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		<title>Digital twin from a single micrograph reveals invisible stress and chemistry around dislocations</title>
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		<pubDate>Mon, 12 Oct 2026 15:01:23 +0000</pubDate>
				<category><![CDATA[Chemistry]]></category>
		<category><![CDATA[atomistic simulation]]></category>
		<category><![CDATA[digital twin]]></category>
		<category><![CDATA[dislocations]]></category>
		<category><![CDATA[dual-phase alloys]]></category>
		<category><![CDATA[electron microscopy]]></category>
		<category><![CDATA[grain boundaries]]></category>
		<category><![CDATA[hydrogen embrittlement]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[National Science Review]]></category>
		<category><![CDATA[plastic deformation]]></category>
		<category><![CDATA[stress fields]]></category>
		<category><![CDATA[Zhejiang University]]></category>
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					<description><![CDATA[A machine learning framework called AtomDiT converts a single atomic-resolution electron micrograph into a digital twin that reveals dislocation type, stress fields, and hydrogen distribution in metals.]]></description>
		
		
		
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