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Search Publications by: Francesca Tavazza (Fed)

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Displaying 1 - 25 of 76

Structure-Aware GNN-Based Deep Transfer Learning Framework For Enhanced Predictive Analytics On Small Materials Data

January 2, 2024
Author(s)
Vishu Gupta, Kamal Choudhary, Brian DeCost, Francesca Tavazza, Carelyn E. Campbell, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
Modern data mining methods have been demonstrated to be effective tools to comprehend and predict materials properties. An essential component in the process of materials discovery is to know which material(s) (represented by their composition and crystal

Recent progress in the JARVIS infrastructure for next-generation data-driven materials design

October 18, 2023
Author(s)
Daniel Wines, Ramya Gurunathan, Kevin Garrity, Brian DeCost, Adam Biacchi, Francesca Tavazza, Kamal Choudhary
The Joint Automated Repository for Various Integrated Simulations (JARVIS) infrastructure at NIST is a large-scale collection of curated datasets and tools with more than 80000 materials and millions of properties. JARVIS uses a combination of electronic

AI for Materials

April 25, 2023
Author(s)
Debra Audus, Kamal Choudhary, Brian DeCost, A. Gilad Kusne, Francesca Tavazza, James A. Warren
The application of artificial intelligence (AI) methods to materials re- search and development (MR&D) is poised to radically reshape how materials are discovered, designed, and deployed into manufactured products. Materials underpin modern life, and

MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction

March 27, 2023
Author(s)
Kamal Choudhary, Francesca Tavazza, Carelyn E. Campbell, Vishu Gupta, Yuwei Mao, Kewei Wang, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from available

Unified graph neural network force-field for the periodic table: solid state applications

February 23, 2023
Author(s)
Kamal Choudhary, Brian DeCost, Lily Major, Keith Butler, Jeyan Thiyagalingam, Francesca Tavazza
Classical force fields (FFs) based on machine learning (ML) methods show great potential for large scale simulations of solids. MLFFs have hitherto largely been designed and fitted for specific systems and are not usually transferable to chemistries beyond

Recent Advances in Weyl Semimetal ( MnBi2Se4) and Axion Insulator (MnBi2Te4)

March 25, 2022
Author(s)
Sugata Chowdhury, Kevin Garrity, Francesca Tavazza
Extensive research is currently focused on 2D and 3D magnetic topological insulators (MTIs), as their many novel properties make them excellent candidates for applications in spintronics and quantum computing. Practical MTIs requires a combination of

Influence of Dimensionality on the Charge Density Wave Phase of 2H-TaSe2

March 23, 2022
Author(s)
Sugata Chowdhury, Albert Rigosi, Heather Hill, David B. Newell, Angela R. Hight Walker, Francesca Tavazza, Andrew Briggs
Metallic transition metal dichalcogenides like tantalum diselenide (TaSe2) exhibit exciting behaviors at low temperatures including the emergence of charge density wave (CDW) states. In this work, density functional theory (DFT) is used to classify the

Recent Advances and Applications of Deep Learning Methods in Materials Science

February 24, 2022
Author(s)
Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca Tavazza, Ryan Cohn, Cheol WooPark, Alok Choudhary, Ankit Agrawal, Simon Billinge, Elizabeth Holm, ShyuePing Ong, Chris Wolverton
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. Deep learning allows analysis of unstructured data and automated

Computational scanning tunneling microscope image database

December 5, 2021
Author(s)
Kamal Choudhary, Kevin Garrity, Charles Camp, Sergei Kalinin, Rama Vasudevan, Maxim Ziatdinov, Francesca Tavazza
We introduce the systematic database of scanning tunneling microscope (STM) images obtained using density functional theory (DFT) for two-dimensional (2D) materials, calculated using the Tersoff-Hamann method. It currently contains data for 716 exfoliable

Bayesian automated weighting of aggregated DFT, MD, and experimental data for candidate thermodynamic models of aluminum with uncertainty quantification

December 1, 2021
Author(s)
Francesca Tavazza, Chandler A. Becker, Ursula R. Kattner, Joshua Gabriel, Noah Palson, Thien Duong, Marius Stan
Atomic-scale modeling methods such as density functional theory (DFT) and molecular dynamics (MD) can predict the thermodynamic properties of materials at a lower cost than experimental measurements. However, their regular usage in thermodynamic model

Uncertainty Prediction for Machine Learning Models of Material Properties

November 23, 2021
Author(s)
Francesca Tavazza, Brian DeCost, Kamal Choudhary
Uncertainty quantification in artificial intelligence (AI)-based predictions of material properties is of immense importance for the success and reliability of AI applications in materials science. While confidence intervals are commonly reported for

Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

November 15, 2021
Author(s)
Vishu Gupta, Kamal Choudhary, Francesca Tavazza, Carelyn E. Campbell, Wei-Keng Liao, Alok Choudhary, Ankit Agrawal
Artificial Intelligence (AI) and Machine Learning (ML) has been increasingly used in materials science to build property prediction models and accelerate materials discovery. The availability of large materials databases for some properties like formation

Atomistic Insights into Disclocation Dynamics in Metal Forming

October 12, 2021
Author(s)
Francesca Tavazza, Anne M. Chaka, Lyle E. Levine
Almost all of the mechanical behavior changes that occur during plastic deformation result from the evolution of dislocation structures. Statistical models, like strain percolation theory, have been developed to understand the transport of dislocations

Predicting anomalous quantum confinement effect in van der Waals materials

April 21, 2021
Author(s)
Francesca Tavazza, Kamal Choudhary
Materials with van der Waals bonding are known to exhibit a quantum confinement effect, in which the electronic band gap of the three-dimensional realization of a material is lower than that of its two-dimensional (2D) counterpart. However, the possibility

Uncertainty Quantification of Atomistic (DFT and MD), Mesoscale (PFM) and Continuum (CALPHAD) Methods and the Impact on Thermodynamic Models of Metals: A Review

March 5, 2021
Author(s)
Gabriel Joshua, Noah Paulson, Thien Duong, Francesca Tavazza, Chandler Becker, Santanu Chaudhuri, Stan Marious
Design of improved metals relies on multi-scale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. In particular, atomistic methods such as Density Functional Theory (DFT) and Molecular Dynamics (MD) have

Topological surface states of MnBi2Te4 at finite temperatures and at domain walls

February 24, 2021
Author(s)
Kevin F. Garrity, Sugata Chowdhury, Francesca Tavazza
MnBi2Te4 has recently been the subject of intensive study, due to the prediction of axion insulator, Weyl semimetal, and quantum anomalous Hall insulator phases, depending on the structure and magnetic ordering. Experimental results have con firmed some