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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Training a Foundation Model for Materials on a Budget · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2508.16067/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2508.16067/feed.xml" rel="self" type="application/rss+xml" /><item><title>Training a Foundation Model for Materials on a Budget — mentioned by teddykoker</title><link>https://news.ycombinator.com/item?id=46705776</link><guid isPermaLink="false">urn:hackerpapers:mention:2508.16067:46705776</guid><pubDate>Wed, 21 Jan 2026 13:57:11 GMT</pubDate><description>&lt;p&gt;OpenEquivariance [1] is another good baseline for with kernels for the Clebsch-Gordon tensor product and convolution, and it is fully open source. Both kernel implementations have been successfully implemented into existing machine learning interatomic potentials, e.g. [2,3]. [1] https://github.com/PASSIONLab/OpenEquivariance [2] https://arxiv.org/abs/2504.16068 [3] https://arxiv.org/abs/2508.16067&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2508.16067/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>physics.comp-ph</category></item></channel></rss>
