Results 181 to 190 of about 11,580 (259)

Advancing Energy Materials by In Situ Atomic Scale Methods

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
Progress in in situ atomic scale methods leads to an improved understanding of new and advanced energy materials, where a local understanding of complex, inhomogeneous systems or interfaces down to the atomic scale and quantum level is required. Topics from photovoltaics, dissipation losses, phase transitions, and chemical energy conversion are ...
Christian Jooss, Michael Seibt, Martin Wenderoth, Oliver Bünermann, Ole Bunjes, Till Domröse, Christian Eckel, Francesca Falorsi, Christoph Flathmann, Monica Kolek Martinez de Azagra, Matthias Krüger, Jonas Lindner, Tobias Meyer, Claus Ropers, Ulrich Ross, Kai Rossnagel, Sreeju Sreekantan Nair Lalithambika, Simone Techert, Georg A. Traeger, Cynthia Volkert, R. Thomas Weitz, Alec M. Wodtke   +21 more
wiley   +1 more source

Chiral Molecular Configuration‐Mediated Crystallization of Perovskites for High‐Performance Solar Cells

Advanced Energy Materials, EarlyView.
In this work, we propose a chiral stereo‐anchoring strategy to modulate the colloidal evolution and crystallization behavior of FAPbI3 perovskites via stereochemical molecular design, which effectively regulates nucleation kinetics and promotes ordered crystal growth.
Runkang Wang, Yujie Zhu, Jiaxue You, Yunfan Wang, Jiarong Wu, Jinghao Ge, Pengda Tong, Shilong Jia, Xiaogang Wang, Yiran Tao, Hao‐Chung Kuo, Adel Najar, Lu Zhang, Yuanhao Gong, Shengzhong (Frank) Liu   +14 more
wiley   +1 more source

Chiral Recognition of L- and D- Amino Acid by Porphyrin Supramolecular Aggregates. [PDF]

Molecules, 2018
Randazzo R, Gaeta M, Gangemi CMA, Fragalà ME, Purrello R, D'Urso A.   +5 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Importance of Chiral Recognition in Designing Metal-Free Ligands for G-Quadruplex DNA. [PDF]

Molecules, 2019
Răsădean DM, Harrison SWO, Owens IR, Miramont A, Bromley FM, Pantoș GD.   +5 more
europepmc   +1 more source

Artificial Intelligence‐Driven Network Pharmacology: A Methodological Paradigm Shift Bridging Traditional Wisdom and Modern Science

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence is redefining network pharmacology (NP). By integrating knowledge graph engineering, geometric deep learning, multiomics anchoring, and generative reasoning, AI‐driven NP (AI‐NP) transforms static target mapping into dynamic, predictive modeling.
Cong Wang, Sile Hu, Rigahai Ji, Riguna A, Siqige Hao, Ma Zhuo, Yi‐Hung Chen, Bisihalatu Hu, Ulana Bai, Xigui Hada   +9 more
wiley   +1 more source

Materials Representation Learning Based on a Material–Motif Network and Heterogeneous Graphs

Advanced Intelligent Discovery, EarlyView.
Structure motifs in materials are used to construct a bipartite material–motif network that links each material to its constituent motifs and establishes connectivity among materials sharing common motifs. Network analysis reveals material clusters associated with different functional applications and supports motif‐guided screening of materials.
Anoj Aryal, Weiyi Gong, Huta Banjade, Qimin Yan   +3 more
wiley   +1 more source

Smart Bioinspired Material‐Based Actuators: Current Challenges and Prospects

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This work gathers, in a review style, an extensive and comprehensive literature overview on the development of autonomous actuators based on synthetic materials, bringing together valuable knowledge from several studies. Furthermore, the article identifies the fundamental principles of actuation mechanisms and defines key parameters to address the size
Alejandro Palacios, Léon Chiriatti, Simon Poppinga, Thomas Speck, Vincent Le Houérou   +4 more
wiley   +1 more source