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knc6/README.md

Hi there 👋

Kamal Choudhary is

My research interests are focused on atomistic materials design using classical, quantum, and machine learning methods. In particular, I have developed JARVIS database and tools (https://jarvis.nist.gov/) that hosts publicly available datasets for millions of material properties.

Contributions to projects:

NameDescriptionDetailsConda PackagePyPi Package
atomgptlab/chatgpt_material_explorerChatGPT based Material Science Assistant for materials data and simulations.📚
atomgptlab/jarvis-toolsJARVIS-Tools: An open-source software package for data-driven atomistic materials design📚📦📦
atomgptlab/alignnALIGNN: Atomistic Line Graph Neural Network and force-field📚📦📦
atomgptlab/jarvis_leaderboardJARVIS-Leaderboard: Explore State-of-the-Art Materials Design Methods and Reproducible Benchmarks📚📦📦
atomgptlab/atomgptAtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design📚
atomgptlab/chemnlpChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data📚📦📦
atomgptlab/atomvisionAtomVision: Deep learning framework for atomistic image data📚📦
atomgptlab/atomqcAtomQC: Atomistic Calculations on Quantum Computers📚📦
JARVIS-Materials-Design/jarvis-tools-notebooksA Google-Colab Notebook Collection for Materials Design📚
deepmaterials/dlmatreviewRepository for links to software packages and databases used in deep-learning applications for materials science📚
deepmaterials/slmatServerLess Materials Design Toolkit📚
atomgptlab/tb3pyTB3Py: Two- and three-body tight-binding calculations for materials📚📦📦
atomgptlab/intermatInterMat: Interface materials design toolkit📚📦📦
atomgptlab/defectmatDefectMat: Defect materials design toolkit📚
atomgptlab/chipsffEvaluation of universal machine learning force-fields📚📦
atomgptlab/benchqcA Benchmarking Toolkit for Quantum Computation📚
atomgptlab/catalysismatExamining Generalizability of AI Models for Catalysis📚
SciVedantaA collection of YouTube videos on Vedanta philosophy📚
eesocietyEncouraging Excellence Society📚

Google Scholar Pages:

Project / AuthorGoogle Scholar Page
Kamal Choudharyscholar
JARVISscholar
ALIGNNscholar
SuperconDBscholar
AtomGPTscholar
DMS200scholar
DeepMaterials LLCscholar

Here are some links that might interest you:

knc6

Popular repositories Loading

  1. material_explorer_examples material_explorer_examplesPublic archive

    ChatGPT Material Explorer 1.0 Example Prompts

    11

  2. JARVIS-FF JARVIS-FFPublic

    This project contains the data for evaluation of interatomic potentials/force-fields (used in Moecular-dynamics and Monte-carlo simulations). LAMMPS calculation were done using MPinterface code (ht…

    Python 10 1

  3. jarvis-tools-notebooksold jarvis-tools-notebooksoldPublic

    Forked from JARVIS-Materials-Design/jarvis-tools-notebooks

    Jupyter Notebook 5 2

  4. knc6 knc6Public

    4

  5. alignn alignnPublic

    Forked from usnistgov/alignn

    Atomistic Line Graph Neural Network

    Python 1 1

  6. chemnlp-1 chemnlp-1Public

    Forked from kellml/chemnlp

    ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data

    Python 1

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knc6/README.md
Hi there 👋
Kamal Choudhary is
My research interests are focused on atomistic materials design using classical, quantum, and machine learning methods. In particular, I have developed JARVIS database and tools (https://jarvis.nist.gov/) that hosts publicly available datasets for millions of material properties.
Contributions to projects:
NameDescriptionDetailsConda PackagePyPi Package
atomgptlab/chatgpt_material_explorerChatGPT based Material Science Assistant for materials data and simulations.📚
atomgptlab/jarvis-toolsJARVIS-Tools: An open-source software package for data-driven atomistic materials design📚📦📦
atomgptlab/alignnALIGNN: Atomistic Line Graph Neural Network and force-field📚📦📦
atomgptlab/jarvis_leaderboardJARVIS-Leaderboard: Explore State-of-the-Art Materials Design Methods and Reproducible Benchmarks📚📦📦
atomgptlab/atomgptAtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design📚
atomgptlab/chemnlpChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data📚📦📦
atomgptlab/atomvisionAtomVision: Deep learning framework for atomistic image data📚📦
atomgptlab/atomqcAtomQC: Atomistic Calculations on Quantum Computers📚📦
JARVIS-Materials-Design/jarvis-tools-notebooksA Google-Colab Notebook Collection for Materials Design📚
deepmaterials/dlmatreviewRepository for links to software packages and databases used in deep-learning applications for materials science📚
deepmaterials/slmatServerLess Materials Design Toolkit📚
atomgptlab/tb3pyTB3Py: Two- and three-body tight-binding calculations for materials📚📦📦
atomgptlab/intermatInterMat: Interface materials design toolkit📚📦📦
atomgptlab/defectmatDefectMat: Defect materials design toolkit📚
atomgptlab/chipsffEvaluation of universal machine learning force-fields📚📦
atomgptlab/benchqcA Benchmarking Toolkit for Quantum Computation📚
atomgptlab/catalysismatExamining Generalizability of AI Models for Catalysis📚
SciVedantaA collection of YouTube videos on Vedanta philosophy📚
eesocietyEncouraging Excellence Society📚
Google Scholar Pages:
Project / AuthorGoogle Scholar Page
Kamal Choudharyscholar
JARVISscholar
ALIGNNscholar
SuperconDBscholar
AtomGPTscholar
DMS200scholar
DeepMaterials LLCscholar
Here are some links that might interest you:
knc6

Popular repositories
Loading
  1. 
material_explorer_examples
material_explorer_examplesPublic archive
    
ChatGPT Material Explorer 1.0 Example Prompts

    
11

  2. 
JARVIS-FF
JARVIS-FFPublic
    
This project contains the data for evaluation of interatomic potentials/force-fields (used in Moecular-dynamics and Monte-carlo simulations). LAMMPS calculation were done using MPinterface code (ht…

    Python
10

1

  3. 
jarvis-tools-notebooksold
jarvis-tools-notebooksoldPublic
    
Forked from JARVIS-Materials-Design/jarvis-tools-notebooks
    Jupyter Notebook
5

2

  4. 
knc6
knc6Public
    
4

  5. 
alignn
alignnPublic
    
Forked from usnistgov/alignn
    
Atomistic Line Graph Neural Network

    Python
1

1

  6. 
chemnlp-1
chemnlp-1Public
    
Forked from kellml/chemnlp
    
ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data

    Python
1


, 'i');
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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knc6/README.md
Hi there 👋
Kamal Choudhary is
My research interests are focused on atomistic materials design using classical, quantum, and machine learning methods. In particular, I have developed JARVIS database and tools (https://jarvis.nist.gov/) that hosts publicly available datasets for millions of material properties.
Contributions to projects:
NameDescriptionDetailsConda PackagePyPi Package
atomgptlab/chatgpt_material_explorerChatGPT based Material Science Assistant for materials data and simulations.📚
atomgptlab/jarvis-toolsJARVIS-Tools: An open-source software package for data-driven atomistic materials design📚📦📦
atomgptlab/alignnALIGNN: Atomistic Line Graph Neural Network and force-field📚📦📦
atomgptlab/jarvis_leaderboardJARVIS-Leaderboard: Explore State-of-the-Art Materials Design Methods and Reproducible Benchmarks📚📦📦
atomgptlab/atomgptAtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design📚
atomgptlab/chemnlpChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data📚📦📦
atomgptlab/atomvisionAtomVision: Deep learning framework for atomistic image data📚📦
atomgptlab/atomqcAtomQC: Atomistic Calculations on Quantum Computers📚📦
JARVIS-Materials-Design/jarvis-tools-notebooksA Google-Colab Notebook Collection for Materials Design📚
deepmaterials/dlmatreviewRepository for links to software packages and databases used in deep-learning applications for materials science📚
deepmaterials/slmatServerLess Materials Design Toolkit📚
atomgptlab/tb3pyTB3Py: Two- and three-body tight-binding calculations for materials📚📦📦
atomgptlab/intermatInterMat: Interface materials design toolkit📚📦📦
atomgptlab/defectmatDefectMat: Defect materials design toolkit📚
atomgptlab/chipsffEvaluation of universal machine learning force-fields📚📦
atomgptlab/benchqcA Benchmarking Toolkit for Quantum Computation📚
atomgptlab/catalysismatExamining Generalizability of AI Models for Catalysis📚
SciVedantaA collection of YouTube videos on Vedanta philosophy📚
eesocietyEncouraging Excellence Society📚
Google Scholar Pages:
Project / AuthorGoogle Scholar Page
Kamal Choudharyscholar
JARVISscholar
ALIGNNscholar
SuperconDBscholar
AtomGPTscholar
DMS200scholar
DeepMaterials LLCscholar
Here are some links that might interest you:
knc6

Popular repositories
Loading
  1. 
material_explorer_examples
material_explorer_examplesPublic archive
    
ChatGPT Material Explorer 1.0 Example Prompts

    
11

  2. 
JARVIS-FF
JARVIS-FFPublic
    
This project contains the data for evaluation of interatomic potentials/force-fields (used in Moecular-dynamics and Monte-carlo simulations). LAMMPS calculation were done using MPinterface code (ht…

    Python
10

1

  3. 
jarvis-tools-notebooksold
jarvis-tools-notebooksoldPublic
    
Forked from JARVIS-Materials-Design/jarvis-tools-notebooks
    Jupyter Notebook
5

2

  4. 
knc6
knc6Public
    
4

  5. 
alignn
alignnPublic
    
Forked from usnistgov/alignn
    
Atomistic Line Graph Neural Network

    Python
1

1

  6. 
chemnlp-1
chemnlp-1Public
    
Forked from kellml/chemnlp
    
ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data

    Python
1


, '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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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knc6/README.md
Hi there 👋
Kamal Choudhary is
My research interests are focused on atomistic materials design using classical, quantum, and machine learning methods. In particular, I have developed JARVIS database and tools (https://jarvis.nist.gov/) that hosts publicly available datasets for millions of material properties.
Contributions to projects:
NameDescriptionDetailsConda PackagePyPi Package
atomgptlab/chatgpt_material_explorerChatGPT based Material Science Assistant for materials data and simulations.📚
atomgptlab/jarvis-toolsJARVIS-Tools: An open-source software package for data-driven atomistic materials design📚📦📦
atomgptlab/alignnALIGNN: Atomistic Line Graph Neural Network and force-field📚📦📦
atomgptlab/jarvis_leaderboardJARVIS-Leaderboard: Explore State-of-the-Art Materials Design Methods and Reproducible Benchmarks📚📦📦
atomgptlab/atomgptAtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design📚
atomgptlab/chemnlpChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data📚📦📦
atomgptlab/atomvisionAtomVision: Deep learning framework for atomistic image data📚📦
atomgptlab/atomqcAtomQC: Atomistic Calculations on Quantum Computers📚📦
JARVIS-Materials-Design/jarvis-tools-notebooksA Google-Colab Notebook Collection for Materials Design📚
deepmaterials/dlmatreviewRepository for links to software packages and databases used in deep-learning applications for materials science📚
deepmaterials/slmatServerLess Materials Design Toolkit📚
atomgptlab/tb3pyTB3Py: Two- and three-body tight-binding calculations for materials📚📦📦
atomgptlab/intermatInterMat: Interface materials design toolkit📚📦📦
atomgptlab/defectmatDefectMat: Defect materials design toolkit📚
atomgptlab/chipsffEvaluation of universal machine learning force-fields📚📦
atomgptlab/benchqcA Benchmarking Toolkit for Quantum Computation📚
atomgptlab/catalysismatExamining Generalizability of AI Models for Catalysis📚
SciVedantaA collection of YouTube videos on Vedanta philosophy📚
eesocietyEncouraging Excellence Society📚
Google Scholar Pages:
Project / AuthorGoogle Scholar Page
Kamal Choudharyscholar
JARVISscholar
ALIGNNscholar
SuperconDBscholar
AtomGPTscholar
DMS200scholar
DeepMaterials LLCscholar
Here are some links that might interest you:
knc6

Popular repositories
Loading
  1. 
material_explorer_examples
material_explorer_examplesPublic archive
    
ChatGPT Material Explorer 1.0 Example Prompts

    
11

  2. 
JARVIS-FF
JARVIS-FFPublic
    
This project contains the data for evaluation of interatomic potentials/force-fields (used in Moecular-dynamics and Monte-carlo simulations). LAMMPS calculation were done using MPinterface code (ht…

    Python
10

1

  3. 
jarvis-tools-notebooksold
jarvis-tools-notebooksoldPublic
    
Forked from JARVIS-Materials-Design/jarvis-tools-notebooks
    Jupyter Notebook
5

2

  4. 
knc6
knc6Public
    
4

  5. 
alignn
alignnPublic
    
Forked from usnistgov/alignn
    
Atomistic Line Graph Neural Network

    Python
1

1

  6. 
chemnlp-1
chemnlp-1Public
    
Forked from kellml/chemnlp
    
ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data

    Python
1


, '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");
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Achievements
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knc6/README.md
Hi there 👋
Kamal Choudhary is
My research interests are focused on atomistic materials design using classical, quantum, and machine learning methods. In particular, I have developed JARVIS database and tools (https://jarvis.nist.gov/) that hosts publicly available datasets for millions of material properties.
Contributions to projects:
NameDescriptionDetailsConda PackagePyPi Package
atomgptlab/chatgpt_material_explorerChatGPT based Material Science Assistant for materials data and simulations.📚
atomgptlab/jarvis-toolsJARVIS-Tools: An open-source software package for data-driven atomistic materials design📚📦📦
atomgptlab/alignnALIGNN: Atomistic Line Graph Neural Network and force-field📚📦📦
atomgptlab/jarvis_leaderboardJARVIS-Leaderboard: Explore State-of-the-Art Materials Design Methods and Reproducible Benchmarks📚📦📦
atomgptlab/atomgptAtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design📚
atomgptlab/chemnlpChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data📚📦📦
atomgptlab/atomvisionAtomVision: Deep learning framework for atomistic image data📚📦
atomgptlab/atomqcAtomQC: Atomistic Calculations on Quantum Computers📚📦
JARVIS-Materials-Design/jarvis-tools-notebooksA Google-Colab Notebook Collection for Materials Design📚
deepmaterials/dlmatreviewRepository for links to software packages and databases used in deep-learning applications for materials science📚
deepmaterials/slmatServerLess Materials Design Toolkit📚
atomgptlab/tb3pyTB3Py: Two- and three-body tight-binding calculations for materials📚📦📦
atomgptlab/intermatInterMat: Interface materials design toolkit📚📦📦
atomgptlab/defectmatDefectMat: Defect materials design toolkit📚
atomgptlab/chipsffEvaluation of universal machine learning force-fields📚📦
atomgptlab/benchqcA Benchmarking Toolkit for Quantum Computation📚
atomgptlab/catalysismatExamining Generalizability of AI Models for Catalysis📚
SciVedantaA collection of YouTube videos on Vedanta philosophy📚
eesocietyEncouraging Excellence Society📚
Google Scholar Pages:
Project / AuthorGoogle Scholar Page
Kamal Choudharyscholar
JARVISscholar
ALIGNNscholar
SuperconDBscholar
AtomGPTscholar
DMS200scholar
DeepMaterials LLCscholar
Here are some links that might interest you:
knc6

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ChatGPT Material Explorer 1.0 Example Prompts

    
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chemnlp-1
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ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data

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, '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");
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knc6/README.md
Hi there 👋
Kamal Choudhary is
My research interests are focused on atomistic materials design using classical, quantum, and machine learning methods. In particular, I have developed JARVIS database and tools (https://jarvis.nist.gov/) that hosts publicly available datasets for millions of material properties.
Contributions to projects:
NameDescriptionDetailsConda PackagePyPi Package
atomgptlab/chatgpt_material_explorerChatGPT based Material Science Assistant for materials data and simulations.📚
atomgptlab/jarvis-toolsJARVIS-Tools: An open-source software package for data-driven atomistic materials design📚📦📦
atomgptlab/alignnALIGNN: Atomistic Line Graph Neural Network and force-field📚📦📦
atomgptlab/jarvis_leaderboardJARVIS-Leaderboard: Explore State-of-the-Art Materials Design Methods and Reproducible Benchmarks📚📦📦
atomgptlab/atomgptAtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design📚
atomgptlab/chemnlpChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data📚📦📦
atomgptlab/atomvisionAtomVision: Deep learning framework for atomistic image data📚📦
atomgptlab/atomqcAtomQC: Atomistic Calculations on Quantum Computers📚📦
JARVIS-Materials-Design/jarvis-tools-notebooksA Google-Colab Notebook Collection for Materials Design📚
deepmaterials/dlmatreviewRepository for links to software packages and databases used in deep-learning applications for materials science📚
deepmaterials/slmatServerLess Materials Design Toolkit📚
atomgptlab/tb3pyTB3Py: Two- and three-body tight-binding calculations for materials📚📦📦
atomgptlab/intermatInterMat: Interface materials design toolkit📚📦📦
atomgptlab/defectmatDefectMat: Defect materials design toolkit📚
atomgptlab/chipsffEvaluation of universal machine learning force-fields📚📦
atomgptlab/benchqcA Benchmarking Toolkit for Quantum Computation📚
atomgptlab/catalysismatExamining Generalizability of AI Models for Catalysis📚
SciVedantaA collection of YouTube videos on Vedanta philosophy📚
eesocietyEncouraging Excellence Society📚
Google Scholar Pages:
Project / AuthorGoogle Scholar Page
Kamal Choudharyscholar
JARVISscholar
ALIGNNscholar
SuperconDBscholar
AtomGPTscholar
DMS200scholar
DeepMaterials LLCscholar
Here are some links that might interest you:
knc6

Popular repositories
Loading
  1. 
material_explorer_examples
material_explorer_examplesPublic archive
    
ChatGPT Material Explorer 1.0 Example Prompts

    
11

  2. 
JARVIS-FF
JARVIS-FFPublic
    
This project contains the data for evaluation of interatomic potentials/force-fields (used in Moecular-dynamics and Monte-carlo simulations). LAMMPS calculation were done using MPinterface code (ht…

    Python
10

1

  3. 
jarvis-tools-notebooksold
jarvis-tools-notebooksoldPublic
    
Forked from JARVIS-Materials-Design/jarvis-tools-notebooks
    Jupyter Notebook
5

2

  4. 
knc6
knc6Public
    
4

  5. 
alignn
alignnPublic
    
Forked from usnistgov/alignn
    
Atomistic Line Graph Neural Network

    Python
1

1

  6. 
chemnlp-1
chemnlp-1Public
    
Forked from kellml/chemnlp
    
ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data

    Python
1


, '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");
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
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var __m = "*";
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knc6/README.md
Hi there 👋
Kamal Choudhary is
My research interests are focused on atomistic materials design using classical, quantum, and machine learning methods. In particular, I have developed JARVIS database and tools (https://jarvis.nist.gov/) that hosts publicly available datasets for millions of material properties.
Contributions to projects:
NameDescriptionDetailsConda PackagePyPi Package
atomgptlab/chatgpt_material_explorerChatGPT based Material Science Assistant for materials data and simulations.📚
atomgptlab/jarvis-toolsJARVIS-Tools: An open-source software package for data-driven atomistic materials design📚📦📦
atomgptlab/alignnALIGNN: Atomistic Line Graph Neural Network and force-field📚📦📦
atomgptlab/jarvis_leaderboardJARVIS-Leaderboard: Explore State-of-the-Art Materials Design Methods and Reproducible Benchmarks📚📦📦
atomgptlab/atomgptAtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design📚
atomgptlab/chemnlpChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data📚📦📦
atomgptlab/atomvisionAtomVision: Deep learning framework for atomistic image data📚📦
atomgptlab/atomqcAtomQC: Atomistic Calculations on Quantum Computers📚📦
JARVIS-Materials-Design/jarvis-tools-notebooksA Google-Colab Notebook Collection for Materials Design📚
deepmaterials/dlmatreviewRepository for links to software packages and databases used in deep-learning applications for materials science📚
deepmaterials/slmatServerLess Materials Design Toolkit📚
atomgptlab/tb3pyTB3Py: Two- and three-body tight-binding calculations for materials📚📦📦
atomgptlab/intermatInterMat: Interface materials design toolkit📚📦📦
atomgptlab/defectmatDefectMat: Defect materials design toolkit📚
atomgptlab/chipsffEvaluation of universal machine learning force-fields📚📦
atomgptlab/benchqcA Benchmarking Toolkit for Quantum Computation📚
atomgptlab/catalysismatExamining Generalizability of AI Models for Catalysis📚
SciVedantaA collection of YouTube videos on Vedanta philosophy📚
eesocietyEncouraging Excellence Society📚
Google Scholar Pages:
Project / AuthorGoogle Scholar Page
Kamal Choudharyscholar
JARVISscholar
ALIGNNscholar
SuperconDBscholar
AtomGPTscholar
DMS200scholar
DeepMaterials LLCscholar
Here are some links that might interest you:
knc6

Popular repositories
Loading
  1. 
material_explorer_examples
material_explorer_examplesPublic archive
    
ChatGPT Material Explorer 1.0 Example Prompts

    
11

  2. 
JARVIS-FF
JARVIS-FFPublic
    
This project contains the data for evaluation of interatomic potentials/force-fields (used in Moecular-dynamics and Monte-carlo simulations). LAMMPS calculation were done using MPinterface code (ht…

    Python
10

1

  3. 
jarvis-tools-notebooksold
jarvis-tools-notebooksoldPublic
    
Forked from JARVIS-Materials-Design/jarvis-tools-notebooks
    Jupyter Notebook
5

2

  4. 
knc6
knc6Public
    
4

  5. 
alignn
alignnPublic
    
Forked from usnistgov/alignn
    
Atomistic Line Graph Neural Network

    Python
1

1

  6. 
chemnlp-1
chemnlp-1Public
    
Forked from kellml/chemnlp
    
ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data

    Python
1


, '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); }
})();
})();

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Achievements
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knc6/README.md
Hi there 👋
Kamal Choudhary is
My research interests are focused on atomistic materials design using classical, quantum, and machine learning methods. In particular, I have developed JARVIS database and tools (https://jarvis.nist.gov/) that hosts publicly available datasets for millions of material properties.
Contributions to projects:
NameDescriptionDetailsConda PackagePyPi Package
atomgptlab/chatgpt_material_explorerChatGPT based Material Science Assistant for materials data and simulations.📚
atomgptlab/jarvis-toolsJARVIS-Tools: An open-source software package for data-driven atomistic materials design📚📦📦
atomgptlab/alignnALIGNN: Atomistic Line Graph Neural Network and force-field📚📦📦
atomgptlab/jarvis_leaderboardJARVIS-Leaderboard: Explore State-of-the-Art Materials Design Methods and Reproducible Benchmarks📚📦📦
atomgptlab/atomgptAtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design📚
atomgptlab/chemnlpChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data📚📦📦
atomgptlab/atomvisionAtomVision: Deep learning framework for atomistic image data📚📦
atomgptlab/atomqcAtomQC: Atomistic Calculations on Quantum Computers📚📦
JARVIS-Materials-Design/jarvis-tools-notebooksA Google-Colab Notebook Collection for Materials Design📚
deepmaterials/dlmatreviewRepository for links to software packages and databases used in deep-learning applications for materials science📚
deepmaterials/slmatServerLess Materials Design Toolkit📚
atomgptlab/tb3pyTB3Py: Two- and three-body tight-binding calculations for materials📚📦📦
atomgptlab/intermatInterMat: Interface materials design toolkit📚📦📦
atomgptlab/defectmatDefectMat: Defect materials design toolkit📚
atomgptlab/chipsffEvaluation of universal machine learning force-fields📚📦
atomgptlab/benchqcA Benchmarking Toolkit for Quantum Computation📚
atomgptlab/catalysismatExamining Generalizability of AI Models for Catalysis📚
SciVedantaA collection of YouTube videos on Vedanta philosophy📚
eesocietyEncouraging Excellence Society📚
Google Scholar Pages:
Project / AuthorGoogle Scholar Page
Kamal Choudharyscholar
JARVISscholar
ALIGNNscholar
SuperconDBscholar
AtomGPTscholar
DMS200scholar
DeepMaterials LLCscholar
Here are some links that might interest you:
knc6

Popular repositories
Loading
  1. 
material_explorer_examples
material_explorer_examplesPublic archive
    
ChatGPT Material Explorer 1.0 Example Prompts

    
11

  2. 
JARVIS-FF
JARVIS-FFPublic
    
This project contains the data for evaluation of interatomic potentials/force-fields (used in Moecular-dynamics and Monte-carlo simulations). LAMMPS calculation were done using MPinterface code (ht…

    Python
10

1

  3. 
jarvis-tools-notebooksold
jarvis-tools-notebooksoldPublic
    
Forked from JARVIS-Materials-Design/jarvis-tools-notebooks
    Jupyter Notebook
5

2

  4. 
knc6
knc6Public
    
4

  5. 
alignn
alignnPublic
    
Forked from usnistgov/alignn
    
Atomistic Line Graph Neural Network

    Python
1

1

  6. 
chemnlp-1
chemnlp-1Public
    
Forked from kellml/chemnlp
    
ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data

    Python
1