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		<title>Physics Meets Machine Learning in New Framework for Trustworthy Materials Discovery</title>
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				<category><![CDATA[Chemistry]]></category>
		<category><![CDATA[Advanced Functional Materials]]></category>
		<category><![CDATA[Artificial Intelligence]]></category>
		<category><![CDATA[Catalysis]]></category>
		<category><![CDATA[Hydrogen storage]]></category>
		<category><![CDATA[interpretable models]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[materials discovery]]></category>
		<category><![CDATA[physics-informed AI]]></category>
		<category><![CDATA[Solid-state batteries]]></category>
		<category><![CDATA[solid-state electrolytes]]></category>
		<category><![CDATA[thermodynamics]]></category>
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					<description><![CDATA[Tohoku University researchers propose a framework called Physics-Grounded Materials AI that embeds thermodynamic, kinetic, and electronic-structure principles into machine learning to make materials discovery more interpretable, testable, and reliable.]]></description>
		
		
		
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