Follow
A Gilad Kusne
A Gilad Kusne
Materials Measurement & Science Division, NIST & Materials Science & Engineering, UMD
Verified email at umd.edu
Title
Cited by
Cited by
Year
Machine learning in materials science: Recent progress and emerging applications
T Mueller, AG Kusne, R Ramprasad
Reviews in computational chemistry 29, 186-273, 2016
5032016
Machine learning modeling of superconducting critical temperature
V Stanev, C Oses, AG Kusne, E Rodriguez, J Paglione, S Curtarolo, ...
npj Computational Materials 4 (1), 29, 2018
4482018
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
K Choudhary, KF Garrity, ACE Reid, B DeCost, AJ Biacchi, ...
npj computational materials 6 (1), 173, 2020
3342020
On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets
AG Kusne, T Gao, A Mehta, L Ke, MC Nguyen, KM Ho, V Antropov, ...
Scientific reports 4 (1), 6367, 2014
3142014
Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies
ML Green, CL Choi, JR Hattrick-Simpers, AM Joshi, I Takeuchi, SC Barron, ...
Applied Physics Reviews 4 (1), 2017
3132017
On-the-fly closed-loop materials discovery via Bayesian active learning
AG Kusne, H Yu, C Wu, H Zhang, J Hattrick-Simpers, B DeCost, S Sarker, ...
Nature communications 11 (1), 5966, 2020
3082020
Accelerated development of perovskite-inspired materials via high-throughput synthesis and machine-learning diagnosis
S Sun, NTP Hartono, ZD Ren, F Oviedo, AM Buscemi, M Layurova, ...
Joule 3 (6), 1437-1451, 2019
2712019
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
F Oviedo, Z Ren, S Sun, C Settens, Z Liu, NTP Hartono, S Ramasamy, ...
npj Computational Materials 5 (1), 60, 2019
2642019
Autonomous experimentation systems for materials development: A community perspective
E Stach, B DeCost, AG Kusne, J Hattrick-Simpers, KA Brown, KG Reyes, ...
Matter 4 (9), 2702-2726, 2021
2332021
Inkjet printed chemical sensor array based on polythiophene conductive polymers
B Li, S Santhanam, L Schultz, M Jeffries-El, MC Iovu, G Sauvé, J Cooper, ...
Sensors and Actuators B: Chemical 123 (2), 651-660, 2007
2292007
Volatile organic compound detection using nanostructured copolymers
B Li, G Sauvé, MC Iovu, M Jeffries-El, R Zhang, J Cooper, S Santhanam, ...
Nano Letters 6 (8), 1598-1602, 2006
2272006
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics
RK Vasudevan, K Choudhary, A Mehta, R Smith, G Kusne, F Tavazza, ...
MRS communications 9 (3), 821-838, 2019
1572019
Machine-learning guided discovery of a new thermoelectric material
Y Iwasaki, I Takeuchi, V Stanev, AG Kusne, M Ishida, A Kirihara, K Ihara, ...
Scientific reports 9 (1), 2751, 2019
1282019
Perspective: composition–structure–property mapping in high-throughput experiments: turning data into knowledge
JR Hattrick-Simpers, JM Gregoire, AG Kusne
APL Materials 4 (5), 2016
1212016
Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries
Y Iwasaki, AG Kusne, I Takeuchi
npj Computational Materials 3 (1), 1-9, 2017
1022017
Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering
V Stanev, VV Vesselinov, AG Kusne, G Antoszewski, I Takeuchi, ...
npj Computational Materials 4 (1), 43, 2018
1012018
High-throughput determination of structural phase diagram and constituent phases using GRENDEL
AG Kusne, D Keller, A Anderson, A Zaban, I Takeuchi
Nanotechnology 26 (44), 444002, 2015
922015
Scientific AI in materials science: a path to a sustainable and scalable paradigm
BL DeCost, JR Hattrick-Simpers, Z Trautt, AG Kusne, E Campo, ML Green
Machine learning: science and technology 1 (3), 033001, 2020
772020
Artificial intelligence for search and discovery of quantum materials
V Stanev, K Choudhary, AG Kusne, J Paglione, I Takeuchi
Communications Materials 2 (1), 105, 2021
562021
CRYSPNet: Crystal structure predictions via neural networks
H Liang, V Stanev, AG Kusne, I Takeuchi
Physical Review Materials 4 (12), 123802, 2020
522020
The system can't perform the operation now. Try again later.
Articles 1–20