<?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-uSqK-1788861347-j072121119</doi_batch_id>
		<timestamp>1788861347</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>1</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Causal Reasoning in Materials Informatics: A Theory-First Roadmap Beyond Correlation</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Claire</given_name>
            <surname>Martin</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Julien</given_name>
            <surname>Robert</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Sophie</given_name>
            <surname>Bernard</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2024</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/j072121119</doi>
					<resource>https://iamrp.net/pub/journal/1/article/j072121119</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/j072121119-6460e22e-79e9-4e14-b6c3-508286dfca40">
					  <unstructured_citation>Pearl J. Theoretical impediments to machine learning with seven sparks from the causal revolution. arXiv. 2021.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-3381a196-f52a-463e-be32-97da24cdf4f5">
					  <unstructured_citation>Peters J, Janzing D, Schölkopf B. Elements of causal inference: foundations and learning algorithms. 2nd ed. Cambridge (MA): MIT Press; 2021.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-993d188e-bc3f-4bd5-9f12-411f679cc81b">
					  <unstructured_citation>Schölkopf B, Locatello F, Bauer S, Ke N, Kalchbrenner N, Goyal A, et al. Toward causal representation learning. Proc IEEE. 2021;109(5):612–34.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-d15b7f56-e8fe-47a4-8fae-630b2011a45c">
					  <unstructured_citation>Pearl J. Causal inference in statistics: An overview (updated perspective). arXiv. 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-39896886-d021-45f3-9d91-6b28b722aee5">
					  <unstructured_citation>Hernán MA, Robins JM. Causal Inference: What If. Boca Raton (FL): Chapman &amp; Hall/CRC; 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-15433135-f507-430f-8ba6-9bfe7d05e080">
					  <unstructured_citation>Imbens GW. Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics. J Econ Lit. 2020;58(4):1129–79.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-caab0d90-1177-4414-9ebb-badfcca9db3c">
					  <unstructured_citation>Bareinboim E, Pearl J. Causal inference and the data-fusion problem. Proc Natl Acad Sci U S A. 2021;118(20):e2001636118.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-6ced6732-13b2-4503-98c0-2d70b76e95e7">
					  <unstructured_citation>Kuang K, Li S, Zhang L, Gao J, Zhou T, Zhuang Z, et al. Stable prediction across unknown environments. Nat Mach Intell. 2021;3:703–11.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-2d767144-f046-4bc8-9e66-754a27ef759e">
					  <unstructured_citation>Arjovsky M, Bottou L, Gulrajani I, Lopez-Paz D. Invariant risk minimization. arXiv. 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-b25f5089-8b4d-45d6-a32c-6c9b3d5a08fd">
					  <unstructured_citation>Lin Y, Jin X, Cai H, Li S, Li R. Benchmarking and validation of explainable artificial intelligence methods in materials science. Patterns (N Y). 2022;3(6):100514.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-a29264bd-635f-443c-b44e-fd0eeb18639a">
					  <unstructured_citation>Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A. Machine learning for molecular and materials science. Nature. 2020;559:547–55.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-225995c3-2d4a-469a-9fcd-6dccc19fc69c">
					  <unstructured_citation>Schmidt J, Marques MRG, Botti S, Marques MAL. Recent advances and applications of machine learning in solid-state materials science. NPJ Comput Mater. 2020;5:83.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-28ec4ce0-8078-46be-9522-7aaef95334a6">
					  <unstructured_citation>Merchant AM, Blaiszik B, et al. Data-driven materials science: Status and challenges. MRS Bull. 2021;46:1022–30.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-6aa6f191-9d35-4c75-b771-789fda24605a">
					  <unstructured_citation>Ward L, Wolverton C. Atomistic calculations and materials informatics: A review. Curr Opin Solid State Mater Sci. 2020;24(3):100803.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-e9054822-a241-4ef4-a653-55816a889f60">
					  <unstructured_citation>Lookman T, Balachandran PV, Xue D, Yuan R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. NPJ Comput Mater. 2020;5:21.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-dc21e2bd-1bd6-41f4-b4c2-501217285b46">
					  <unstructured_citation>Himanen L, Geurts A, Foster AS, Rinke P. Data-driven materials science: Status, challenges, and perspectives. Adv Sci. 2020;6(21):1900808.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-b9f5ac64-3e10-47ec-9042-85382d8cdabd">
					  <unstructured_citation>Bareinboim E, Tian J, Pearl J. Recovering from selection bias in causal and statistical inference. AAAI. 2022;36(6):5601–8.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-8c008c9b-bcad-48bc-ba8f-776991d16e50">
					  <unstructured_citation>von Kügelgen J, Gresele L, Schölkopf B. Simpson’s paradox in machine learning. arXiv. 2021.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-46144963-c6db-4958-92f7-19c1c40ff2c8">
					  <unstructured_citation>Goyal A, Schölkopf B, Bengio Y. The inductive biases for representation learning in physical systems. arXiv. 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-7078c57a-9388-4e74-81fd-2083b3dbc52a">
					  <unstructured_citation>Sagawa S, Koh PW, Hashimoto TB, Liang P. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. ICLR. 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-d0d0cce3-5f6c-40cb-9f3c-0f815c97cce5">
					  <unstructured_citation>Nassar M, et al. Robust machine learning in materials science: A perspective. Chem Mater. 2022;34.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-ecbb43c1-2048-499d-aef6-199bfeb38635">
					  <unstructured_citation>Lepri S, et al. Explainability and causality in AI: A systematic perspective. Nat Mach Intell. 2021;3:93–100.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-ead245b1-cf2b-4d47-8da6-3bac4c56ad83">
					  <unstructured_citation>Ghorbani A, Abid A, Zou J. Interpretation of neural networks is fragile. Proc Natl Acad Sci U S A. 2020;117(40):25076–82.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-ecdffca6-7709-45e0-ac6d-e9318a61b434">
					  <unstructured_citation>Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv. 2020.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-96cdf1f6-fc7f-433b-b390-118aa77d6d12">
					  <unstructured_citation>Glymour C, Zhang K, Spirtes P. Review of causal discovery methods based on graphical models. Front Genet. 2020;11:1–15.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-f3b008ba-d85c-450c-8f70-f4b01ff9082a">
					  <unstructured_citation>Mooij JM, Peters J, Janzing D, Zscheischler J, Schölkopf B. Distinguishing cause from effect using observational data: methods and benchmarks. J Mach Learn Res. 2020;17:1–102.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-31be662c-54ef-4ec3-b1c5-40f08de439f2">
					  <unstructured_citation>Vowels MJ, Camgoz NC, Bowditch P. D’you know what I mean? A survey of causal discovery and inference. arXiv. 2021.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-80af035d-0e1f-4c78-a967-f363bef86b92">
					  <unstructured_citation>Krueger D, Caballero E, Jacobsen JH, Zhang A, Binas J, Zhang D, et al. Out-of-distribution generalization via risk extrapolation (REx). ICML. 2021.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-3b8e654e-1726-4174-8be5-ae9a89ee02ca">
					  <unstructured_citation>Zhou T, et al. Causal mechanism transfer in materials modeling. NPJ Comput Mater. 2023;9.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-8951e1e9-5625-495b-b49d-e4b59ede62e5">
					  <unstructured_citation>Zhang J, Bareinboim E. Transportability and data fusion in causal inference. Ann Rev Stat Appl. 2021;8.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-a7e79b34-57ae-45ea-ac3e-5cb7af1d0065">
					  <unstructured_citation>Jain A, Ong SP, Hautier G, Chen W, Richards WD, Dacek S, et al. The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 2021;9:070701.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-c186e8ce-1bb8-4e87-a6c2-fe96c69288fd">
					  <unstructured_citation>Jha D, Ward L, Paul A, Liao W, Choudhary A, Agrawal A, et al. ElemNet: Deep learning the chemistry of materials from only elemental composition. Sci Rep. 2020;10:1–13.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-18e76c4d-6ca9-439f-adcc-559561252dab">
					  <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;31(9):3564–72.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-db75ba96-4831-4d1e-acc0-4b3bc6a14dd7">
					  <unstructured_citation>Xie T, Grossman JC. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys Rev Lett. 2020;120:145301.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/j072121119-8badbc19-0eb6-4bf6-b791-d492f787cb9d">
					  <unstructured_citation>Maheshwari C, et al. Causal machine learning for scientific discovery: Opportunities and challenges. Nat Rev Phys. 2023;5.</unstructured_citation>
											</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
