Results 181 to 190 of about 8,521,646 (245)

Emerging Materials and Future Strategies for Solid Oxide Electrochemical Cells

Advanced Energy Materials, EarlyView.
Solid oxide electrochemical cells operate under strongly coupled electrochemical and thermodynamic conditions, where performance is constrained by interactions among crystal structure, defect chemistry, and interfacial evolution. This review, based on a structure‐defect‐property‐durability framework, reveals the roles of lattice symmetry and defect ...
Qiuchun Lu, Yufei Zhang, Han Zhao, Pooi See Lee, Daniel H.C. Chua   +4 more
wiley   +1 more source

Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. [PDF]

J Chem Theory Comput
Gelžinytė E, Öeren M, Segall MD, Csányi G.   +3 more
europepmc   +1 more source

Multiscale Perspectives on Perovskite Instability: From Atomistic Deformation to Device Degradation in Perovskite Solar Cells

Advanced Energy Materials, EarlyView.
Multiscale perspectives on how excess charges in soft lead halide perovskites induce operational instability in photovoltaic devices are presented. Localized carriers formed under thermodynamic non‐equilibrium states modify atomistic interactions and drive lattice distortions, accelerating device degradation.
Joo‐Hong Lee, Donghyeon Lee, Seoyoon Lee, Jin‐Wook Lee, Mansoo Choi, Min‐cheol Kim, Namyoung Ahn   +6 more
wiley   +1 more source

Deciphering Intricacies in Directional CO2 Conversion From Electrolysis to CO2 Batteries

Advanced Energy Materials, EarlyView.
This review will delve into the inherent connections and distinctions of CO2‐directed conversion in ECO2RR and CO2 batteries, in terms of product types, catalyst selection, catalytic mechanisms, and electrochemical performances, while proposing a benchmarking framework for the evaluation of CO2 batteries and innovative CO2 battery configurations for ...
Changfan Xu, Huaping Zhao, Lizhuang Chen, Weidong Shi, Guosheng Shao, Yong Lei   +5 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Multifunctional Matrix‐Activated Electrodes Enabling Efficient Proton Transport and Durable Low‐Pt Operation for High‐Temperature Proton Exchange Membrane Fuel Cells

Advanced Energy Materials, EarlyView.
This work introduces a CeHP‐enabled multifunctional matrix‐activated electrode (MME) for high‐temperature PEM fuel cells, forming an electrochemically and mechanically functional matrix within the catalyst layer. The design increases peak power density by 38% and reduces voltage decay by 62% at 240 °C, while using only 40% of the Pt loading of a ...
Gyeongseok Gwak, Sunjin Park, Joonbyum Kim, Hena Lee, Jihyeon Ok, Chae Young Lee, JunHwa Kwon, Jong Geun Seong, Sung Pil Yoon, Suk‐Woo Nam, Hansung Kim, Katie Heeyum Lim, So Young Lee   +12 more
wiley   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source