Results 141 to 150 of about 89,896 (247)

Accelerated Screening of Halide Double Perovskites via Hybrid Density Functional Theory and Machine Learning for Thermoelectric Energy Conversion

Advanced Energy and Sustainability Research, EarlyView.
This study integrates hybrid density functional theory, Boltzmann transport theory, and machine learning to accelerate the discovery of lead‐free halide double perovskites for thermoelectric energy conversion. By screening 102 compounds, the authors identify high‐performing candidates such as Rb2GeI6 and Cs2SnBr6, offering a sustainable pathway toward ...
Souraya Goumri‐Said, Ghouti Abdellaoui, Mohammed Benali Kanoun   +2 more
wiley   +1 more source

Superprotonic Conduction in Donor Co‐Doped Perovskites

Angewandte Chemie, EarlyView.
Ceramic proton conductors are promising materials for various applications such as fuel cells. Here, we report superprotonic conduction of BaSc0.8Mo0.1W0.1O2.8 created by donor Mo/W co‐doping into BaScO2.5 (e.g., 10−2 S cm−1 at 193 °C). The high proton conductivity is attributed to high carrier concentration and mobility.
Kensei Umeda, Kei Saito, Takashi Honda, Masatomo Yashima   +3 more
wiley   +2 more sources

Thermal Conductivity of UO₂ with Defects via DFT+U Calculation and Boltzmann Transport Equation. [PDF]

Materials (Basel)
Qin J, Zhao M, Pan R, Tang A, Wu L.
europepmc   +1 more source

Designing Memristive Materials for Artificial Dynamic Intelligence

Advanced Intelligent Discovery, EarlyView.
Key characteristics required of memristors for realizing next‐generation computing, along with modeling approaches employed to analyze their underlying mechanisms. These modeling techniques span from the atomic scale to the array scale and cover temporal scales ranging from picoseconds to microseconds. Hardware architectures inspired by neural networks
Youngmin Kim, Ho Won Jang
wiley   +1 more source

EnsembleDesign: messenger RNA design minimizing ensemble free energy via probabilistic lattice parsing. [PDF]

Bioinformatics
Dai N, Zhou T, Tang WY, Mathews DH, Huang L.   +4 more
europepmc   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Unravel the rotational and translational behavior of a single squirmer in flexible polymer solutions at different Reynolds numbers. [PDF]

Commun Phys
Qi K, Zhou Y, De Corato M, Stratford K, Pagonabarraga I.   +4 more
europepmc   +1 more source

Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Advanced Intelligent Discovery, EarlyView.
This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley   +1 more source

A Pedagogical Reinforcement of the Ideal (Hard Sphere) Gas Using a Lattice Model: From Quantized Volume to Mechanical Equilibrium. [PDF]

Entropy (Basel)
de Miguel R.
europepmc   +1 more source

An Efficient GPU-Accelerated High-Order Upwind Rotated Lattice Boltzmann Flux Solver for Simulating Three-Dimensional Compressible Flows with Strong Shock Waves. [PDF]

Entropy (Basel)
Wang Y, Wang Q, Wang Y.
europepmc   +1 more source