Page 105 - Read Online
P. 105

Shu et al. J. Mater. Inf. 2025, 5, 36  https://dx.doi.org/10.20517/jmi.2025.13  Page 29 of 31

               96.       Goodfellow, I. J.; Pouget-Abadie, J.; Mirza, M.; et al. Generative adversarial networks. arXiv 2014, arXiv:1406.2661. https://doi.org/
                    10.48550/arXiv.1406.2661. (accessed 27 May 2025)
               97.       Zheng, Z.; Zhang, O.; Borgs, C.; Chayes, J. T.; Yaghi, O. M. ChatGPT chemistry assistant for text mining and the prediction of MOF
                    synthesis. J. Am. Chem. Soc. 2023, 145, 18048-62.  DOI  PubMed  PMC
               98.       OpenAI: Optimizing language models for dialogue. 2023. https://openai.com/blog/chatgpt/. (accessed 27 May 2025).
               99.       Chowdhary, K. R. Natural language processing. In: Fundamentals of artificial intelligence. New Delhi: Springer India; 2020. pp. 603-
                    49.  DOI
               100.      Zhu, J. J.; Yang, M.; Ren, Z. J. Machine learning in environmental research: common pitfalls and best practices. Environ. Sci.
                    Technol. 2023, 57, 17671-89.  DOI  PubMed
               101.      Li, Z.; Yoon, J.; Zhang, R.; et al. Machine learning in concrete science: applications, challenges, and best practices. npj. Comput.
                    Mater. 2022, 8, 810.  DOI
               102.      Artrith, N.; Butler, K. T.; Coudert, F. X.; et al. Best practices in machine learning for chemistry. Nat. Chem. 2021, 13, 505-8.  DOI
               103.      Wong, T. Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern. Recognit. 2015,
                    48, 2839-46.  DOI
               104.      Efron, B.; Tibshirani, R. J. An introduction to the bootstrap. Chapman and Hall/CRC: 1994. https://www.hms.harvard.edu/bss/neuro/
                    bornlab/nb204/statistics/bootstrap.pdf. (accessed 27 May 2025).
               105.      Palanivinayagam, A.; El-Bayeh, C. Z.; Damaševičius, R. Twenty years of machine-learning-based text classification: a systematic
                    review. Algorithms 2023, 16, 236.  DOI
               106.      Sebastiani, F. Machine learning in automated text categorization. ACM. Comput. Surv. 2002, 34, 1-47.  DOI
               107.      Chicco, D.; Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary
                    classification evaluation. BMC. Genomics. 2020, 21, 6.  DOI  PubMed  PMC
               108.      Ho, S. Y.; Phua, K.; Wong, L.; Bin, G. W. W. Extensions of the external validation for checking learned model interpretability and
                    generalizability. Patterns 2020, 1, 100129.  DOI  PubMed  PMC
               109.      Xiong, Z.; Cui, Y.; Liu, Z.; Zhao, Y.; Hu, M.; Hu, J. Evaluating explorative prediction power of machine learning algorithms for
                    materials discovery using k-fold forward cross-validation. Comput. Mater. Sci. 2020, 171, 109203.  DOI
               110.      Probst, P.; Bischl, B.; Boulesteix, A. L. Tunability: importance of hyperparameters of machine learning algorithms. arXiv 2018,
                    arXiv:1802.09596. https://doi.org/10.48550/arXiv.1802.09596. (accessed 27 May 2025)
               111.      Bischl, B.; Binder, M.; Lang, M.; et al. Hyperparameter optimization: foundations, algorithms, best practices, and open challenges.
                    WIREs. Data. Min. Knowl. 2023, 13, e1484.  DOI
               112.      Li, L.; Jamieson, K.; DeSalvo, G.; Rostamizadeh, A.; Talwalkar, A. Hyperband: a novel bandit-based approach to hyperparameter
                    optimization. arXiv 2016, arXiv:1603.06560. https://doi.org/10.48550/arXiv.1603.06560. (accessed 27 May 2025)
               113.      Victoria, A. H.; Maragatham, G. Automatic tuning of hyperparameters using Bayesian optimization. Evol. Syst. 2021, 12, 217-23.
                    DOI
               114.      Sa, B.; Hu, R.; Zheng, Z.; et al. High-throughput computational screening and machine learning modeling of Janus 2D III-VI van der
                    Waals heterostructures for solar energy applications. Chem. Mater. 2022, 34, 6687-701.  DOI
               115.      Mooraj, S.; Chen, W. A review on high-throughput development of high-entropy alloys by combinatorial methods. J. Mater. Inf.
                    2023, 3, 4.  DOI
               116.      Sa, Z.; Liu, F.; Zhuang, X.; et al. Toward high bias-stress stability P-type GaSb nanowire field-effect-transistor for gate-controlled
                    near-infrared photodetection and photocommunication. Adv. Funct. Mater. 2023, 33, 2304064.  DOI
               117.      Kang, Y.; Hou, X.; Zhang, Z.; et al. Ultrahigh-performance and broadband photodetector from visible to shortwave infrared band
                    based on GaAsSb nanowires. Chem. Eng. J. 2024, 501, 157392.  DOI
               118.      Kang, Y.; Hou, X.; Zhang, Z.; et al. Enhanced visible-NIR dual-band performance of GaAs nanowire photodetectors through phase
                    manipulation. Adv. Opt. Mater.2025, 2500289.  DOI
               119.      Li, D.; Lan, C.; Manikandan, A.; et al. Ultra-fast photodetectors based on high-mobility indium gallium antimonide nanowires. Nat.
                    Commun. 2019, 10, 1664.  DOI  PubMed  PMC
               120.      Gao, Y.; Zhang, Q.; Hu, W.; Yang, J. First-principles computational screening of two-dimensional polar materials for photocatalytic
                    water splitting. ACS. Nano. 2024, 18, 19381-90.  DOI
               121.      Kangsabanik, J.; Svendsen, M. K.; Taghizadeh, A.; Crovetto, A.; Thygesen, K. S. Indirect band gap semiconductors for thin-film
                    photovoltaics: high-throughput calculation of phonon-assisted absorption. J. Am. Chem. Soc. 2022, 144, 19872-83.  DOI  PubMed
               122.      Jiang, X.; Yin, W. High-throughput computational screening of oxide double perovskites for optoelectronic and photocatalysis
                    applications. J. Energy. Chem. 2021, 57, 351-8.  DOI
               123.      Tang, J.; Xue, J.; Xu, H.; et al. Power generation density boost of bifacial tandem solar cells revealed by high throughput
                    optoelectrical modelling. Energy. Environ. Sci. 2024, 17, 6068-78.  DOI
               124.      Xie, T.; Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material
                    properties. Phys. Rev. Lett. 2018, 120, 145301.  DOI  PubMed
               125.      Liang, C.; Rouzhahong, Y.; Ye, C.; Li, C.; Wang, B.; Li, H. Material symmetry recognition and property prediction accomplished by
                    crystal capsule representation. Nat. Commun. 2023, 14, 5198.  DOI  PubMed  PMC
               126.      Mannodi-Kanakkithodi, A.; Toriyama, M. Y.; Sen, F. G.; Davis, M. J.; Klie, R. F.; Chan, M. K. Y. Machine-learned impurity level
                    prediction for semiconductors: the example of Cd-based chalcogenides. npj. Comput. Mater. 2020, 6, 296.  DOI
   100   101   102   103   104   105   106   107   108   109   110