Results 191 to 200 of about 278,341 (297)

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

open access: yesAdvanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar   +3 more
wiley   +1 more source

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

open access: yesAdvanced Intelligent Discovery, EarlyView.
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali   +5 more
wiley   +1 more source

Smart Bioinspired Material‐Based Actuators: Current Challenges and Prospects

open access: yesAdvanced 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   +4 more
wiley   +1 more source

Prediction of One‐Dimensional Metallicity and π‐Band Superconductivity in Rhodizonate Radical Pancakes

open access: yesAngewandte Chemie, EarlyView.
Equidistant stacking of radicals is typically unstable due to symmetry‐lowering distortions. We predict that such arrangements can be stabilized by multicentered covalent (“pancake”) bonding and Coulombic repulsion between negatively charged radicals.
Alvaro Lobato   +2 more
wiley   +2 more sources

R‐APEX: A Knowledge Graph–Based Platform for the Elucidation of the Toxicological Mechanisms of Ambient Particulate Matter

open access: yesAdvanced Intelligent Systems, EarlyView.
R‐APEX is a knowledge graph platform developed to investigate how air pollutants such as particularly fine particulate matter (PM2.5) affect human health. By integrating large‐scale biomedical data and using machine learning, it reveals pollutant–gene–disease associations.
Zhixing Zhu   +7 more
wiley   +1 more source

Submolecular‐Resolution Probing of Vibrational Anharmonicity Using Tip‐Enhanced Raman Spectroscopy

open access: yesAngewandte Chemie, EarlyView.
This work explores how tip‐enhanced Raman spectroscopy achieves submolecular resolution in detecting vibrational overtones and combination bands. This breakthrough reveals site‐specific anharmonicities and energy transfer pathways within single molecules, advancing chemical analysis with unprecedented spatial and spectral detail.
Youngwook Park   +3 more
wiley   +2 more sources

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