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<details><summary><b><ahref="https://github.com/deepmodeling/deepmd-kit">DeePMD-kit</a></b> (🥇29 · ⭐ 1.5K) - A deep learning package for many-body potential energy representation and molecular dynamics. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
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<details><summary><b><ahref="https://github.com/deepmodeling/abacus-develop">ABACUS</a></b> (🥇26 · ⭐ 180) - An electronic structure package based on either plane wave basis or numerical atomic orbitals. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
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<details><summary><b><ahref="https://github.com/deepmodeling/dpgen">DP-GEN</a></b> (🥇24 · ⭐ 320) - The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
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<details><summary><b><ahref="https://github.com/deepmodeling/dpdata">dpdata</a></b> (🥇23 · ⭐ 200) - A Python package for manipulating atomistic data of software in computational science. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
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<details><summary><b><ahref="https://github.com/deepmodeling/dpdispatcher">dpdispatcher</a></b> (🥈22 · ⭐ 46) - generate HPC scheduler systems jobs input scripts and submit these scripts to HPC systems and poke until they finish. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
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conda install -c conda-forge reacnetgenerator
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<details><summary><b><ahref="https://github.com/deepmodeling/dargs">dargs</a></b> (🥈20 · ⭐ 5) - Argument processing for the deep modeling project. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
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<details><summary><b><ahref="https://github.com/deepmodeling/dargs">dargs</a></b> (🥈19 · ⭐ 5 · 📉) - Argument processing for the deep modeling project. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
<details><summary><b><ahref="https://github.com/deepmodeling/DeePTB">DeePTB</a></b> (🥈15 · ⭐ 58) - DeePTB: A deep learning package for tight-binding approach with ab initio accuracy. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
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<details><summary><b><ahref="https://github.com/deepmodeling/DeePTB">DeePTB</a></b> (🥈15 · ⭐ 59) - DeePTB: A deep learning package for tight-binding approach with ab initio accuracy. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
<details><summary><b><ahref="https://github.com/deepmodeling/APEX">APEX</a></b> (🥈15 · ⭐ 25 · 📉) - APEX: Alloy Properties EXplorer using simulations. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
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<details><summary><b><ahref="https://github.com/deepmodeling/dpgui">DP-GUI</a></b> (🥈15 · ⭐ 7) - A web-based GUI for user inputs. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
<details><summary><b><ahref="https://github.com/deepmodeling/dpgui">DP-GUI</a></b> (🥈15 · ⭐ 7) - A web-based GUI for user inputs. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
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<details><summary><b><ahref="https://github.com/deepmodeling/jax-fem">JAX-FEM</a></b> (🥈14 · ⭐ 320 · 📉) - Differentiable Finite Element Method with JAX. <code><ahref="http://bit.ly/2M0xdwT">GPL-3.0</a></code></summary>
<details><summary><b><ahref="https://github.com/deepmodeling/DMFF">DMFF</a></b> (🥈14 · ⭐ 160 · 💤) - DMFF (Differentiable Molecular Force Field) is a Jax-based python package that provides a full differentiable.. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
<details><summary><b><ahref="https://github.com/deepmodeling/docs">docs</a></b> (🥉6 · ⭐ 3) - The home page of DeepModeling documentation. <code><ahref="http://bit.ly/37RvQcA">LGPL-3.0</a></code></summary>
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