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		<title>Silicon-swapped selenide sheets emerge as fast-charging battery anodes in simulations</title>
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		<pubDate>Mon, 05 Oct 2026 10:56:33 +0000</pubDate>
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		<category><![CDATA[alkali-ion batteries]]></category>
		<category><![CDATA[anode materials]]></category>
		<category><![CDATA[density functional theory]]></category>
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		<category><![CDATA[ion diffusion]]></category>
		<category><![CDATA[lithium-ion batteries]]></category>
		<category><![CDATA[MXenes]]></category>
		<category><![CDATA[potassium-ion batteries]]></category>
		<category><![CDATA[silicon substitution]]></category>
		<category><![CDATA[Sodium-ion batteries]]></category>
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					<description><![CDATA[A new density functional theory study identifies silicon-substituted titanium, zirconium, and hafnium selenide monolayers as stable, metallic, fast-diffusing anode candidates for lithium, sodium, and potassium-ion batteries.]]></description>
		
		
		
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		<title>Machine Learning Cracks the Vast Code of High-Entropy Catalysts</title>
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		<pubDate>Mon, 05 Oct 2026 10:21:45 +0000</pubDate>
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		<category><![CDATA[adsorption energy]]></category>
		<category><![CDATA[Catalysis]]></category>
		<category><![CDATA[density functional theory]]></category>
		<category><![CDATA[electrocatalysis]]></category>
		<category><![CDATA[graph neural networks]]></category>
		<category><![CDATA[High-entropy alloys]]></category>
		<category><![CDATA[Hydrogen evolution reaction]]></category>
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		<category><![CDATA[machine learning interatomic potentials]]></category>
		<category><![CDATA[surface segregation]]></category>
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					<description><![CDATA[A new review in the Journal of Materials Science maps how machine learning, from graph neural networks to large language models, is accelerating the design of high-entropy alloy catalysts across vast compositional spaces.]]></description>
		
		
		
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