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Chi Chen
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Update docs
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‎api-docs-source/index.rst‎

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The SNAP potential comes with this lammps installation. The GAP package for GAP and MLIP package for MTP are needed to run the corresponding potentials. For fitting NNP potential, the `n2p2` package is needed.
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Install all the libraries from requirement.txt file::
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pip install -r requirements.txt
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(If doesn't works provide the path of requirement.txt file)
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For all the requirements above::
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pip install -r requirements-ci.txt
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pip install -r requirements-optional.txt
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pip install -r requirements-tf.txt
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pip install -r requirements.txt
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(If doesn't works provide the path of requirements.txt file)
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Usage
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-----
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Many Jupyter notebooks are available on usage. See `notebooks </notebooks>`_.
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Many Jupyter notebooks are available on usage. See `notebooks </notebooks>`_. We also have established a tool and a tutorial lecture at nanoHUB `https://nanohub.org/resources/maml <https://nanohub.org/resources/maml>`_.
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API documentation
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-----------------
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See `API docs <https://guide.materialsvirtuallab.org/maml/modules.html>`_.
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Machine learning (ML) is the study of computer algorithms that improve automatically through experience.[1][2] It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.[3] Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
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Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning.[5][6] In its application across business problems, machine learning is also referred to as predictive analytics.
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Citing
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------
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::
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@misc{maml,
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author = {Chen, Chi and Zuo, Yunxing and Ye, Weike and Ong, Shyue Ping},
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title = {{Maml - materials machine learning package}},
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year = {2020},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/materialsvirtuallab/maml}},
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}
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‎docs/_sources/index.rst.txt‎

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@@ -53,12 +53,43 @@ To run the potential energy surface (pes), lammps installation is required you c
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The SNAP potential comes with this lammps installation. The GAP package for GAP and MLIP package for MTP are needed to run the corresponding potentials. For fitting NNP potential, the `n2p2` package is needed.
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Install all the libraries from requirement.txt file::
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pip install -r requirements.txt
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(If doesn't works provide the path of requirement.txt file)
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For all the requirements above::
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pip install -r requirements-ci.txt
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pip install -r requirements-optional.txt
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pip install -r requirements-tf.txt
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pip install -r requirements.txt
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(If doesn't works provide the path of requirements.txt file)
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Usage
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-----
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Many Jupyter notebooks are available on usage. See `notebooks </notebooks>`_.
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Many Jupyter notebooks are available on usage. See `notebooks </notebooks>`_. We also have established a tool and a tutorial lecture at nanoHUB `https://nanohub.org/resources/maml <https://nanohub.org/resources/maml>`_.
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API documentation
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-----------------
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See `API docs <https://guide.materialsvirtuallab.org/maml/modules.html>`_.
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Machine learning (ML) is the study of computer algorithms that improve automatically through experience.[1][2] It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.[3] Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
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Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning.[5][6] In its application across business problems, machine learning is also referred to as predictive analytics.
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Citing
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------
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::
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@misc{maml,
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author = {Chen, Chi and Zuo, Yunxing and Ye, Weike and Ong, Shyue Ping},
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title = {{Maml - materials machine learning package}},
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year = {2020},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/materialsvirtuallab/maml}},
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}
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‎docs/_static/basic.css‎

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*
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* Sphinx stylesheet -- basic theme.
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*
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* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
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* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
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* :license: BSD, see LICENSE for details.
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*
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*/
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}
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table.highlighttable td.linenos,
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span.linenos,
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div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */
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user-select: none;
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}

‎docs/_static/doctools.js‎

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*
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* Sphinx JavaScript utilities for all documentation.
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*
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* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
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* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
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* :license: BSD, see LICENSE for details.
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*
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*/
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initOnKeyListeners: function() {
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$(document).keydown(function(event) {
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var activeElementType = document.activeElement.tagName;
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// don't navigate when in search box or textarea
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// don't navigate when in search box, textarea, dropdown or button
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if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
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&& !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
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&& activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
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&& !event.shiftKey) {
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switch (event.keyCode) {
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case 37: // left
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var prevHref = $('link[rel="prev"]').prop('href');

‎docs/_static/language_data.js‎

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* This script contains the language-specific data used by searchtools.js,
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* namely the list of stopwords, stemmer, scorer and splitter.
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*
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* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
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* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
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* :license: BSD, see LICENSE for details.
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*
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*/

‎docs/_static/searchtools.js‎

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*
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* Sphinx JavaScript utilities for the full-text search.
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*
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* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
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* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
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* :license: BSD, see LICENSE for details.
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*
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*/
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_pulse_status : -1,
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htmlToText : function(htmlString) {
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var htmlElement = document.createElement('span');
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htmlElement.innerHTML = htmlString;
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$(htmlElement).find('.headerlink').remove();
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docContent = $(htmlElement).find('[role=main]')[0];
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var virtualDocument = document.implementation.createHTMLDocument('virtual');
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var htmlElement = $(htmlString, virtualDocument);
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htmlElement.find('.headerlink').remove();
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docContent = htmlElement.find('[role=main]')[0];
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if(docContent === undefined) {
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console.warn("Content block not found. Sphinx search tries to obtain it " +
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"via '[role=main]'. Could you check your theme or template.");

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