Results 81 to 90 of about 1,062 (170)

Bridging Performance and Fate: A Framework for Sustainable, Composite‐Based Flexible Electronic Systems

Advanced Electronic Materials, EarlyView.
Composite‐based flexible electronics integrate biodegradable polymers, conductive networks, and multilayer device architectures to balance electrical performance, mechanical durability, and controlled degradation. Interfacial engineering and encapsulation regulate transport stability and operational lifetime, while programmed disassembly enables ...
Sayam, Sangho Cho
wiley   +1 more source

Topology, imaging, and emergent phenomena of spin textures. [PDF]

Sci Adv
Rosch A, Pfleiderer C, Fiebig M, Yu X, Tokura Y, Nagaosa N.   +5 more
europepmc   +1 more source

Enhanced Spin‐Reorientation Transition and Polarization in DyFeO3 Thin Films

Advanced Electronic Materials, EarlyView.
Epitaxial strain in antiferromagnetic orthoferrite thin films enhances known bulk properties into a parameter range that makes them more accessible for potential applications. We show that bulk polar properties move from 4 K towards room temperature and the magnetic field‐induced AFM to FM spin‐flip in the paramagnetic Dy lattice when applying small ...
Banani Biswas, Bixin Yan, Morgan Trassin, Dirk Fuchs, Ekatarina V. Pomjakushina, Manfred Fiebig, Thomas Lippert, Christof W. Schneider   +7 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

M polynomial and degree based topological indices of six common synthetic polymers. [PDF]

Sci Rep
George N, Arcot U, Palanisamy BD.
europepmc   +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

How growth-induced stresses guide shape changes during animal morphogenesis: Mechanisms and implications. [PDF]

Semin Cell Dev Biol
Erlich A, Harmansa S.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Magmatic origin of the Dewar magnetic anomaly: Implications for an early lunar dynamo. [PDF]

Sci Adv
Yang X, Mittelholz A, Broquet A, Moorkamp M.   +3 more
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source