Results 51 to 60 of about 529,167 (319)

Active Learning‐Accelerated Discovery of Fibrous Hydrogels with Tissue‐Mimetic Viscoelasticity

Advanced Functional Materials, EarlyView.
Active learning accelerates the design of fibrous hydrogels that mimic the viscoelasticity of native tissues. By integrating multi‐objective optimization and closed‐loop experimentation, this approach efficiently identifies optimal formulations from thousands of possibilities and decouples elasticity and viscosity. The resulting hydrogels offer tunable
Zhengkun Chen, Zheyuan Miao, Gary Tom, Austin H. Cheng, Elizaveta Gusarova, Benjamen Zondag, Tianyi Wu, Dianoosh Kalhori, Boris Hinz, Milad Abolhasani, Alán Aspuru‐Guzik, Eugenia Kumacheva   +11 more
wiley   +1 more source

Evaluation of a Bayesian inference network for ligand-based virtual screening

Journal of Cheminformatics, 2009
Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query.
Chen Beining, Mueller Christoph, Willett Peter   +2 more
doaj   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Bayesian topology identification of linear dynamic networks

, 2019
In networks of dynamic systems, one challenge is to identify the interconnection structure on the basis of measured signals. Inspired by a Bayesian approach in [1], in this paper, we explore a Bayesian model selection method for identifying the ...
chickering, hendrickx, kuppinger, ljung
core   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion

Remote Sensing, 2019
Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered.
Yu Li, Sandro Martinis, Marc Wieland, Stefan Schlaffer, Ryo Natsuaki   +4 more
doaj   +1 more source

Superionic Amorphous Li2ZrCl6 and Li2HfCl6

Advanced Materials, EarlyView.
Amorphous Li2HfCl6 and L2ZrCl6 are shown to be promising solid‐state electrolytes with predicted ionic conductivities >20 mS·cm−1. Molecular dynamics simulations with machine‐learning force fields reveal that anion vibrations and flexible MCl6 octahedra soften the Li coordination cage and enhance mobility. Correlation between Li‐ion diffusivity and the
Shukai Yao, De‐en Jiang
wiley   +1 more source

Interference Effects in Quantum Belief Networks

, 2014
Probabilistic graphical models such as Bayesian Networks are one of the most powerful structures known by the Computer Science community for deriving probabilistic inferences.
Moreira, Catarina, Wichert, Andreas
core   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Bayesian network learning with cutting planes [PDF]

, 2011
The problem of learning the structure of Bayesian networks from complete discrete data with a limit on parent set size is considered. Learning is cast explicitly as an optimisation problem where the goal is to find a BN structure which maximises log ...
Cussens, James
core   +1 more source