Results 81 to 90 of about 946,507 (289)

Photoswitchable Conductive Metal–Organic Frameworks

Advanced Functional Materials, EarlyView.
A conductive material where the conductivity can be modulated remotely by irradiation with light is presented. It is based on films of conductive metal–organic framework type Cu3(HHTP)2 with embedded photochromic molecules such as azobenzene, diarylethene, spiropyran, and hexaarylbiimidazole in the pores.
Yidong Liu, Mersad Mostaghimi, Abhinav Chandresh, Saibal Jana, Wolfgang Wenzel, Lars Heinke   +5 more
wiley   +1 more source

Fine-Tuning Quadcopter Control Parameters via Deep Actor-Critic Learning Framework: An Exploration of Nonlinear Stability Analysis and Intelligent Gain Tuning

IEEE Access
Quadcopters have underactuated, nonlinear, and coupled dynamics, making their control a challenging endeavor. However, PID controllers have exhibited remarkable performance for such systems in a variety of circumstances, including obstacle avoidance ...
Hassan Moin, Umer Hameed Shah, Muhammad Jawad Khan, Hasan Sajid   +3 more
doaj   +1 more source

Exploration and Exploitation of New Knowledge Emergence to Improve the Collective Intelligent Decision-Making Level of Web-of-Cells With Cyber-Physical-Social Systems Based on Complex Network Modeling

IEEE Access, 2018
Through exploration and exploitation of new knowledge emergence, the collective intelligent decision-making (CID) level of Web-of-Cells (WoC) proposed by ELECTRA will be dramatically improved.
Lefeng Cheng, Tao Yu
doaj   +1 more source

Adversarial scheduling analysis of Game-Theoretic Models of Norm Diffusion. [PDF]

In (Istrate et al. SODA 2001) we advocated the investigation of robustness of results in the theory of learning in games under adversarial scheduling models.
Istrate, Gabriel, Marathe, Madhav V., Ravi, S.S.   +2 more
core   +1 more source

Selective Benzene Capture by Metal‐Organic Frameworks

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) hold significant potential for capturing benzene from air emissions and hydrocarbon mixtures in liquid phases. This capability stems from their precisely engineered structures, versatile chemistries, and diverse binding interactions.
Zongsu Han, Ankit Mondal, Jiatong Huo, Yihao Yang, Hong‐Cai Zhou   +4 more
wiley   +1 more source

Decoding the Stability of Transition-Metal Alloys with Theory-infused Deep Learning

We introduce an interpretable deep learning framework that predicts the cohesive energy of transition-metal alloys (TMAs) by embedding cohesion theory within graph neural networks (GNNs). Beyond accurate prediction of cohesive energy, a key indicator of thermodynamic stability, the model offers mechanistic insights by disentangling energy contributions
Huang, Yang, Wang, Shih-Han, Cao, Shuyi, Achenie, Luke E. K., Xin, Hongliang   +4 more
openaire   +2 more sources

Electroactive Metal–Organic Frameworks for Electrocatalysis

Advanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska, Yutong Luo, Ruyan Zhao, Jianjun Zhang, Mingchao Wang, Xinliang Feng, Eike Brunner, Stefan Kaskel   +7 more
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Active delta-learning for fast construction of interatomic potentials and stable molecular dynamics simulations

Machine Learning: Science and Technology
Active learning (AL) requires massive time for comprehensive sampling of complex potential energy surfaces to achieve desirable accuracy and stability of machine learning (ML) potentials.
Yaohuang Huang, Yi-Fan Hou, Pavlo O Dral
doaj   +1 more source

Optimal Control Based on Reinforcement Learning for Flexible High-Rise Buildings with Time-Varying Actuator Failures and Asymmetric State Constraints

Buildings
This study centers on the vibration suppression of high-rise building systems under extreme conditions, exploring a reinforcement learning (RL)-based vibration control strategy for flexible building systems with time-varying faults and asymmetric state ...
Min Li, Rui Xie
doaj   +1 more source