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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine learning modeling of superconducting critical temperature · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1709.02727/</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/1709.02727/feed.xml" rel="self" type="application/rss+xml" /><item><title>Machine learning modeling of superconducting critical temperature — mentioned by plaidfuji</title><link>https://news.ycombinator.com/item?id=16896662</link><guid isPermaLink="false">urn:hackerpapers:mention:1709.02727:16896662</guid><pubDate>Sun, 22 Apr 2018 15:49:26 GMT</pubDate><description>&lt;p&gt;definitely: https://arxiv.org/abs/1709.02727 I work kind of adjacent to two of the supervisors on this project at NIST, and the workflow for these types of projects usually goes: (1) build predictive model of chemical composition -&amp;gt; superconductivity from past experiments or databases of simulations (2) Build and program automated sample prep (this is actually the hardest part, not the machine learning) (3) Build and program automated structural characterization and superconductivity measurement The difficulty is finding a system whose design space can be explored with and measured with…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1709.02727/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cond-mat.supr-con</category></item></channel></rss>
