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    <title>PyPI recent updates for matbench-discovery</title>
    <link>https://pypi.org/project/matbench-discovery/</link>
    <description>Recent updates to the Python Package Index for matbench-discovery</description>
    <language>en</language>    <item>
      <title>1.3.1</title>
      <link>https://pypi.org/project/matbench-discovery/1.3.1/</link>
      <description>A benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures</description>
<author>janosh.riebesell@gmail.com</author>      <pubDate>Wed, 11 Sep 2024 19:00:12 GMT</pubDate>
    </item>    <item>
      <title>1.3.0</title>
      <link>https://pypi.org/project/matbench-discovery/1.3.0/</link>
      <description>A benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures</description>
<author>janosh.riebesell@gmail.com</author>      <pubDate>Fri, 06 Sep 2024 19:59:21 GMT</pubDate>
    </item>    <item>
      <title>1.2.0</title>
      <link>https://pypi.org/project/matbench-discovery/1.2.0/</link>
      <description>A benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures</description>
<author>janosh.riebesell@gmail.com</author>      <pubDate>Mon, 15 Jul 2024 20:08:07 GMT</pubDate>
    </item>    <item>
      <title>1.1.2</title>
      <link>https://pypi.org/project/matbench-discovery/1.1.2/</link>
      <description>A benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures</description>
<author>janosh.riebesell@gmail.com</author>      <pubDate>Thu, 30 May 2024 18:23:23 GMT</pubDate>
    </item>    <item>
      <title>1.1.1</title>
      <link>https://pypi.org/project/matbench-discovery/1.1.1/</link>
      <description>A benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures</description>
<author>janosh.riebesell@gmail.com</author>      <pubDate>Sun, 28 Jan 2024 14:19:40 GMT</pubDate>
    </item>    <item>
      <title>1.1.0</title>
      <link>https://pypi.org/project/matbench-discovery/1.1.0/</link>
      <description>A benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures</description>
<author>janosh.riebesell@gmail.com</author>      <pubDate>Thu, 25 Jan 2024 12:46:06 GMT</pubDate>
    </item>    <item>
      <title>1.0.0</title>
      <link>https://pypi.org/project/matbench-discovery/1.0.0/</link>
      <description>A benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures</description>
<author>janosh@lbl.gov</author>      <pubDate>Thu, 14 Sep 2023 07:06:24 GMT</pubDate>
    </item>    <item>
      <title>0.1.2</title>
      <link>https://pypi.org/project/matbench-discovery/0.1.2/</link>
      <description>A benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures</description>
<author>janosh@lbl.gov</author>      <pubDate>Tue, 21 Feb 2023 21:55:52 GMT</pubDate>
    </item>    <item>
      <title>0.1.1</title>
      <link>https://pypi.org/project/matbench-discovery/0.1.1/</link>
      <description>A benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures</description>
<author>janosh@lbl.gov</author>      <pubDate>Tue, 21 Feb 2023 21:42:56 GMT</pubDate>
    </item>    <item>
      <title>0.1.0</title>
      <link>https://pypi.org/project/matbench-discovery/0.1.0/</link>
      <description>A machine learning benchmark that simulates high-throughput screening for new materials and ranks energy models by their ability to increase the hit rate of stable crystals</description>
<author>janosh@lbl.gov</author>      <pubDate>Fri, 10 Feb 2023 03:37:46 GMT</pubDate>
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