Results 221 to 230 of about 882,341 (296)

Comprehensive evaluation of agronomic, processing, nutritional and functional properties of 22 main-cultivated adzuki bean varieties in China and identification of elite varieties for different applications. [PDF]

Front Plant Sci
Zhang L, Shi Z, Yin C, Jia F, Li Y, Zhou H, Cui Q, Li J, Li D, Zhang Y.   +9 more
europepmc   +1 more source

FIRE‐GNN: Force‐Informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties

Advanced Intelligent Discovery, EarlyView.
This study introduces FIRE‐GNN, a force‐informed, relaxed equivariant graph neural network for predicting surface work functions and cleavage energies from slab structures. By incorporating surface‐normal symmetry breaking and machine learning interatomic potential‐derived force information, the approach achieves state‐of‐the‐art accuracy and enables ...
Circe Hsu, Claire Schlesinger, Karan Mudaliar, Jordan Leung, Robin Walters, Peter Schindler   +5 more
wiley   +1 more source

Effect of Pumpkin Seed Protein Concentrate and Xanthan Gum on the Properties of Gluten-Free Frozen Batter and the Resulting Cake. [PDF]

Food Technol Biotechnol
Malekitabrizi H, Aalami M.
europepmc   +1 more source

Evolution of Physical Intelligence Across Scales

Advanced Intelligent Discovery, EarlyView.
By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu, Tianyun Huang, Aming Li, Pengyu Lv, Tingting Qin, Wenhui Chen, Hui Wei, Huiling Duan   +7 more
wiley   +1 more source

Utilizing Defatted Hulless Pumpkin Seed Meal for Bread Fortification: A Valorization Approach to Improve Techno-functional and Textural properties. [PDF]

Sci Rep
Raina S, Joshi T, Singh A, Dhatt AS, Sharma M.   +4 more
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

Extruded and Enzyme-Fractionated Avocado (<i>Persea americana</i> Mill.) Seed Flour as an Ingredient for Frankfurter-Type Sausages: Technological, Physicochemical, and Sensory Implications. [PDF]

Foods
Jaramillo-De la Garza JS, Pérez-Carrillo E, Hernández-Brenes C, Rodríguez-Sánchez DG, Heredia-Olea E.   +4 more
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Physiological and molecular mechanisms underlying the quality changes of lotus seeds during different ripening periods. [PDF]

Front Plant Sci
Chen W, Yang S, Song Y, Chen M, Zhong L, Yao F, Wei Y, Wu Z, Lin F, Qiu Y.   +9 more
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