Skip to content
BranchesTags

Repository files navigation

Test StatusDocumentation StatusPyPI versionDownloadsDOI

 ______ _ _ _ _______ _______ (_____ \ \ \ / / | | (_______|_______)
_____) ) _ \ \/ / |_ ____| | _____ _____ | ____/ | | | ) (| _)/ _ | | | ___) | ___) | | | |_| |/ /\ \ |_( ( | | |_______| | | | |_| \__ /_/ \_\___)_||_|_(_______)_| |_| (____/

A Python package for Machine learning of interatomic force field. PyXtal FF is an open-source Python library for developing machine learning interatomic potential of materials.

The aim of PyXtal_FF is to promote the application of atomistic simulations by providing several choices of structural descriptors and machine learning regressions in one platform. Based on the given choice of structural descriptors including

  • atom-centered symmetry functions
  • embedded atom density
  • SNAP
  • SO4 bispectrum
  • SO3 power spectrum

PyXtal_FF can train the MLPs with either the linear regression or neural networks model, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from the ab-initio simulation.

See the documentation page for more background materials.

One can also quickly checkout the example section to see how to train and apply the force fields for productive simulations.

This is an ongoing project.

Relevant works

[1]. Yanxon H, Zagaceta D, Tang B, Matteson D, Zhu Q* (2020)
PyXtal_FF: a Python Library for Automated Force Field Generation

[2]. Zagaceta D, Yanxon H, Zhu Q* (2020)
Spectral Neural Network Potentials for Binary Alloys

[3]. Yanxon H, Zagaceta D, Wood B, Zhu Q* (2019)
On Transferability of Machine Learning Force Fields: A Case Study on Silicon

[4]. Fredericks S, Sayre D, Zhu Q *(2019)
PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis

About

Machine Learning Interatomic Potential Predictions

Resources

Readme
Activity
Custom properties

Stars

93 stars

Watchers

7 watching

Forks

23 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
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;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
GitHub - MaterSim/PyXtal_FF: Machine Learning Interatomic Potential Predictions · GitHub
Skip to content
BranchesTags
Repository files navigation
Test StatusDocumentation StatusPyPI versionDownloadsDOI
 ______ _ _ _ _______ _______ (_____ \ \ \ / / | | (_______|_______)
_____) ) _ \ \/ / |_ ____| | _____ _____ | ____/ | | | ) (| _)/ _ | | | ___) | ___) | | | |_| |/ /\ \ |_( ( | | |_______| | | | |_| \__ /_/ \_\___)_||_|_(_______)_| |_| (____/ 
A Python package for Machine learning of interatomic force field.
PyXtal FF is an open-source Python library for developing machine learning interatomic potential of materials.
The aim of PyXtal_FF is to promote the application of atomistic simulations by providing several choices of structural descriptors and machine learning regressions in one platform. Based on the given choice of structural descriptors including
  • atom-centered symmetry functions
  • embedded atom density
  • SNAP
  • SO4 bispectrum
  • SO3 power spectrum
PyXtal_FF can train the MLPs with either the linear regression or neural networks model, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from the ab-initio simulation.
See the documentation page for more background materials.
One can also quickly checkout the example section to see how to train and apply the force fields for productive simulations.
This is an ongoing project.
Relevant works
[1]. Yanxon H, Zagaceta D, Tang B, Matteson D, Zhu Q* (2020)
PyXtal_FF: a Python Library for Automated Force Field Generation
[2]. Zagaceta D, Yanxon H, Zhu Q* (2020) 
Spectral Neural Network Potentials for Binary Alloys
[3]. Yanxon H, Zagaceta D, Wood B, Zhu Q* (2019) 
On Transferability of Machine Learning Force Fields: A Case Study on Silicon
[4]. Fredericks S, Sayre D, Zhu Q *(2019) 
PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis
About
Machine Learning Interatomic Potential Predictions
Resources
Readme
Activity
Custom properties
Stars
93 stars
Watchers
7 watching
Forks
23 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - MaterSim/PyXtal_FF: Machine Learning Interatomic Potential Predictions · GitHub
Skip to content
BranchesTags
Repository files navigation
Test StatusDocumentation StatusPyPI versionDownloadsDOI
 ______ _ _ _ _______ _______ (_____ \ \ \ / / | | (_______|_______)
_____) ) _ \ \/ / |_ ____| | _____ _____ | ____/ | | | ) (| _)/ _ | | | ___) | ___) | | | |_| |/ /\ \ |_( ( | | |_______| | | | |_| \__ /_/ \_\___)_||_|_(_______)_| |_| (____/ 
A Python package for Machine learning of interatomic force field.
PyXtal FF is an open-source Python library for developing machine learning interatomic potential of materials.
The aim of PyXtal_FF is to promote the application of atomistic simulations by providing several choices of structural descriptors and machine learning regressions in one platform. Based on the given choice of structural descriptors including
  • atom-centered symmetry functions
  • embedded atom density
  • SNAP
  • SO4 bispectrum
  • SO3 power spectrum
PyXtal_FF can train the MLPs with either the linear regression or neural networks model, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from the ab-initio simulation.
See the documentation page for more background materials.
One can also quickly checkout the example section to see how to train and apply the force fields for productive simulations.
This is an ongoing project.
Relevant works
[1]. Yanxon H, Zagaceta D, Tang B, Matteson D, Zhu Q* (2020)
PyXtal_FF: a Python Library for Automated Force Field Generation
[2]. Zagaceta D, Yanxon H, Zhu Q* (2020) 
Spectral Neural Network Potentials for Binary Alloys
[3]. Yanxon H, Zagaceta D, Wood B, Zhu Q* (2019) 
On Transferability of Machine Learning Force Fields: A Case Study on Silicon
[4]. Fredericks S, Sayre D, Zhu Q *(2019) 
PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis
About
Machine Learning Interatomic Potential Predictions
Resources
Readme
Activity
Custom properties
Stars
93 stars
Watchers
7 watching
Forks
23 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - MaterSim/PyXtal_FF: Machine Learning Interatomic Potential Predictions · GitHub
Skip to content
BranchesTags
Repository files navigation
Test StatusDocumentation StatusPyPI versionDownloadsDOI
 ______ _ _ _ _______ _______ (_____ \ \ \ / / | | (_______|_______)
_____) ) _ \ \/ / |_ ____| | _____ _____ | ____/ | | | ) (| _)/ _ | | | ___) | ___) | | | |_| |/ /\ \ |_( ( | | |_______| | | | |_| \__ /_/ \_\___)_||_|_(_______)_| |_| (____/ 
A Python package for Machine learning of interatomic force field.
PyXtal FF is an open-source Python library for developing machine learning interatomic potential of materials.
The aim of PyXtal_FF is to promote the application of atomistic simulations by providing several choices of structural descriptors and machine learning regressions in one platform. Based on the given choice of structural descriptors including
  • atom-centered symmetry functions
  • embedded atom density
  • SNAP
  • SO4 bispectrum
  • SO3 power spectrum
PyXtal_FF can train the MLPs with either the linear regression or neural networks model, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from the ab-initio simulation.
See the documentation page for more background materials.
One can also quickly checkout the example section to see how to train and apply the force fields for productive simulations.
This is an ongoing project.
Relevant works
[1]. Yanxon H, Zagaceta D, Tang B, Matteson D, Zhu Q* (2020)
PyXtal_FF: a Python Library for Automated Force Field Generation
[2]. Zagaceta D, Yanxon H, Zhu Q* (2020) 
Spectral Neural Network Potentials for Binary Alloys
[3]. Yanxon H, Zagaceta D, Wood B, Zhu Q* (2019) 
On Transferability of Machine Learning Force Fields: A Case Study on Silicon
[4]. Fredericks S, Sayre D, Zhu Q *(2019) 
PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis
About
Machine Learning Interatomic Potential Predictions
Resources
Readme
Activity
Custom properties
Stars
93 stars
Watchers
7 watching
Forks
23 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - MaterSim/PyXtal_FF: Machine Learning Interatomic Potential Predictions · GitHub
Skip to content
BranchesTags
Repository files navigation
Test StatusDocumentation StatusPyPI versionDownloadsDOI
 ______ _ _ _ _______ _______ (_____ \ \ \ / / | | (_______|_______)
_____) ) _ \ \/ / |_ ____| | _____ _____ | ____/ | | | ) (| _)/ _ | | | ___) | ___) | | | |_| |/ /\ \ |_( ( | | |_______| | | | |_| \__ /_/ \_\___)_||_|_(_______)_| |_| (____/ 
A Python package for Machine learning of interatomic force field.
PyXtal FF is an open-source Python library for developing machine learning interatomic potential of materials.
The aim of PyXtal_FF is to promote the application of atomistic simulations by providing several choices of structural descriptors and machine learning regressions in one platform. Based on the given choice of structural descriptors including
  • atom-centered symmetry functions
  • embedded atom density
  • SNAP
  • SO4 bispectrum
  • SO3 power spectrum
PyXtal_FF can train the MLPs with either the linear regression or neural networks model, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from the ab-initio simulation.
See the documentation page for more background materials.
One can also quickly checkout the example section to see how to train and apply the force fields for productive simulations.
This is an ongoing project.
Relevant works
[1]. Yanxon H, Zagaceta D, Tang B, Matteson D, Zhu Q* (2020)
PyXtal_FF: a Python Library for Automated Force Field Generation
[2]. Zagaceta D, Yanxon H, Zhu Q* (2020) 
Spectral Neural Network Potentials for Binary Alloys
[3]. Yanxon H, Zagaceta D, Wood B, Zhu Q* (2019) 
On Transferability of Machine Learning Force Fields: A Case Study on Silicon
[4]. Fredericks S, Sayre D, Zhu Q *(2019) 
PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis
About
Machine Learning Interatomic Potential Predictions
Resources
Readme
Activity
Custom properties
Stars
93 stars
Watchers
7 watching
Forks
23 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - MaterSim/PyXtal_FF: Machine Learning Interatomic Potential Predictions · GitHub
Skip to content
BranchesTags
Repository files navigation
Test StatusDocumentation StatusPyPI versionDownloadsDOI
 ______ _ _ _ _______ _______ (_____ \ \ \ / / | | (_______|_______)
_____) ) _ \ \/ / |_ ____| | _____ _____ | ____/ | | | ) (| _)/ _ | | | ___) | ___) | | | |_| |/ /\ \ |_( ( | | |_______| | | | |_| \__ /_/ \_\___)_||_|_(_______)_| |_| (____/ 
A Python package for Machine learning of interatomic force field.
PyXtal FF is an open-source Python library for developing machine learning interatomic potential of materials.
The aim of PyXtal_FF is to promote the application of atomistic simulations by providing several choices of structural descriptors and machine learning regressions in one platform. Based on the given choice of structural descriptors including
  • atom-centered symmetry functions
  • embedded atom density
  • SNAP
  • SO4 bispectrum
  • SO3 power spectrum
PyXtal_FF can train the MLPs with either the linear regression or neural networks model, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from the ab-initio simulation.
See the documentation page for more background materials.
One can also quickly checkout the example section to see how to train and apply the force fields for productive simulations.
This is an ongoing project.
Relevant works
[1]. Yanxon H, Zagaceta D, Tang B, Matteson D, Zhu Q* (2020)
PyXtal_FF: a Python Library for Automated Force Field Generation
[2]. Zagaceta D, Yanxon H, Zhu Q* (2020) 
Spectral Neural Network Potentials for Binary Alloys
[3]. Yanxon H, Zagaceta D, Wood B, Zhu Q* (2019) 
On Transferability of Machine Learning Force Fields: A Case Study on Silicon
[4]. Fredericks S, Sayre D, Zhu Q *(2019) 
PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis
About
Machine Learning Interatomic Potential Predictions
Resources
Readme
Activity
Custom properties
Stars
93 stars
Watchers
7 watching
Forks
23 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - MaterSim/PyXtal_FF: Machine Learning Interatomic Potential Predictions · GitHub
Skip to content
BranchesTags
Repository files navigation
Test StatusDocumentation StatusPyPI versionDownloadsDOI
 ______ _ _ _ _______ _______ (_____ \ \ \ / / | | (_______|_______)
_____) ) _ \ \/ / |_ ____| | _____ _____ | ____/ | | | ) (| _)/ _ | | | ___) | ___) | | | |_| |/ /\ \ |_( ( | | |_______| | | | |_| \__ /_/ \_\___)_||_|_(_______)_| |_| (____/ 
A Python package for Machine learning of interatomic force field.
PyXtal FF is an open-source Python library for developing machine learning interatomic potential of materials.
The aim of PyXtal_FF is to promote the application of atomistic simulations by providing several choices of structural descriptors and machine learning regressions in one platform. Based on the given choice of structural descriptors including
  • atom-centered symmetry functions
  • embedded atom density
  • SNAP
  • SO4 bispectrum
  • SO3 power spectrum
PyXtal_FF can train the MLPs with either the linear regression or neural networks model, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from the ab-initio simulation.
See the documentation page for more background materials.
One can also quickly checkout the example section to see how to train and apply the force fields for productive simulations.
This is an ongoing project.
Relevant works
[1]. Yanxon H, Zagaceta D, Tang B, Matteson D, Zhu Q* (2020)
PyXtal_FF: a Python Library for Automated Force Field Generation
[2]. Zagaceta D, Yanxon H, Zhu Q* (2020) 
Spectral Neural Network Potentials for Binary Alloys
[3]. Yanxon H, Zagaceta D, Wood B, Zhu Q* (2019) 
On Transferability of Machine Learning Force Fields: A Case Study on Silicon
[4]. Fredericks S, Sayre D, Zhu Q *(2019) 
PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis
About
Machine Learning Interatomic Potential Predictions
Resources
Readme
Activity
Custom properties
Stars
93 stars
Watchers
7 watching
Forks
23 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - MaterSim/PyXtal_FF: Machine Learning Interatomic Potential Predictions · GitHub
Skip to content
BranchesTags
Repository files navigation
Test StatusDocumentation StatusPyPI versionDownloadsDOI
 ______ _ _ _ _______ _______ (_____ \ \ \ / / | | (_______|_______)
_____) ) _ \ \/ / |_ ____| | _____ _____ | ____/ | | | ) (| _)/ _ | | | ___) | ___) | | | |_| |/ /\ \ |_( ( | | |_______| | | | |_| \__ /_/ \_\___)_||_|_(_______)_| |_| (____/ 
A Python package for Machine learning of interatomic force field.
PyXtal FF is an open-source Python library for developing machine learning interatomic potential of materials.
The aim of PyXtal_FF is to promote the application of atomistic simulations by providing several choices of structural descriptors and machine learning regressions in one platform. Based on the given choice of structural descriptors including
  • atom-centered symmetry functions
  • embedded atom density
  • SNAP
  • SO4 bispectrum
  • SO3 power spectrum
PyXtal_FF can train the MLPs with either the linear regression or neural networks model, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data from the ab-initio simulation.
See the documentation page for more background materials.
One can also quickly checkout the example section to see how to train and apply the force fields for productive simulations.
This is an ongoing project.
Relevant works
[1]. Yanxon H, Zagaceta D, Tang B, Matteson D, Zhu Q* (2020)
PyXtal_FF: a Python Library for Automated Force Field Generation
[2]. Zagaceta D, Yanxon H, Zhu Q* (2020) 
Spectral Neural Network Potentials for Binary Alloys
[3]. Yanxon H, Zagaceta D, Wood B, Zhu Q* (2019) 
On Transferability of Machine Learning Force Fields: A Case Study on Silicon
[4]. Fredericks S, Sayre D, Zhu Q *(2019) 
PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis
About
Machine Learning Interatomic Potential Predictions
Resources
Readme
Activity
Custom properties
Stars
93 stars
Watchers
7 watching
Forks
23 forks
Report repository
Releases
Packages
Used by
Contributors
Languages