Results 191 to 200 of about 110,431 (303)

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

Advanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

Using PyBioNetFit to leverage qualitative and quantitative data in biological model parameterization and uncertainty quantification. [PDF]

Front Immunol
Miller EF, Mallela A, Neumann J, Lin YT, Hlavacek WS, Posner RG.   +5 more
europepmc   +1 more source

Integrated Field‐Free SOT Domain‐Wall Synapses and MTJ Stochastic Neurons for Hardware Boltzmann Machines

Advanced Functional Materials, EarlyView.
Field‐free spin‐orbit torque domain‐wall synapses integrated with stochastic MTJ neurons enable compact hardware Boltzmann machines. Leveraging intrinsic stochasticity and multi‐level conductance, the system achieves efficient probabilistic learning with high accuracy, demonstrating a scalable spintronic platform for energy‐efficient edge AI.
Aijaz H. Lone, Meng Tang, Camelia Florica, Bin He, Jingkai Xu, Selma Amara, Georgian Melinte, Xixiang Zhang, Gianluca Setti   +8 more
wiley   +1 more source

Exciton Binding Energy of Phosphorescent Emitter Molecules in Organic Light‐Emitting Diodes

Advanced Functional Materials, EarlyView.
Energy level alignment is key to efficient OLED design, yet determining LUMO energies remains challenging. A methodology based on field‐induced dissociation and kinetic Monte Carlo simulations is presented to extract LUMO energies of iridium‐based phosphorescent emitters from their exciton binding energy.
Hiroki Tomita, Lucas R. L. Norg, Patrick Diederen, Clint van Hoesel, Christ H. L. Weijtens, Peter A. Bobbert, Reinder Coehoorn   +6 more
wiley   +1 more source

Physics-Informed Emulation of Systemic Circulation for Fast Parameter Estimation and Uncertainty Quantification. [PDF]

Int J Numer Method Biomed Eng
Ryan W, Taylor-LaPole A, Olufsen M, Husmeier D, Vyshemirskiy V.   +4 more
europepmc   +1 more source

In Situ Crystalline‐Amorphous Composite Oxide Structure Suppressing Thermal Wear Instability for Refractory Multi‐Principal Element Alloy

Advanced Functional Materials, EarlyView.
An effective strategy is developed to overcome the medium temperatures wear resistance degradation in refractory multi‐principal element alloys. By utilizing chemical short‐range ordering to trigger oxygen‐induced amorphization, a composite oxide structure is formed, which combines high hardness with robust stiffness.
Xuhui Pei, Jianbao Zhang, Hanming Wang, Yin Du, Tao Yang, Ji‐Jung Kai, Benyebka Bou‐Saïd, Haifeng Wang   +7 more
wiley   +1 more source

An evaluation of uncertainty quantification methods and measures for deep learning outcome prediction models in head and neck cancer radiotherapy. [PDF]

Phys Imaging Radiat Oncol
MacRae DC, van der Hoek L, van Aalst JE, de Vette SPM, van der Wal R, Neh H, Ma B, Sijtsema NM, Valdenegro-Toro MA, van Ooijen PMA, van Dijk LV.   +10 more
europepmc   +1 more source

Ultrasmall High‐Entropy Materials: Nanoscale Effects, Synthesis, and Mechanistic Insights

Advanced Functional Materials, EarlyView.
This review article focuses on sub‐10 nm high‐entropy materials that combine nanoscale design with complex compositions for next‐generation applications. ABSTRACT Ultrasmall high‐entropy nanomaterials (USHENMs, <10 nm) merge multicomponent chemistry with size‐dependent effects, forming a distinct class of materials with unprecedented properties.
Yueyue He, Wenbo Wang, Simon Schweidler, Yanjiao Ma, Ben Breitung, Torsten Brezesinski   +5 more
wiley   +1 more source

Uncertainty quantification [PDF]

, 2014
openaire   +1 more source

Thermally Pre‐Formed Reconfigurable Resistive Random‐Access Memory Crossbar Arrays: A Dual‐Mode Platform for Robust Physically Unclonable Functions and In‐Memory Computing

Advanced Functional Materials, EarlyView.
A reconfigurable RRAM platform utilizing thermally pre‐formed filaments (TPFs) is developed to realize robust hardware security. By exploiting the thermodynamic stochasticity of TPFs, exceptionally reliable physically unclonable functions (PUFs) are achieved.
Seongbin Kwon, Taewon Lee, Dohyeong Kim, Sungmin Hwang, Sungjoon Kim   +4 more
wiley   +1 more source