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Data-driven approaches for material discovery and design have been accelerated by emerging efforts in machine learning.
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Data-driven review of thermoelectric materials: performance and resource considerations
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Python materials genomics (pymatgen): A robust, open-source python library for materials analysis
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Hastagiri P Vanchinathan, Isidor Nikolic, Fabio De Bona, and Andreas Krause · 2014
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The netflix recommender system: Algorithms, business value, and innovation
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Review of recent progress in chemical stability of perovskite solar cells
Guangda Niu, Xudong Guo, and Liduo Wang · 2015
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Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties
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Thermoelectric properties of tis2 mechanically alloyed compounds
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Recent advances in improving the stability of perovskite solar cells
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Epw: Electron–phonon coupling, transport and superconducting properties using maximally localized wannier functions
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Universal fragment descriptors for predicting properties of inorganic crystals
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Discovery of high-performance low-cost n-type mg3sb2-based thermoelectric materials with multi-valley conduction bands
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Ultralow thermal conductivity in all-inorganic halide perovskites
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High-throughput search of ternary chalcogenides for p-type transparent electrodes
Jingming Shi, Tiago FT Cerqueira, Wenwen Cui, Fernando Nogueira, Silvana Botti, and Miguel AL Marques · 2017
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Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
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Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity
Nils ER Zimmermann and Anubhav Jain · 2020
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Jiaxing Qu, Vladan Stevanovic, Elif Ertekin, and Prashun Gorai · 2020
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Symmetry breaking induced anisotropic carrier transport and remarkable thermoelectric performance in mixed halide perovskites cspb (i1–x br x) 3
Lifu Yan, Mingchao Wang, Chenxi Zhai, Lingling Zhao, and Shangchao Lin · 2020
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Effects of multi-scale defects on the thermoelectric properties of delafossite cucr1-xmgxo2 materials
Dung Van Hoang, Anh Tuan Thanh Pham, Hanh Kieu Thi Ta, Truong Huu Nguyen, Ngoc Kim Pham, Lai Thi Hoa, Vinh Cao Tran, Ohtaki Michitaka, Quang Minh Nhat Tran, Jong-Ho Park, et al · 2020
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Machine learning for high performance organic solar cells: current scenario and future prospects
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Phase boundary mapping to obtain n-type mg3sb2-based thermoelectrics
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Atomistic line graph neural network for improved materials property predictions
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Defect chemistry and doping of bicuseo
Michael Y Toriyama, Jiaxing Qu, G Jeffrey Snyder, and Prashun Gorai · 2021
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Predicting thermoelectric properties from chemical formula with explicitly identifying dopant effects
Gyoung S Na, Seunghun Jang, and Hyunju Chang · 2021
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Self-supervised graph-level representation learning with local and global structure
Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, and Jian Tang · 2021
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Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data
Vishu Gupta, Kamal Choudhary, Francesca Tavazza, Carelyn Campbell, Wei-keng Liao, Alok Choudhary, and Ankit Agrawal · 2021
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Crystal diffusion variational autoencoder for periodic material generation
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Efficient calculation of carrier scattering rates from first principles
Alex M Ganose, Junsoo Park, Alireza Faghaninia, Rachel Woods-Robinson, Kristin A Persson, and Anubhav Jain · 2021
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Periodic graph transformers for crystal material property prediction
Keqiang Yan, Yi Liu, Yuchao Lin, and Shuiwang Ji · 2022
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Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science
Amalie Trewartha, Nicholas Walker, Haoyan Huo, Sanghoon Lee, Kevin Cruse, John Dagdelen, Alexander Dunn, Kristin A Persson, Gerbrand Ceder, and Anubhav Jain · 2022
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Matscibert: A materials domain language model for text mining and information extraction
Tanishq Gupta, Mohd Zaki, and NM Anoop Krishnan · 2022
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Significantly enhanced thermoelectric performance achieved in cugate2 through dual-element permutations at cation sites
Mengyue Wu, Lujun Zhu, Shixuan Liu, Mingzhen Song, Fudong Zhang, Pengfei Liang, Xiaolian Chao, Zupei Yang, Jiaqing He, and Di Wu · 2022
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Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework
Rees Chang, Yu-Xiong Wang, and Elif Ertekin · 2022
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A public database of thermoelectric materials and system-identified material representation for data-driven discovery
Gyoung S Na and Hyunju Chang · 2022
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Study of lead-free double perovskites halides cs2ticl6, and cs2tibr6 for optoelectronics, and thermoelectric applications
Q Mahmood, M Hassan, N Yousaf, Abeer A AlObaid, Tahani I Al-Muhimeed, Manal Morsi, Hind Albalawi, and Osama A Alamri · 2022
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First-principles prediction of the ground-state crystal structure of double-perovskite halides cs2agcrx6 (x= cl, br, and i)
Muhammad Saeed, Izaz Ul Haq, Awais Siddique Saleemi, Shafiq Ur Rehman, Bakhtiar Ul Haq, Aijaz Rasool Chaudhry, and Imad Khan · 2022
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First-principles calculations to investigate structural, magnetic, optical, electronic and thermoelectric properties of x2mgs4 (x= gd, tm) spinel sulfides
Mubashar Nazar, Shatha A Aldaghfag, Muhammad Yaseen, Mudassir Ishfaq, Rasheed Ahmad Khera, Saima Noreen, Magda H Abdellattif, et al · 2022
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Contrastive representation learning of inorganic materials to overcome lack of training datasets
Gyoung S Na and Hyun Woo Kim · 2022
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Data-driven discovery of 2d materials by deep generative models
Peder Lyngby and Kristian Sommer Thygesen · 2022
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A thermoelectric materials database auto-generated from the scientific literature using chemdataextractor
Odysseas Sierepeklis and Jacqueline M Cole · 2022
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Structure feature vectors derived from robocrystallographer text descriptions of crystal structures using word embeddings
Hasan M Sayeed, Sterling G Baird, and Taylor D Sparks · 2023
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