K. Choudhary

发表

Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics

Sergei V. Kalinin, R. Vasudevan, M. Ziatdinov, 2019, MRS communications.

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

Ghanshyam Pilania, Ankit Agrawal, Andrew C. E. Reid, 2020, npj Computational Materials.

Database of Wannier tight-binding Hamiltonians using high-throughput density functional theory

K. Garrity, K. Choudhary, 2020, Scientific data.

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

Brian L. DeCost, Sergei V. Kalinin, D. Vanderbilt, 2020, npj Computational Materials.

Density functional theory-based electric field gradient database

K. Choudhary, I. Mazin, K. Sauer, 2020, Scientific data.

Machine learning predicted magnetic entropy change using chemical descriptors across a large compositional landscape

K. Choudhary, D. Paudyal, H. Ucar, 2022, Computational Materials Science.

Efficiently searching extreme mechanical properties via boundless objective-free exploration and minimal first-principles calculations

Ming Hu, K. Choudhary, M. Al-Fahdi, 2022, npj Computational Materials.

High-throughput discovery of topological materials using spin-orbit spillage

K. Garrity, F. Tavazza, K. Choudhary, 2018 .

Computational Discovery of Inorganic Electrides from an Automated Screening

T. Frolov, K. Choudhary, Qiang Zhu, 2019, Matter.

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

K. Choudhary, Mathew L. Kelley, 2022, 2209.08203.

Reproducible sorbent materials foundry for carbon capture at scale

Brian L. DeCost, C. Stafford, T. Yildirim, 2022, Cell Reports Physical Science.

Unified Graph Neural Network Force-field for the Periodic Table for Solids

Brian L. DeCost, F. Tavazza, K. Choudhary, 2022, Digital Discovery.

A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials

B. Sumpter, K. Choudhary, 2022, 2205.08366.

Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning

K. Garrity, F. Tavazza, G. Pilania, 2020, 2004.03025.

A critical examination of robustness and generalizability of machine learning prediction of materials properties

Brian L. DeCost, J. Hattrick-Simpers, K. Choudhary, 2022, npj Computational Materials.

Rapid prediction of phonon structure and properties using the atomistic line graph neural network (ALIGNN)

F. Tavazza, K. Choudhary, Ramya Gurunathan, 2022, Physical Review Materials.

Graph Neural Network Predictions of Metal Organic Framework CO 2 Adsorption Properties

T. Yildirim, D. Siderius, A. Gilad, 2022 .

AtomVision: A Machine Vision Library for Atomistic Images

Brian L. DeCost, A. Biacchi, K. Choudhary, 2022, J. Chem. Inf. Model..

A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials

Jason R. Hattrick-Simpers, Kamal Choudhary, Claudio Corgnale, 2018 .

Recent advances and applications of deep learning methods in materials science

Brian L. DeCost, Cheol Woo Park, A. Choudhary, 2021, npj Computational Materials.

Graph neural network predictions of metal organic framework CO2 adsorption properties

A. Kusne, D. Siderius, A. McDannald, 2022, Computational Materials Science.

Graph Neural Network Predictions of Metal Organic Framework CO2 Adsorption Properties

T. Yildirim, A. Kusne, A. McDannald, 2021, 2112.10231.

Atomistic Line Graph Neural Network for improved materials property predictions

Brian L. DeCost, K. Choudhary, 2021, npj Computational Materials.

OPTIMADE, an API for exchanging materials data

Matthew K. Horton, A. J. Morris, G. Rignanese, 2021, Scientific Data.

Characterization of Few-Layer 1T' MoTe2 by Polarization-Resolved Second Harmonic Generation and Raman Scattering.

Albert V. Davydov, Ryan Beams, Francesca Tavazza, 2016, ACS nano.

Artificial intelligence for search and discovery of quantum materials

I. Takeuchi, A. Kusne, K. Choudhary, 2021, Communications Materials.

MPInterfaces: A Materials Project based Python tool for high-throughput computational screening of interfacial systems

Albert V. Davydov, Richard G. Hennig, Francesca Tavazza, 2016, 1602.07784.

High-throughput density functional perturbation theory and machine learning predictions of infrared, piezoelectric, and dielectric responses

Francesca Tavazza, Angela R. Hight Walker, Kamal Choudhary, 2019, npj Computational Materials.

Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods.

Jie Jiang, Ruth Pachter, Francesca Tavazza, 2019, Chemistry of materials : a publication of the American Chemical Society.

Computational scanning tunneling microscope image database

Sergei V. Kalinin, R. Vasudevan, M. Ziatdinov, 2021, Scientific data.

Density Functional Theory and Deep-learning to Accelerate Data Analytics in Scanning Tunneling Microscopy

Rama Vasudevan, Maxim Ziatdinov, Sergei V. Kalinin, 2019, 1912.09027.

Quantum computation for predicting electron and phonon properties of solids

K. Choudhary, 2021, Journal of physics. Condensed matter : an Institute of Physics journal.

JARVIS: An Integrated Infrastructure for Data-driven Materials Design

Brian L. DeCost, Sergei V. Kalinin, D. Vanderbilt, 2020 .

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

K. Choudhary, Mathew L. Kelley, 2022, 2209.08203.

Machine learning approaches for feature engineering of the crystal structure: Application to the prediction of the formation energy of cubic compounds

S. Kalidindi, K. Choudhary, Prathik R. Kaundinya, 2021, Physical Review Materials.

Computational search for magnetic and non-magnetic 2D topological materials using unified spin–orbit spillage screening

K. Garrity, F. Tavazza, R. Pachter, 2020, npj Computational Materials.

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Wei-keng Liao, Alok Choudhary, Ankit Agrawal, 2019, Nature Communications.

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

Yong Zhao, Ming Hu, Jianjun Hu, 2023, npj Computational Materials.

Inverse design of next-generation superconductors using data-driven deep generative models

K. Choudhary, T. Xie, D. Wines, 2023, 2304.08446.

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

W. Liao, F. Tavazza, C. Campbell, 2021, Nature Communications.

On the redundancy in large material datasets: efficient and robust learning with less data

Brian L. DeCost, J. Hattrick-Simpers, K. Choudhary, 2023, 2304.13076.

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

Ghezal Ahmad Jan Zia, Samuel G. Rodriques, Logan T. Ward, 2023, 2306.06283.

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

W. Liao, F. Tavazza, Vishu Gupta, 2023, J. Chem. Inf. Model..

Predicting Lattice Vibrational Frequencies Using Deep Graph Neural Networks

Steph-Yves M. Louis, Ming Hu, Jianjun Hu, 2022, ACS omega.

Prediction of the Electron Density of States for Crystalline Compounds with Atomistic Line Graph Neural Networks (ALIGNN)

Surya R. Kalidindi, Kamal Choudhary, Prathik R Kaundinya, 2022, JOM.

Predicting Lattice Phonon Vibrational Frequencies Using Deep Graph Neural Networks pdf

Kamal Choudhary, Steph-Yves M. Louis, Ming Hu, 2021, ArXiv.

Uncertainty Prediction for Machine Learning Models of Material Properties pdf

Brian L. DeCost, Francesca Tavazza, Kamal Choudhary, 2021, ACS omega.

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

Brian L. DeCost, K. Garrity, F. Tavazza, 2023, 2305.11842.

Fano resonance due to discrete breather in nonlinear Klein–Gordon lattice in metamaterials

K. Choudhary, A. Biswas, S. Adhikari, 2012 .

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

Ghezal Ahmad Jan Zia, Samuel G. Rodriques, Logan T. Ward, 2023, Digital discovery.

Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning

K. Garrity, F. Tavazza, G. Pilania, 2023, Physical Review Materials.

OPTIMADE, an API for exchanging materials data

Andrew J. Morris, K. Persson, G. Rignanese, 2021, Scientific Data.

An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

Md Mahmudul Hasan, W. Liao, F. Tavazza, 2023, npj Computational Materials.

Computational Discovery of Inorganic Electrides from an Automated Screening

T. Frolov, K. Choudhary, Q. Zhu, 2018, Matter.

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

K. Choudhary, Mathew L. Kelley, 2022, 2209.08203.

Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example

H. Zhuang, K. Choudhary, Haofan Sun, 2023, ArXiv.

Exploiting redundancy in large materials datasets for efficient machine learning with less data

Brian L. DeCost, J. Hattrick-Simpers, K. Choudhary, 2023, Nature communications.

High-throughput discovery of topological materials using spin-orbit spillage

K. Garrity, F. Tavazza, K. Choudhary, 2018 .

Materials science in the arti ﬁ cial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics

Sergei V. Kalinin, R. Vasudevan, M. Ziatdinov, 2019 .

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

Brian L. DeCost, K. Garrity, F. Tavazza, 2023, Applied Physics Reviews.

Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models.

K. Choudhary, T. Xie, D. Wines, 2023, The journal of physical chemistry letters.

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

Brian L. DeCost, Sergei V. Kalinin, D. Vanderbilt, 2020, npj Computational Materials.

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Luke E. K. Achenie, Sterling G. Baird, Shuiwang Ji, 2023, npj Computational Materials.