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		<title>Adapting language models for fuel property prediction</title>
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		<pubDate>Sun, 18 Oct 2026 06:27:46 +0000</pubDate>
				<category><![CDATA[Chemistry]]></category>
		<category><![CDATA[Adapting]]></category>
		<category><![CDATA[AI chemistry]]></category>
		<category><![CDATA[ENGINEERING Energy]]></category>
		<category><![CDATA[fuel property prediction]]></category>
		<category><![CDATA[In-Context Learning]]></category>
		<category><![CDATA[instruction tuning]]></category>
		<category><![CDATA[language]]></category>
		<category><![CDATA[Large Language Models]]></category>
		<category><![CDATA[QSPR]]></category>
		<category><![CDATA[SMILES]]></category>
		<category><![CDATA[sustainable fuels]]></category>
		<category><![CDATA[Tsinghua University]]></category>
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					<description><![CDATA[Tsinghua University researchers developed FuelProp-LM, a language model framework that predicts ten fuel properties from SMILES strings using instruction tuning and dynamic in-context learning.]]></description>
		
		
		
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