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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
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
Two-step machine learning enables optimized nanoparticle synthesis
F Mekki-Berrada, Z Ren, T Huang, WK Wong, F Zheng, J Xie, IPS Tian, ...
npj Computational Materials 7 (1), 55, 2021
AI applications through the whole life cycle of material discovery
J Li, K Lim, H Yang, Z Ren, S Raghavan, PY Chen, T Buonassisi, X Wang
Matter 3 (2), 393-432, 2020
A data fusion approach to optimize compositional stability of halide perovskites
S Sun, A Tiihonen, F Oviedo, Z Liu, J Thapa, Y Zhao, NTP Hartono, ...
Matter 4 (4), 1305-1322, 2021
Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing
Z Liu, N Rolston, AC Flick, TW Colburn, Z Ren, RH Dauskardt, ...
Joule 6 (4), 834-849, 2022
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Z Ren, SIP Tian, J Noh, F Oviedo, G Xing, J Li, Q Liang, R Zhu, AG Aberle, ...
Matter 5 (1), 314-335, 2022
The realistic energy yield potential of GaAs-on-Si tandem solar cells: a theoretical case study
H Liu, Z Ren, Z Liu, AG Aberle, T Buonassisi, IM Peters
Optics express 23 (7), A382-A390, 2015
Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains
Q Liang, AE Gongora, Z Ren, A Tiihonen, Z Liu, S Sun, JR Deneault, ...
npj Computational Materials 7 (1), 188, 2021
Numerical analysis of radiative recombination and reabsorption in GaAs/Si tandem
Z Ren, JP Mailoa, Z Liu, H Liu, SC Siah, T Buonassisi, IM Peters
IEEE Journal of Photovoltaics 5 (4), 1079-1086, 2015
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics
Z Ren, F Oviedo, M Thway, SIP Tian, Y Wang, H Xue, J Dario Perea, ...
npj Computational Materials 6 (1), 9, 2020
Inverse design of crystals using generalized invertible crystallographic representation
Z Ren, J Noh, S Tian, F Oviedo, G Xing, Q Liang, A Aberle, Y Liu, Q Li, ...
arXiv preprint arXiv:2005.07609, 2020
Multi‐fidelity high‐throughput optimization of electrical conductivity in P3HT‐CNT composites
D Bash, Y Cai, V Chellappan, SL Wong, X Yang, P Kumar, JD Tan, ...
Advanced Functional Materials 31 (36), 2102606, 2021
The GaAs/GaAs/Si solar cell–Towards current matching in an integrated two terminal tandem
Z Ren, H Liu, Z Liu, CS Tan, AG Aberle, T Buonassisi, IM Peters
Solar Energy Materials and Solar Cells 160, 94-100, 2017
Fabrication and characterization of single junction GaAs solar cells on Si with As-doped Ge buffer
Y Wang, Z Ren, M Thway, K Lee, SF Yoon, IM Peters, T Buonassisi, ...
Solar Energy Materials and Solar Cells 172, 140-144, 2017
Predicting antimicrobial activity of conjugated oligoelectrolyte molecules via machine learning
A Tiihonen, SJ Cox-Vazquez, Q Liang, M Ragab, Z Ren, NTP Hartono, ...
Journal of the American Chemical Society 143 (45), 18917-18931, 2021
Benchmarking the performance of bayesian optimization across multiple experimental materials science domains. npj Computational Mater. 7
Q Liang, AE Gongora, Z Ren, A Tiihonen, Z Liu, S Sun, JR Deneault, ...
ISSN, 2021
Physics-guided characterization and optimization of solar cells using surrogate machine learning model
Z Ren, F Oviedo, H Xue, M Thway, K Zhang, N Li, JD Perea, M Layurova, ...
2019 IEEE 46th Photovoltaic Specialists Conference (PVSC), 3054-3058, 2019
Predicting the outdoor performance of flat-plate III–V/Si tandem solar cells
H Liu, Z Ren, Z Liu, AG Aberle, T Buonassisi, IM Peters
Solar Energy 149, 77-84, 2017
Autonomous experiments using active learning and AI
Z Ren, Z Ren, Z Zhang, T Buonassisi, J Li
Nature Reviews Materials 8 (9), 563-564, 2023
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