際際滷shows by User: KAMALCHOUDHARY4 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: KAMALCHOUDHARY4 / Sun, 07 Jul 2024 09:51:41 GMT 際際滷Share feed for 際際滷shows by User: KAMALCHOUDHARY4 Recent Advancements in the NIST-JARVIS Infrastructure /slideshow/recent-advancements-in-the-nist-jarvis-infrastructure/270103944 jarvis-lb-240707095142-5c5585b0
Recent advancements in the NIST-JARVIS infrastructure: JARVIS-Overview, JARVIS-DFT, AtomGPT, ALIGNN, JARVIS-Leaderboard]]>
Recent advancements in the NIST-JARVIS infrastructure: JARVIS-Overview, JARVIS-DFT, AtomGPT, ALIGNN, JARVIS-Leaderboard]]> Sun, 07 Jul 2024 09:51:41 GMT /slideshow/recent-advancements-in-the-nist-jarvis-infrastructure/270103944 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) Recent Advancements in the NIST-JARVIS Infrastructure KAMALCHOUDHARY4 Recent advancements in the NIST-JARVIS infrastructure: JARVIS-Overview, JARVIS-DFT, AtomGPT, ALIGNN, JARVIS-Leaderboard <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-lb-240707095142-5c5585b0-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Recent advancements in the NIST-JARVIS infrastructure: JARVIS-Overview, JARVIS-DFT, AtomGPT, ALIGNN, JARVIS-Leaderboard
Recent Advancements in the NIST-JARVIS Infrastructure from KAMAL CHOUDHARY
]]> 252 0 https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-lb-240707095142-5c5585b0-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data /slideshow/chemnlp-a-natural-language-processing-based-library-for-materials-chemistry-text-data/259189987 jarvis-polymergroup-230713153156-57148694
In this work, we present the ChemNLP library that can be used for 1) curating open access datasets for materials and chemistry literature, developing and comparing traditional machine learning, transformers and graph neural network models for 2) classifying and clustering texts, 3) named entity recognition for large-scale text-mining, 4) abstractive summarization for generating titles of articles from abstracts, 5) text generation for suggesting abstracts from titles, 6) integration with density functional theory dataset for identifying potential candidate materials such as superconductors, and 7) web-interface development for text and reference query. We primarily use the publicly available arXiv and Pubchem datasets but the tools can be used for other datasets as well. Moreover, as new models are developed, they can be easily integrated in the library. ]]>
In this work, we present the ChemNLP library that can be used for 1) curating open access datasets for materials and chemistry literature, developing and comparing traditional machine learning, transformers and graph neural network models for 2) classifying and clustering texts, 3) named entity recognition for large-scale text-mining, 4) abstractive summarization for generating titles of articles from abstracts, 5) text generation for suggesting abstracts from titles, 6) integration with density functional theory dataset for identifying potential candidate materials such as superconductors, and 7) web-interface development for text and reference query. We primarily use the publicly available arXiv and Pubchem datasets but the tools can be used for other datasets as well. Moreover, as new models are developed, they can be easily integrated in the library. ]]> Thu, 13 Jul 2023 15:31:56 GMT /slideshow/chemnlp-a-natural-language-processing-based-library-for-materials-chemistry-text-data/259189987 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data KAMALCHOUDHARY4 In this work, we present the ChemNLP library that can be used for 1) curating open access datasets for materials and chemistry literature, developing and comparing traditional machine learning, transformers and graph neural network models for 2) classifying and clustering texts, 3) named entity recognition for large-scale text-mining, 4) abstractive summarization for generating titles of articles from abstracts, 5) text generation for suggesting abstracts from titles, 6) integration with density functional theory dataset for identifying potential candidate materials such as superconductors, and 7) web-interface development for text and reference query. We primarily use the publicly available arXiv and Pubchem datasets but the tools can be used for other datasets as well. Moreover, as new models are developed, they can be easily integrated in the library. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-polymergroup-230713153156-57148694-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> In this work, we present the ChemNLP library that can be used for 1) curating open access datasets for materials and chemistry literature, developing and comparing traditional machine learning, transformers and graph neural network models for 2) classifying and clustering texts, 3) named entity recognition for large-scale text-mining, 4) abstractive summarization for generating titles of articles from abstracts, 5) text generation for suggesting abstracts from titles, 6) integration with density functional theory dataset for identifying potential candidate materials such as superconductors, and 7) web-interface development for text and reference query. We primarily use the publicly available arXiv and Pubchem datasets but the tools can be used for other datasets as well. Moreover, as new models are developed, they can be easily integrated in the library.
ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data from KAMAL CHOUDHARY
]]> 71 0 https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-polymergroup-230713153156-57148694-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 NIST-JARVIS infrastructure for Improved Materials Design /slideshow/nistjarvis-infrastructure-for-improved-materials-design/253621754 jarvis-cecam-infrastructure-221016153558-28808b52
NIST-JARVIS infrastructure for Improved Materials Design]]>
NIST-JARVIS infrastructure for Improved Materials Design]]> Sun, 16 Oct 2022 15:35:57 GMT /slideshow/nistjarvis-infrastructure-for-improved-materials-design/253621754 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) NIST-JARVIS infrastructure for Improved Materials Design KAMALCHOUDHARY4 NIST-JARVIS infrastructure for Improved Materials Design <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-cecam-infrastructure-221016153558-28808b52-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> NIST-JARVIS infrastructure for Improved Materials Design
NIST-JARVIS infrastructure for Improved Materials Design from KAMAL CHOUDHARY
]]> 162 0 https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-cecam-infrastructure-221016153558-28808b52-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Quantum Computation for Predicting Electron and Phonon Properties of Solids /slideshow/quantum-computation-for-predicting-electron-and-phonon-properties-of-solids/251252830 jarvis-qc-dcsvedits-220226231732
Quantum Computation for Predicting Electron and Phonon Properties of Solids]]>
Quantum Computation for Predicting Electron and Phonon Properties of Solids]]> Sat, 26 Feb 2022 23:17:31 GMT /slideshow/quantum-computation-for-predicting-electron-and-phonon-properties-of-solids/251252830 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) Quantum Computation for Predicting Electron and Phonon Properties of Solids KAMALCHOUDHARY4 Quantum Computation for Predicting Electron and Phonon Properties of Solids <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-qc-dcsvedits-220226231732-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Quantum Computation for Predicting Electron and Phonon Properties of Solids
Quantum Computation for Predicting Electron and Phonon Properties of Solids from KAMAL CHOUDHARY
]]> 475 0 https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-qc-dcsvedits-220226231732-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Materials Design in the Age of Deep Learning and Quantum Computation /slideshow/materials-design-in-the-age-of-deep-learning-and-quantum-computation/251252826 jarvis-miracle-220226231414
Materials Design in the Age of Deep Learning and Quantum Computation]]>
Materials Design in the Age of Deep Learning and Quantum Computation]]> Sat, 26 Feb 2022 23:14:14 GMT /slideshow/materials-design-in-the-age-of-deep-learning-and-quantum-computation/251252826 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) Materials Design in the Age of Deep Learning and Quantum Computation KAMALCHOUDHARY4 Materials Design in the Age of Deep Learning and Quantum Computation <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-miracle-220226231414-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Materials Design in the Age of Deep Learning and Quantum Computation
Materials Design in the Age of Deep Learning and Quantum Computation from KAMAL CHOUDHARY
]]> 321 0 https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-miracle-220226231414-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Smart Metrics for High Performance Material Design /slideshow/smart-metrics-for-high-performance-material-design-166597985/166597985 jarvis-aimsv22019-190826172340
Kamal Choudhary, NIST]]>
Kamal Choudhary, NIST]]> Mon, 26 Aug 2019 17:23:40 GMT /slideshow/smart-metrics-for-high-performance-material-design-166597985/166597985 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) Smart Metrics for High Performance Material Design KAMALCHOUDHARY4 Kamal Choudhary, NIST <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-aimsv22019-190826172340-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Kamal Choudhary, NIST
Smart Metrics for High Performance Material Design from KAMAL CHOUDHARY
]]> 695 3 https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-aimsv22019-190826172340-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Database of Topological Materials and Spin-orbit Spillage /slideshow/database-of-topological-materials-and-spinorbit-spillage/135213201 jarvis-aps2019-talkv1-190308135746
We present the results of a high-throughput, first principles search for topological materials based on identifying materials with band inversion induced by spin-orbit coupling. Out of the currently available 30000 materials in our database, we investigate more than 4507 non-magnetic materials having heavy atoms and low bandgaps. We compute the spillage between the spin-orbit and non-spin-orbit wave functions, resulting in more than 1699 high-spillage candidate materials. We demonstrate that in addition to Z2 topological insulators, this screening method successfully identifies many semimetals and topological crystalline insulators. Our approach is applicable to the investigation of disordered or distorted materials, because it is not based on symmetry considerations, and it can be extended to magnetic materials. After our first screening step, we use Wannier-interpolation to calculate the topological invariants and to search for band crossings in our candidate materials. We discuss some individual example materials, as well as trends throughout our dataset, that is available at JARVIS-DFT website: http://jarvis.nist.gov]]>
We present the results of a high-throughput, first principles search for topological materials based on identifying materials with band inversion induced by spin-orbit coupling. Out of the currently available 30000 materials in our database, we investigate more than 4507 non-magnetic materials having heavy atoms and low bandgaps. We compute the spillage between the spin-orbit and non-spin-orbit wave functions, resulting in more than 1699 high-spillage candidate materials. We demonstrate that in addition to Z2 topological insulators, this screening method successfully identifies many semimetals and topological crystalline insulators. Our approach is applicable to the investigation of disordered or distorted materials, because it is not based on symmetry considerations, and it can be extended to magnetic materials. After our first screening step, we use Wannier-interpolation to calculate the topological invariants and to search for band crossings in our candidate materials. We discuss some individual example materials, as well as trends throughout our dataset, that is available at JARVIS-DFT website: http://jarvis.nist.gov]]> Fri, 08 Mar 2019 13:57:45 GMT /slideshow/database-of-topological-materials-and-spinorbit-spillage/135213201 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) Database of Topological Materials and Spin-orbit Spillage KAMALCHOUDHARY4 We present the results of a high-throughput, first principles search for topological materials based on identifying materials with band inversion induced by spin-orbit coupling. Out of the currently available 30000 materials in our database, we investigate more than 4507 non-magnetic materials having heavy atoms and low bandgaps. We compute the spillage between the spin-orbit and non-spin-orbit wave functions, resulting in more than 1699 high-spillage candidate materials. We demonstrate that in addition to Z2 topological insulators, this screening method successfully identifies many semimetals and topological crystalline insulators. Our approach is applicable to the investigation of disordered or distorted materials, because it is not based on symmetry considerations, and it can be extended to magnetic materials. After our first screening step, we use Wannier-interpolation to calculate the topological invariants and to search for band crossings in our candidate materials. We discuss some individual example materials, as well as trends throughout our dataset, that is available at JARVIS-DFT website: http://jarvis.nist.gov <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-aps2019-talkv1-190308135746-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> We present the results of a high-throughput, first principles search for topological materials based on identifying materials with band inversion induced by spin-orbit coupling. Out of the currently available 30000 materials in our database, we investigate more than 4507 non-magnetic materials having heavy atoms and low bandgaps. We compute the spillage between the spin-orbit and non-spin-orbit wave functions, resulting in more than 1699 high-spillage candidate materials. We demonstrate that in addition to Z2 topological insulators, this screening method successfully identifies many semimetals and topological crystalline insulators. Our approach is applicable to the investigation of disordered or distorted materials, because it is not based on symmetry considerations, and it can be extended to magnetic materials. After our first screening step, we use Wannier-interpolation to calculate the topological invariants and to search for band crossings in our candidate materials. We discuss some individual example materials, as well as trends throughout our dataset, that is available at JARVIS-DFT website: http://jarvis.nist.gov
Database of Topological Materials and Spin-orbit Spillage from KAMAL CHOUDHARY
]]> 702 2 https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-aps2019-talkv1-190308135746-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Elastic properties of bulk and low-dimensional materials using Van der Waals density functional and machine-learning /slideshow/elastic-properties-of-bulk-and-lowdimensional-materials-using-van-der-waals-density-functional-and-machinelearning/124205032 elasticposter-mrs2018-181127205104
JARVIS-DFT]]>
JARVIS-DFT]]> Tue, 27 Nov 2018 20:51:04 GMT /slideshow/elastic-properties-of-bulk-and-lowdimensional-materials-using-van-der-waals-density-functional-and-machinelearning/124205032 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) Elastic properties of bulk and low-dimensional materials using Van der Waals density functional and machine-learning KAMALCHOUDHARY4 JARVIS-DFT <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/elasticposter-mrs2018-181127205104-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> JARVIS-DFT
Elastic properties of bulk and low-dimensional materials using Van der Waals density functional and machine-learning from KAMAL CHOUDHARY
]]> 335 4 https://cdn.slidesharecdn.com/ss_thumbnails/elasticposter-mrs2018-181127205104-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 High-throughput discovery of low-dimensional and topologically non-trivial materials /slideshow/highthroughput-discovery-of-lowdimensional-and-topologically-nontrivial-materials/124204973 2dtopo-mrs2018-181127204943
JARVIS-DFT database]]>
JARVIS-DFT database]]> Tue, 27 Nov 2018 20:49:43 GMT /slideshow/highthroughput-discovery-of-lowdimensional-and-topologically-nontrivial-materials/124204973 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) High-throughput discovery of low-dimensional and topologically non-trivial materials KAMALCHOUDHARY4 JARVIS-DFT database <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2dtopo-mrs2018-181127204943-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> JARVIS-DFT database
High-throughput discovery of low-dimensional and topologically non-trivial materials from KAMAL CHOUDHARY
]]> 205 6 https://cdn.slidesharecdn.com/ss_thumbnails/2dtopo-mrs2018-181127204943-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Accelerated Materials Discovery & Characterization with Classical, Quantum and Machine learning approaches /slideshow/accelerated-materials-discovery-characterization-with-classical-quantum-and-machine-learning-approaches/124204896 jarvis-iitbhu-4-181127204801
JARVIS NIST database and tools]]>
JARVIS NIST database and tools]]> Tue, 27 Nov 2018 20:48:01 GMT /slideshow/accelerated-materials-discovery-characterization-with-classical-quantum-and-machine-learning-approaches/124204896 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) Accelerated Materials Discovery & Characterization with Classical, Quantum and Machine learning approaches KAMALCHOUDHARY4 JARVIS NIST database and tools <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-iitbhu-4-181127204801-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> JARVIS NIST database and tools
Accelerated Materials Discovery & Characterization with Classical, Quantum and Machine learning approaches from KAMAL CHOUDHARY
]]> 381 4 https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-iitbhu-4-181127204801-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Physics inspired artificial intelligence/machine learning /slideshow/physics-inspired-artificial-intelligencemachine-learning-101315296/101315296 devfestdc-2-180608131039
JARVIS-FF, JARVIS-DFT, and JARVIS-ML https://jarvis.nist.gov/]]>
JARVIS-FF, JARVIS-DFT, and JARVIS-ML https://jarvis.nist.gov/]]> Fri, 08 Jun 2018 13:10:39 GMT /slideshow/physics-inspired-artificial-intelligencemachine-learning-101315296/101315296 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) Physics inspired artificial intelligence/machine learning KAMALCHOUDHARY4 JARVIS-FF, JARVIS-DFT, and JARVIS-ML https://jarvis.nist.gov/ <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/devfestdc-2-180608131039-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> JARVIS-FF, JARVIS-DFT, and JARVIS-ML https://jarvis.nist.gov/
Physics inspired artificial intelligence/machine learning from KAMAL CHOUDHARY
]]> 2599 7 https://cdn.slidesharecdn.com/ss_thumbnails/devfestdc-2-180608131039-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Computational Database for 3D and 2D materials to accelerate discovery /slideshow/computational-database-for-3d-and-2d-materials-to-accelerate-discovery/96395621 psu-2018-180508140816
The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.]]>
The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.]]> Tue, 08 May 2018 14:08:16 GMT /slideshow/computational-database-for-3d-and-2d-materials-to-accelerate-discovery/96395621 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) Computational Database for 3D and 2D materials to accelerate discovery KAMALCHOUDHARY4 The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/psu-2018-180508140816-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons.
Computational Database for 3D and 2D materials to accelerate discovery from KAMAL CHOUDHARY
]]> 439 3 https://cdn.slidesharecdn.com/ss_thumbnails/psu-2018-180508140816-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Computational Discovery of Two-Dimensional Materials, Evaluation of Force-Fields and Machine Learning /slideshow/computational-discovery-of-twodimensional-materials-evaluation-of-forcefields-and-machine-learning/87953516 jarvis-nu-180213224935
JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery using classical force-field, density functional theory, machine learning calculations and experiments. The Force-field section of JARVIS (JARVIS-FF) consists of thousands of automated LAMMPS based force-field calculations on DFT geometries. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials. The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons. The Machine-learning section of JARVIS (JARVIS-ML) consists of machine learning prediction tools, trained on JARVIS-DFT data. Some of the ML-predictions focus on energetics, heat of formation, GGA/METAGGA bandgaps, bulk and shear modulus. The ML webpage is visible to NIST employees only right now, but will be available publicly soon.]]>
JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery using classical force-field, density functional theory, machine learning calculations and experiments. The Force-field section of JARVIS (JARVIS-FF) consists of thousands of automated LAMMPS based force-field calculations on DFT geometries. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials. The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons. The Machine-learning section of JARVIS (JARVIS-ML) consists of machine learning prediction tools, trained on JARVIS-DFT data. Some of the ML-predictions focus on energetics, heat of formation, GGA/METAGGA bandgaps, bulk and shear modulus. The ML webpage is visible to NIST employees only right now, but will be available publicly soon.]]> Tue, 13 Feb 2018 22:49:35 GMT /slideshow/computational-discovery-of-twodimensional-materials-evaluation-of-forcefields-and-machine-learning/87953516 KAMALCHOUDHARY4@slideshare.net(KAMALCHOUDHARY4) Computational Discovery of Two-Dimensional Materials, Evaluation of Force-Fields and Machine Learning KAMALCHOUDHARY4 JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery using classical force-field, density functional theory, machine learning calculations and experiments. The Force-field section of JARVIS (JARVIS-FF) consists of thousands of automated LAMMPS based force-field calculations on DFT geometries. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials. The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons. The Machine-learning section of JARVIS (JARVIS-ML) consists of machine learning prediction tools, trained on JARVIS-DFT data. Some of the ML-predictions focus on energetics, heat of formation, GGA/METAGGA bandgaps, bulk and shear modulus. The ML webpage is visible to NIST employees only right now, but will be available publicly soon. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-nu-180213224935-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> JARVIS (Joint Automated Repository for Various Integrated Simulations) is a repository designed to automate materials discovery using classical force-field, density functional theory, machine learning calculations and experiments. The Force-field section of JARVIS (JARVIS-FF) consists of thousands of automated LAMMPS based force-field calculations on DFT geometries. Some of the properties included in JARVIS-FF are energetics, elastic constants, surface energies, defect formations energies and phonon frequencies of materials. The Density functional theory section of JARVIS (JARVIS-DFT) consists of thousands of VASP based calculations for 3D-bulk, single layer (2D), nanowire (1D) and molecular (0D) systems. Most of the calculations are carried out with optB88vDW functional. JARVIS-DFT includes materials data such as: energetics, diffraction pattern, radial distribution function, band-structure, density of states, carrier effective mass, temperature and carrier concentration dependent thermoelectric properties, elastic constants and gamma-point phonons. The Machine-learning section of JARVIS (JARVIS-ML) consists of machine learning prediction tools, trained on JARVIS-DFT data. Some of the ML-predictions focus on energetics, heat of formation, GGA/METAGGA bandgaps, bulk and shear modulus. The ML webpage is visible to NIST employees only right now, but will be available publicly soon.
Computational Discovery of Two-Dimensional Materials, Evaluation of Force-Fields and Machine Learning from KAMAL CHOUDHARY
]]> 1170 6 https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-nu-180213224935-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-lb-240707095142-5c5585b0-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/recent-advancements-in-the-nist-jarvis-infrastructure/270103944 Recent Advancements in... https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-polymergroup-230713153156-57148694-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/chemnlp-a-natural-language-processing-based-library-for-materials-chemistry-text-data/259189987 ChemNLP: A Natural Lan... https://cdn.slidesharecdn.com/ss_thumbnails/jarvis-cecam-infrastructure-221016153558-28808b52-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/nistjarvis-infrastructure-for-improved-materials-design/253621754 NIST-JARVIS infrastruc...