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

Systematic DFT+U and Quantum Monte Carlo Benchmark of Magnetic Two-Dimensional (2D) CrX3 (X = I, Br, Cl, F)

(2023) Daniel Wines et al.   Journal of Physical Chemistry C

High-Throughput DFT-Based Discovery of Next Generation Two-Dimensional (2D) Superconductors

(2023) Daniel Wines et al.   Nano Letters

Hundreds of new, stable, one-dimensional materials from a generative machine learning model

(2023) Hadeel Moustafa et al.   Physical Review Materials

Machine Learning Prediction of Superconducting Critical Temperature through the Structural Descriptor

(2022) Jingzi Zhang et al.   Journal of Physical Chemistry C

E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

(2022) Simon Batzner et al.   Nature Communications

A Generative Approach to Materials Discovery, Design, and Optimization

(2022) Dhruv Menon et al.   ACS Omega

Data-driven discovery of 2D materials by deep generative models

(2022) Peder Lyngby et al.   npj Computational Materials

Designing high-TC superconductors with BCS-inspired screening, density functional theory, and deep-learning

(2022) Kamal Choudhary et al.   npj Computational Materials

Computational Discovery of New 2D Materials Using Deep Learning Generative Models

(2021) Yuqi Song et al.   ACS Applied Materials & Interfaces

Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures

(2021) Teng Long et al.   npj Computational Materials

Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

(2021) Paulino José García-Nieto et al.   NEURAL COMPUTING & APPLICATIONS

Recent progress of the computational 2D materials database (C2DB)

(2021) Morten Niklas Gjerding et al.   2D Materials

High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks

(2021) Yong Zhao et al.   Advanced Science

Atomistic Line Graph Neural Network for improved materials property predictions

(2021) Kamal Choudhary et al.   npj Computational Materials

Predicting new superconductors and their critical temperatures using machine learning

(2020) B. Roter et al.   PHYSICA C-SUPERCONDUCTIVITY AND ITS APPLICATIONS

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

(2020) Kamal Choudhary et al.   npj Computational Materials

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

(2020) Kamal Choudhary et al.   npj Computational Materials

Generative Adversarial Networks for Crystal Structure Prediction

(2020) Sungwon Kim et al.   ACS Central Science

Density functional theory-based electric field gradient database

(2020) Kamal Choudhary et al.   Scientific Data

Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations

(2019) Kamal Choudhary et al.   COMPUTATIONAL MATERIALS SCIENCE

Accelerated Discovery of Efficient Solar Cell Materials Using Quantum and Machine-Learning Methods

(2019) Kamal Choudhary et al.   CHEMISTRY OF MATERIALS

High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage

(2019) Kamal Choudhary et al.   Scientific Reports

Superconductivity Behavior in Epitaxial TiN Films Points to Surface Magnetic Disorder

(2019) N.A. Saveskul et al.   Physical Review Applied

The computational 2D materials database: High-throughput modeling and discovery of atomically thin crystals

(2018) Sten Haastrup et al.   2D Materials

Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms

(2018) Kamal Choudhary et al.   Scientific Data

Thermodynamic limit for synthesis of metastable inorganic materials

(2018) Muratahan Aykol et al.   Science Advances

Machine learning modeling of superconducting critical temperature

(2018) Valentin Stanev et al.   npj Computational Materials

A data-driven statistical model for predicting the critical temperature of a superconductor

(2018) Kam Hamidieh   COMPUTATIONAL MATERIALS SCIENCE

Electron-phonon interactions from first principles

(2017) Feliciano Giustino   REVIEWS OF MODERN PHYSICS

High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory

(2017) Kamal Choudhary et al.   Scientific Reports

Superconducting TiN Films Sputtered Over a Large Range of Substrate DC Bias

(2015) H. M. Iftekhar Jaim et al.   IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

(2015) Scott Kirklin et al.   npj Computational Materials

Pseudopotentials for high-throughput DFT calculations

(2013) Kevin F. Garrity et al.   COMPUTATIONAL MATERIALS SCIENCE

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

(2013) James E. Saal et al.   JOM

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

(2012) Shyue Ping Ong et al.   COMPUTATIONAL MATERIALS SCIENCE

QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials

(2009) Paolo Giannozzi et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Restoring the Density-Gradient Expansion for Exchange in Solids and Surfaces

(2008) John P. Perdew et al.   PHYSICAL REVIEW LETTERS