BranchesTags

Repository files navigation

mdlearn

PyPI versionDocumentation Status

mdlearn is a Python library for analyzing molecular dynamics with machine learning. It contains PyTorch implementations of several deep learning methods such as autoencoders, as well as preprocessing functions which include the kabsch alignment algorithm and higher-order statistical methods like quasi-anharmonic analysis.

Currently supported models:

For more details and specific examples of how to use mdlearn, please see our documentation.

Table of Contents

  1. Installation
  2. Usage
  3. Contributing
  4. Acknowledgments
  5. License

Installation

Install latest version with PyPI

If you have access to an NVIDIA GPU, we highly recommend installing mdlearn into a Conda environment which contains RAPIDS to accelerate t-SNE computations useful for visualizing the model results during training. For the latest RAPIDS version, see here. If you don't have GPU support, mdlearn will still work on CPU by using the scikit-learn implementation.

Run the following commands with updated versions to create a conda environment:

conda create -p conda-env -c rapidsai -c nvidia -c conda-forge cuml=0.19 python=3.7 cudatoolkit=11.2
conda activate conda-env
export IBM_POWERAI_LICENSE_ACCEPT=yes
pip install -U scikit-learn

Then install mdlearn via: pip install mdlearn.

Some systems require PyTorch to be built from source instead of installed via PyPI or Conda, for this reason we made torch an optional dependency. However, it can be installed with mdlearn by running pip install 'mdlearn[torch]' for convenience. Installing this way will also install the wandb package. Please check that torch version >= 1.7.

Usage

Train an autoencoder model with only a few lines of code!

frommdlearn.nn.models.ae.linearimportLinearAETrainer# Initialize autoencoder modeltrainer=LinearAETrainer(
input_dim=40, latent_dim=3, hidden_neurons=[32, 16, 8], epochs=100
)
# Train autoencoder on (N, 40) dimensional datatrainer.fit(X, output_path="./run")
# Generate latent embeddings in inference modez, loss=trainer.predict(X)

Preprocessing

We provide a CLI for collecting common data products from simulations. Currently, we support the following preprocessing methods:

  • Coordinates
  • Contact maps
  • Root mean square deviation (RMSD)

Run the following command for details on how to use the CLI:

mdlearn preprocess --help

Contributing

Please report bugs, enhancement requests, or questions through the Issue Tracker.

If you are looking to contribute, please see CONTRIBUTING.md.

Acknowledgments

License

mdlearn has a MIT license, as seen in the LICENSE file.

About

Machine learning for molecular dynamics

Resources

Readme
MIT license

Code of conduct

Code of conduct

Contributing

Contributing
Activity
Custom properties

Stars

13 stars

Watchers

2 watching

Forks

7 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
mdlearn
PyPI versionDocumentation Status
mdlearn is a Python library for analyzing molecular dynamics with machine learning. It contains PyTorch implementations of several deep learning methods such as autoencoders, as well as preprocessing functions which include the kabsch alignment algorithm and higher-order statistical methods like quasi-anharmonic analysis.
Currently supported models:
For more details and specific examples of how to use mdlearn, please see our documentation.
Table of Contents
  1. Installation
  2. Usage
  3. Contributing
  4. Acknowledgments
  5. License
Installation
Install latest version with PyPI
If you have access to an NVIDIA GPU, we highly recommend installing mdlearn into a Conda environment which contains RAPIDS to accelerate t-SNE computations useful for visualizing the model results during training. For the latest RAPIDS version, see here. If you don't have GPU support, mdlearn will still work on CPU by using the scikit-learn implementation.
Run the following commands with updated versions to create a conda environment:
conda create -p conda-env -c rapidsai -c nvidia -c conda-forge cuml=0.19 python=3.7 cudatoolkit=11.2
conda activate conda-env
export IBM_POWERAI_LICENSE_ACCEPT=yes
pip install -U scikit-learn

Then install mdlearn via: pip install mdlearn.
Some systems require PyTorch to be built from source instead of installed via PyPI or Conda, for this reason we made torch an optional dependency. However, it can be installed with mdlearn by running pip install 'mdlearn[torch]' for convenience. Installing this way will also install the wandb package. Please check that torch version >= 1.7.
Usage
Train an autoencoder model with only a few lines of code!
frommdlearn.nn.models.ae.linearimportLinearAETrainer# Initialize autoencoder modeltrainer=LinearAETrainer(
input_dim=40, latent_dim=3, hidden_neurons=[32, 16, 8], epochs=100
)
# Train autoencoder on (N, 40) dimensional datatrainer.fit(X, output_path="./run")
# Generate latent embeddings in inference modez, loss=trainer.predict(X)
Preprocessing
We provide a CLI for collecting common data products from simulations. Currently, we support the following preprocessing methods:
  • Coordinates
  • Contact maps
  • Root mean square deviation (RMSD)
Run the following command for details on how to use the CLI:
mdlearn preprocess --help
Contributing
Please report bugs, enhancement requests, or questions through the Issue Tracker.
If you are looking to contribute, please see CONTRIBUTING.md.
Acknowledgments
License
mdlearn has a MIT license, as seen in the LICENSE file.
About
Machine learning for molecular dynamics
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
7 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
mdlearn
PyPI versionDocumentation Status
mdlearn is a Python library for analyzing molecular dynamics with machine learning. It contains PyTorch implementations of several deep learning methods such as autoencoders, as well as preprocessing functions which include the kabsch alignment algorithm and higher-order statistical methods like quasi-anharmonic analysis.
Currently supported models:
For more details and specific examples of how to use mdlearn, please see our documentation.
Table of Contents
  1. Installation
  2. Usage
  3. Contributing
  4. Acknowledgments
  5. License
Installation
Install latest version with PyPI
If you have access to an NVIDIA GPU, we highly recommend installing mdlearn into a Conda environment which contains RAPIDS to accelerate t-SNE computations useful for visualizing the model results during training. For the latest RAPIDS version, see here. If you don't have GPU support, mdlearn will still work on CPU by using the scikit-learn implementation.
Run the following commands with updated versions to create a conda environment:
conda create -p conda-env -c rapidsai -c nvidia -c conda-forge cuml=0.19 python=3.7 cudatoolkit=11.2
conda activate conda-env
export IBM_POWERAI_LICENSE_ACCEPT=yes
pip install -U scikit-learn

Then install mdlearn via: pip install mdlearn.
Some systems require PyTorch to be built from source instead of installed via PyPI or Conda, for this reason we made torch an optional dependency. However, it can be installed with mdlearn by running pip install 'mdlearn[torch]' for convenience. Installing this way will also install the wandb package. Please check that torch version >= 1.7.
Usage
Train an autoencoder model with only a few lines of code!
frommdlearn.nn.models.ae.linearimportLinearAETrainer# Initialize autoencoder modeltrainer=LinearAETrainer(
input_dim=40, latent_dim=3, hidden_neurons=[32, 16, 8], epochs=100
)
# Train autoencoder on (N, 40) dimensional datatrainer.fit(X, output_path="./run")
# Generate latent embeddings in inference modez, loss=trainer.predict(X)
Preprocessing
We provide a CLI for collecting common data products from simulations. Currently, we support the following preprocessing methods:
  • Coordinates
  • Contact maps
  • Root mean square deviation (RMSD)
Run the following command for details on how to use the CLI:
mdlearn preprocess --help
Contributing
Please report bugs, enhancement requests, or questions through the Issue Tracker.
If you are looking to contribute, please see CONTRIBUTING.md.
Acknowledgments
License
mdlearn has a MIT license, as seen in the LICENSE file.
About
Machine learning for molecular dynamics
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
7 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
mdlearn
PyPI versionDocumentation Status
mdlearn is a Python library for analyzing molecular dynamics with machine learning. It contains PyTorch implementations of several deep learning methods such as autoencoders, as well as preprocessing functions which include the kabsch alignment algorithm and higher-order statistical methods like quasi-anharmonic analysis.
Currently supported models:
For more details and specific examples of how to use mdlearn, please see our documentation.
Table of Contents
  1. Installation
  2. Usage
  3. Contributing
  4. Acknowledgments
  5. License
Installation
Install latest version with PyPI
If you have access to an NVIDIA GPU, we highly recommend installing mdlearn into a Conda environment which contains RAPIDS to accelerate t-SNE computations useful for visualizing the model results during training. For the latest RAPIDS version, see here. If you don't have GPU support, mdlearn will still work on CPU by using the scikit-learn implementation.
Run the following commands with updated versions to create a conda environment:
conda create -p conda-env -c rapidsai -c nvidia -c conda-forge cuml=0.19 python=3.7 cudatoolkit=11.2
conda activate conda-env
export IBM_POWERAI_LICENSE_ACCEPT=yes
pip install -U scikit-learn

Then install mdlearn via: pip install mdlearn.
Some systems require PyTorch to be built from source instead of installed via PyPI or Conda, for this reason we made torch an optional dependency. However, it can be installed with mdlearn by running pip install 'mdlearn[torch]' for convenience. Installing this way will also install the wandb package. Please check that torch version >= 1.7.
Usage
Train an autoencoder model with only a few lines of code!
frommdlearn.nn.models.ae.linearimportLinearAETrainer# Initialize autoencoder modeltrainer=LinearAETrainer(
input_dim=40, latent_dim=3, hidden_neurons=[32, 16, 8], epochs=100
)
# Train autoencoder on (N, 40) dimensional datatrainer.fit(X, output_path="./run")
# Generate latent embeddings in inference modez, loss=trainer.predict(X)
Preprocessing
We provide a CLI for collecting common data products from simulations. Currently, we support the following preprocessing methods:
  • Coordinates
  • Contact maps
  • Root mean square deviation (RMSD)
Run the following command for details on how to use the CLI:
mdlearn preprocess --help
Contributing
Please report bugs, enhancement requests, or questions through the Issue Tracker.
If you are looking to contribute, please see CONTRIBUTING.md.
Acknowledgments
License
mdlearn has a MIT license, as seen in the LICENSE file.
About
Machine learning for molecular dynamics
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
7 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
mdlearn
PyPI versionDocumentation Status
mdlearn is a Python library for analyzing molecular dynamics with machine learning. It contains PyTorch implementations of several deep learning methods such as autoencoders, as well as preprocessing functions which include the kabsch alignment algorithm and higher-order statistical methods like quasi-anharmonic analysis.
Currently supported models:
For more details and specific examples of how to use mdlearn, please see our documentation.
Table of Contents
  1. Installation
  2. Usage
  3. Contributing
  4. Acknowledgments
  5. License
Installation
Install latest version with PyPI
If you have access to an NVIDIA GPU, we highly recommend installing mdlearn into a Conda environment which contains RAPIDS to accelerate t-SNE computations useful for visualizing the model results during training. For the latest RAPIDS version, see here. If you don't have GPU support, mdlearn will still work on CPU by using the scikit-learn implementation.
Run the following commands with updated versions to create a conda environment:
conda create -p conda-env -c rapidsai -c nvidia -c conda-forge cuml=0.19 python=3.7 cudatoolkit=11.2
conda activate conda-env
export IBM_POWERAI_LICENSE_ACCEPT=yes
pip install -U scikit-learn

Then install mdlearn via: pip install mdlearn.
Some systems require PyTorch to be built from source instead of installed via PyPI or Conda, for this reason we made torch an optional dependency. However, it can be installed with mdlearn by running pip install 'mdlearn[torch]' for convenience. Installing this way will also install the wandb package. Please check that torch version >= 1.7.
Usage
Train an autoencoder model with only a few lines of code!
frommdlearn.nn.models.ae.linearimportLinearAETrainer# Initialize autoencoder modeltrainer=LinearAETrainer(
input_dim=40, latent_dim=3, hidden_neurons=[32, 16, 8], epochs=100
)
# Train autoencoder on (N, 40) dimensional datatrainer.fit(X, output_path="./run")
# Generate latent embeddings in inference modez, loss=trainer.predict(X)
Preprocessing
We provide a CLI for collecting common data products from simulations. Currently, we support the following preprocessing methods:
  • Coordinates
  • Contact maps
  • Root mean square deviation (RMSD)
Run the following command for details on how to use the CLI:
mdlearn preprocess --help
Contributing
Please report bugs, enhancement requests, or questions through the Issue Tracker.
If you are looking to contribute, please see CONTRIBUTING.md.
Acknowledgments
License
mdlearn has a MIT license, as seen in the LICENSE file.
About
Machine learning for molecular dynamics
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
7 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
mdlearn
PyPI versionDocumentation Status
mdlearn is a Python library for analyzing molecular dynamics with machine learning. It contains PyTorch implementations of several deep learning methods such as autoencoders, as well as preprocessing functions which include the kabsch alignment algorithm and higher-order statistical methods like quasi-anharmonic analysis.
Currently supported models:
For more details and specific examples of how to use mdlearn, please see our documentation.
Table of Contents
  1. Installation
  2. Usage
  3. Contributing
  4. Acknowledgments
  5. License
Installation
Install latest version with PyPI
If you have access to an NVIDIA GPU, we highly recommend installing mdlearn into a Conda environment which contains RAPIDS to accelerate t-SNE computations useful for visualizing the model results during training. For the latest RAPIDS version, see here. If you don't have GPU support, mdlearn will still work on CPU by using the scikit-learn implementation.
Run the following commands with updated versions to create a conda environment:
conda create -p conda-env -c rapidsai -c nvidia -c conda-forge cuml=0.19 python=3.7 cudatoolkit=11.2
conda activate conda-env
export IBM_POWERAI_LICENSE_ACCEPT=yes
pip install -U scikit-learn

Then install mdlearn via: pip install mdlearn.
Some systems require PyTorch to be built from source instead of installed via PyPI or Conda, for this reason we made torch an optional dependency. However, it can be installed with mdlearn by running pip install 'mdlearn[torch]' for convenience. Installing this way will also install the wandb package. Please check that torch version >= 1.7.
Usage
Train an autoencoder model with only a few lines of code!
frommdlearn.nn.models.ae.linearimportLinearAETrainer# Initialize autoencoder modeltrainer=LinearAETrainer(
input_dim=40, latent_dim=3, hidden_neurons=[32, 16, 8], epochs=100
)
# Train autoencoder on (N, 40) dimensional datatrainer.fit(X, output_path="./run")
# Generate latent embeddings in inference modez, loss=trainer.predict(X)
Preprocessing
We provide a CLI for collecting common data products from simulations. Currently, we support the following preprocessing methods:
  • Coordinates
  • Contact maps
  • Root mean square deviation (RMSD)
Run the following command for details on how to use the CLI:
mdlearn preprocess --help
Contributing
Please report bugs, enhancement requests, or questions through the Issue Tracker.
If you are looking to contribute, please see CONTRIBUTING.md.
Acknowledgments
License
mdlearn has a MIT license, as seen in the LICENSE file.
About
Machine learning for molecular dynamics
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
7 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
mdlearn
PyPI versionDocumentation Status
mdlearn is a Python library for analyzing molecular dynamics with machine learning. It contains PyTorch implementations of several deep learning methods such as autoencoders, as well as preprocessing functions which include the kabsch alignment algorithm and higher-order statistical methods like quasi-anharmonic analysis.
Currently supported models:
For more details and specific examples of how to use mdlearn, please see our documentation.
Table of Contents
  1. Installation
  2. Usage
  3. Contributing
  4. Acknowledgments
  5. License
Installation
Install latest version with PyPI
If you have access to an NVIDIA GPU, we highly recommend installing mdlearn into a Conda environment which contains RAPIDS to accelerate t-SNE computations useful for visualizing the model results during training. For the latest RAPIDS version, see here. If you don't have GPU support, mdlearn will still work on CPU by using the scikit-learn implementation.
Run the following commands with updated versions to create a conda environment:
conda create -p conda-env -c rapidsai -c nvidia -c conda-forge cuml=0.19 python=3.7 cudatoolkit=11.2
conda activate conda-env
export IBM_POWERAI_LICENSE_ACCEPT=yes
pip install -U scikit-learn

Then install mdlearn via: pip install mdlearn.
Some systems require PyTorch to be built from source instead of installed via PyPI or Conda, for this reason we made torch an optional dependency. However, it can be installed with mdlearn by running pip install 'mdlearn[torch]' for convenience. Installing this way will also install the wandb package. Please check that torch version >= 1.7.
Usage
Train an autoencoder model with only a few lines of code!
frommdlearn.nn.models.ae.linearimportLinearAETrainer# Initialize autoencoder modeltrainer=LinearAETrainer(
input_dim=40, latent_dim=3, hidden_neurons=[32, 16, 8], epochs=100
)
# Train autoencoder on (N, 40) dimensional datatrainer.fit(X, output_path="./run")
# Generate latent embeddings in inference modez, loss=trainer.predict(X)
Preprocessing
We provide a CLI for collecting common data products from simulations. Currently, we support the following preprocessing methods:
  • Coordinates
  • Contact maps
  • Root mean square deviation (RMSD)
Run the following command for details on how to use the CLI:
mdlearn preprocess --help
Contributing
Please report bugs, enhancement requests, or questions through the Issue Tracker.
If you are looking to contribute, please see CONTRIBUTING.md.
Acknowledgments
License
mdlearn has a MIT license, as seen in the LICENSE file.
About
Machine learning for molecular dynamics
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
7 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
mdlearn
PyPI versionDocumentation Status
mdlearn is a Python library for analyzing molecular dynamics with machine learning. It contains PyTorch implementations of several deep learning methods such as autoencoders, as well as preprocessing functions which include the kabsch alignment algorithm and higher-order statistical methods like quasi-anharmonic analysis.
Currently supported models:
For more details and specific examples of how to use mdlearn, please see our documentation.
Table of Contents
  1. Installation
  2. Usage
  3. Contributing
  4. Acknowledgments
  5. License
Installation
Install latest version with PyPI
If you have access to an NVIDIA GPU, we highly recommend installing mdlearn into a Conda environment which contains RAPIDS to accelerate t-SNE computations useful for visualizing the model results during training. For the latest RAPIDS version, see here. If you don't have GPU support, mdlearn will still work on CPU by using the scikit-learn implementation.
Run the following commands with updated versions to create a conda environment:
conda create -p conda-env -c rapidsai -c nvidia -c conda-forge cuml=0.19 python=3.7 cudatoolkit=11.2
conda activate conda-env
export IBM_POWERAI_LICENSE_ACCEPT=yes
pip install -U scikit-learn

Then install mdlearn via: pip install mdlearn.
Some systems require PyTorch to be built from source instead of installed via PyPI or Conda, for this reason we made torch an optional dependency. However, it can be installed with mdlearn by running pip install 'mdlearn[torch]' for convenience. Installing this way will also install the wandb package. Please check that torch version >= 1.7.
Usage
Train an autoencoder model with only a few lines of code!
frommdlearn.nn.models.ae.linearimportLinearAETrainer# Initialize autoencoder modeltrainer=LinearAETrainer(
input_dim=40, latent_dim=3, hidden_neurons=[32, 16, 8], epochs=100
)
# Train autoencoder on (N, 40) dimensional datatrainer.fit(X, output_path="./run")
# Generate latent embeddings in inference modez, loss=trainer.predict(X)
Preprocessing
We provide a CLI for collecting common data products from simulations. Currently, we support the following preprocessing methods:
  • Coordinates
  • Contact maps
  • Root mean square deviation (RMSD)
Run the following command for details on how to use the CLI:
mdlearn preprocess --help
Contributing
Please report bugs, enhancement requests, or questions through the Issue Tracker.
If you are looking to contribute, please see CONTRIBUTING.md.
Acknowledgments
License
mdlearn has a MIT license, as seen in the LICENSE file.
About
Machine learning for molecular dynamics
Resources
Readme
MIT license
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
7 forks
Report repository
Releases
Packages
Used by
Contributors
Languages