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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1712.03641/</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/1712.03641/feed.xml" rel="self" type="application/rss+xml" /><item><title>DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics — mentioned by chazeon</title><link>https://news.ycombinator.com/item?id=33206863</link><guid isPermaLink="false">urn:hackerpapers:mention:1712.03641:33206863</guid><pubDate>Fri, 14 Oct 2022 18:08:03 GMT</pubDate><description>&lt;p&gt;See this one, for example, you could look at the method description section: Modeling liquid water by climbing up Jacob&amp;#x27;s ladder in density functional theory facilitated by using deep neural network potentials https://arxiv.org/abs/2104.14410 The software that are used are DeePMD-kit: https://github.com/deepmodeling/deepmd-kit the corresponding paper is https://arxiv.org/pdf/1712.03641.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1712.03641/"&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>
