<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0">
  <channel>
    <title>PyPI recent updates for BUCToolkit</title>
    <link>https://pypi.org/project/buctoolkit/</link>
    <description>Recent updates to the Python Package Index for BUCToolkit</description>
    <language>en</language>    <item>
      <title>1.0b3</title>
      <link>https://pypi.org/project/buctoolkit/1.0b3/</link>
      <description>BUCToolkit is a PyTorch-based high-performance AI4Science software package of computational chemistry,</description>
<author>buctoolkit@163.com</author>      <pubDate>Sat, 25 Apr 2026 16:11:42 GMT</pubDate>
    </item>    <item>
      <title>1.0b2</title>
      <link>https://pypi.org/project/buctoolkit/1.0b2/</link>
      <description>Batch-upscaled Catalysis Toolkit (BUCToolkit) is an ai4science software package of computational chemistry, which can apply PyTorch-based deep-learning models (of molecular or crystal potentials) to perform training, predictions, batched structure optimization, batched molecular dynamics with/without constraints, and batched Monte Carlo simulations. Various tools for handling catalyst structure files are also included.</description>
      <pubDate>Wed, 15 Apr 2026 10:28:05 GMT</pubDate>
    </item>    <item>
      <title>1.0b1</title>
      <link>https://pypi.org/project/buctoolkit/1.0b1/</link>
      <description>Batch-upscaled Catalysis Toolkit (BUCToolkit) is an ai4science software package of computational chemistry, which can apply PyTorch-based deep-learning models (of molecular or crystal potentials) to perform training, predictions, batched structure optimization, batched molecular dynamics with/without constraints, and batched Monte Carlo simulations. Various tools for handling catalyst structure files are also included.</description>
      <pubDate>Tue, 14 Apr 2026 17:20:22 GMT</pubDate>
    </item>    <item>
      <title>1.0b0</title>
      <link>https://pypi.org/project/buctoolkit/1.0b0/</link>
      <description>Batch-upscaled Catalysis Toolkit (BUCToolkit), which can apply PyTorch-based deep-learning model to perform training, predictions, batched structure optimization, batched molecular dynamics with/without constraints, and batched Monte Carlo simulations. Various tools for handling catalyst structure files are also included.</description>
      <pubDate>Tue, 14 Apr 2026 06:22:49 GMT</pubDate>
    </item>  </channel>
</rss>