b-61963e835ad6">
					  <unstructured_citation>Kumagai M, Ando Y, Tanaka A, Tsuda K. Effects of data bias on machine-learning–based material discovery using experimental property data. Adv Mater Interfaces. 2022.</unstructured_citation>
						 <doi>10.1002/admi.202200944</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-64587a09-fde0-47b2-be3c-8a6ec1d7ded6">
					  <unstructured_citation>Ntoutsi E, Fafalios P, Gadiraju U. Bias in data-driven artificial intelligence systems—an introductory survey. WIREs Data Min Knowl Discov. 2020;10(3):e1356.</unstructured_citation>
						 <doi>10.1002/widm.1356</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-1f595ed5-d40d-4426-b442-5660503aa6e9">
					  <unstructured_citation>Trezza G, Chiavazzo E. Classification-based detection and quantification of cross-domain data bias in materials discovery. J Chem Inf Model. 2024;65(4):1747-61.</unstructured_citation>
						 <doi>10.1021/acs.jcim.4c00012</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-7c77f275-390e-4698-a48d-a6f3eb03bc49">
					  <unstructured_citation>Leavy S, O’Sullivan B, Siapera E. Data, power and bias in artificial intelligence. arXiv. 2020;arXiv:2008.07341.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/y074219531-da51bfad-394b-4491-940a-b64c13a7bdaf">
					  <unstructured_citation>Zhang H, Chen W, Rondinelli J. Mitigating bias in scientific data: A materials science case study. AI for Science Workshop. 2023.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/y074219531-fbb7106d-a7f4-4be4-8272-a3fb89fc19e4">
					  <unstructured_citation>Davariashtiyani A, Wang B, Hajinazar S. Impact of data bias on machine learning for crystal compound synthesizability predictions. Mach Learn Sci Technol. 2024;5(4):045001.</unstructured_citation>
						 <doi>10.1088/2632-2153/ad9378</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-6bcc1154-6b01-426f-a928-c7fc70151a29">
					  <unstructured_citation>Tejani AS, Ng YS, Xi Y, Rayan JC. Understanding and mitigating bias in imaging artificial intelligence. Radiographics. 2024;44(1):e230067.</unstructured_citation>
						 <doi>10.1148/rg.230067</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-14ac6ac4-f56e-4acd-a0d0-a58905726d41">
					  <unstructured_citation>Daneshjou R, Smith MP, Sun MD. Lack of transparency and potential bias in artificial intelligence data sets and algorithms: A scoping review. JAMA Dermatol. 2021;157(11):1362-9.</unstructured_citation>
						 <doi>10.1001/jamadermatol.2021.3129</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-22f39126-7429-457c-a67a-2cbc70b8e5e9">
					  <unstructured_citation>Zhang Z, Li J, Stork DG, Mansfield E. Reducing bias in ai-based analysis of visual artworks. IEEE BITS the International Week. 2022.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/y074219531-729160ef-f7f2-4588-be17-2ce5ad2de63d">
					  <unstructured_citation>Dehkharghanian T, Bidgoli AA, Riasatian A. Biased data, biased ai: Deep networks predict the acquisition site of tcga images. Diagn Pathol. 2023;18(1):86.</unstructured_citation>
						 <doi>10.1186/s13000-023-01355-3</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-5c1c136e-5210-4345-899b-a67daf63ab84">
					  <unstructured_citation>Alvarado R. Ai as an epistemic technology. Sci Eng Ethics. 2023;29(5):1-30.</unstructured_citation>
						 <doi>10.1007/s11948-023-00451-3</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-0e5f2d71-da9d-426b-992e-32c8ea0ff889">
					  <unstructured_citation>Cheung KKC, Long Y, Liu Q, Chan HY. Unpacking epistemic insights of artificial intelligence (ai) in science education: A systematic review. Sci Educ. 2024;34(2):747-77.</unstructured_citation>
						 <doi>10.1007/s11191-024-00511-5</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-45999364-a45d-4915-b23d-96f418d92649">
					  <unstructured_citation>Cruz-Aguilar MA. The epistemic revolution of ai: Reconfiguring the foundations of scientific knowledge. AI Soc. 2025.</unstructured_citation>
						 <doi>10.1007/s00146-025-02658-3</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-29203b04-325b-4004-a2ce-b51f1368ceed">
					  <unstructured_citation>Russo F, Schliesser E, Wagemans J. Connecting ethics and epistemology of ai. AI Soc. 2024;39(2):361-3.</unstructured_citation>
						 <doi>10.1007/s00146-022-01617-6</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-5f2c9553-6f8b-451c-97f6-26bed11e1295">
					  <unstructured_citation>Ngwenyama O, Rowe F. Should we collaborate with ai to conduct literature reviews? Changing epistemic values in a flattening world. J Assoc Inf Sci Technol. 2024;75(1):67-82.</unstructured_citation>
						 <doi>10.1002/asi.24835</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-1db5adf0-d5fe-4288-8077-de7bb05124f5">
					  <unstructured_citation>Berber A, Mijić J. Epistemic education for an ai-driven world. EMERGE 2024 Ethics of AI Alignment—Book of Abstracts. 2024</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/y074219531-a13c2f29-9d15-4dcc-84ec-f653fd794496">
					  <unstructured_citation>Koskinen I. We have no satisfactory social epistemology of ai-based science. Soc Epistemol. 2024;38(4):458-75.</unstructured_citation>
						 <doi>10.1080/02691728.2023.2286253</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-1f07ee2d-9b58-414d-b199-0b3325b4788f">
					  <unstructured_citation>Hauswald R. Artificial epistemic authorities. Soc Epistemol. 2025;39(6):1-10.</unstructured_citation>
						 <doi>10.1080/02691728.2025.2449602</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-17f7b61a-e967-4e24-9d14-1c47b3256379">
					  <unstructured_citation>Lei K, Joress H, Persson N. Aggressively optimizing validation statistics can degrade interpretability of data-driven materials models. J Chem Phys. 2021;155(5):054105.</unstructured_citation>
						 <doi>10.1063/5.0052205</doi> 					</citation>
          					<citation key="rk-10.68159/y074219531-0f75a3b3-d2f7-48b2-985b-ca3d2acdcd6e">
					  <unstructured_citation>Hartung T, Kleinstreuer N. Challenges and opportunities for validation of ai-based new approach methods. ALTEX. 2025</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/y074219531-c278cbe2-b764-450b-bd20-75253ddd0049">
					  <unstructured_citation>Myllyaho L, Raatikainen M, Männistö T. Systematic literature review of validation methods for ai systems. arXiv. 2021;arXiv:2107.12190</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/y074219531-8aa2bf74-1d67-4a8b-8c3a-edb3ec9e885a">
					  <unstructured_citation>Ebrahimian S, Kalra MK, Agarwal S, Bizzo BC. Fda-regulated ai algorithms: Trends, strengths, and gaps of validation studies. Acad Radiol. 2022;29(6):805-11.</unstructured_citation>
						 <doi>10.1016/j.acra.2021.09.018</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
