April 19, 2024 - ExploTec

April 2024

“Freezing” intermediate levels for environment friendly and strong FAPbI3 perovskite sun cells

Energy Environ. Sci., 2024, Advance ArticleDOI: 10.1039/D4EE00865K, Paper Muyang Chen, Tingting Niu, Lingfeng Chao, Xiaozheng Duan, Jingpei Wang, Tengfei Pan, Yajing Li, Junhan Zhang, Chenyue Wang, Biyun Ren, Lijuan Guo, Mohammad Hatamvand, Jing Zhang, Qingxun Guo, Yingdong Xia, Xingyu Gao, Yonghua ChenPreparation of low defect density

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Operando Investigations of the Cast Electrolyte Interphase within the Lithium Mediated Nitrogen Aid Response

Energy Environ. Sci., 2024, Accepted ManuscriptDOI: 10.1039/D3EE04235A, Paper Niklas H. Deissler, Jon Bjarke Valbaek V. Mygind, Katja Li, Valerie Niemann, Peter Benedek, Valentin Vinci, Shaofeng Li, Xianbiao Fu, Peter C K Vesborg, Thomas Jaramillo, Jakob Kibsgaard, Jakub Drnec, Ib ChorkendorffThe lithium-mediated nitrogen reduction reaction (Li-NRR)

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Extrapolation Validation (EV): A Common Validation Way for Mitigating System Finding out Extrapolation Chance

Digital Discovery, 2024, Accepted ManuscriptDOI: 10.1039/D3DD00256J, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. Mengxian Yu, Yin-Ning Zhou, Qiang Wang, Fangyou YanMachine Learning (ML) can provide decision-making advice for major challenges in science and engineering, and its

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deepspinn-–-deep-reinforcement-finding-out-for-molecular-construction-prediction-from-infrared-and-13c-nmr-spectra

DeepSPInN – deep reinforcement finding out for molecular construction prediction from infrared and 13C NMR spectra

Digital Discovery, 2024, 3,818-829DOI: 10.1039/D4DD00008K, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Sriram Devata, Bhuvanesh Sridharan, Sarvesh Mehta, Yashaswi Pathak, Siddhartha Laghuvarapu, Girish Varma, U. Deva PriyakumarDeepSPInI is a deep reinforcement learning method that predicts the molecular

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benchmarking-machine-readable-vectors-of-chemical-reactions-on-computed-activation-limitations

Benchmarking machine-readable vectors of chemical reactions on computed activation limitations

Digital Discovery, 2024, Advance ArticleDOI: 10.1039/D3DD00175J, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Puck van Gerwen, Ksenia R. Briling, Yannick Calvino Alonso, Malte Franke, Clemence CorminboeufWe benchmark various methods for the prediction of computed activation barriers on

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evaluating-device-gear-for-optical-chemical-construction-popularity

Evaluating device gear for optical chemical construction popularity

Digital Discovery, 2024, 3,681-693DOI: 10.1039/D3DD00228D, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Aleksei Krasnov, Shadrack J. Barnabas, Timo Boehme, Stephen K. Boyer, Lutz WeberThe extraction of chemical information from images, also known as Optical Chemical Structure Recognition

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egraffbench:-analysis-of-equivariant-graph-neural-community-power-fields-for-atomistic-simulations

EGraFFBench: analysis of equivariant graph neural community power fields for atomistic simulations

Digital Discovery, 2024, 3,759-768DOI: 10.1039/D4DD00027G, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Vaibhav Bihani, Sajid Mannan, Utkarsh Pratiush, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M. Smedskjaer, Sayan Ranu, N. M. Anoop KrishnanEGraFFBench: a framework

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