Results 161 to 170 of about 11,198,121 (288)

Large‐Scale Determination of Frontier Orbital Energies of Disordered Small‐Molecule Organic Semiconductors Using Exciplex Emission Spectra

Advanced Materials, EarlyView.
ABSTRACT Accurately knowing the frontier orbital energies of the structurally disordered small‐molecule organic semiconductors that are used in optoelectronic devices such as organic light‐emitting diodes is required to rationally improve their performance. Here, we show that these energies can be deduced with a large accuracy from the peak energies of
Christian B. McDonald, Hiroki Tomita, Giacomo Cotelli, Louka L. Brüll, Nanno G. de Rooij, Lucas R.L. Norg, Milo Clément, Reinder Coehoorn   +7 more
wiley   +1 more source

Smart Industrial Frameworks Integrating Physics-Informed Machine Learning and Additive Manufacturing for High-Performance Engineering

Physics-informed machine learning, digital twins, and additive manufacturing are a new direction for the creation of intelligent, adaptive, and high-performance engineering systems that are being integrated into a smart industrial framework. The strategy
Fahmina Afrin
core   +1 more source

Phase Engineering of Nanomaterials (PEN): Evolution, Current Challenges, and Future Opportunities

Advanced Materials, EarlyView.
This review summarizes the synthesis, phase transition, advanced characterization spanning ex situ to in situ and operando techniques, and diverse applications of phase engineering of nanomaterials (PEN). It further outlines key challenges and future opportunities, such as phase stability, architecture control, and artificial intelligence (AI)‐driven ...
Ye Chen, Zhanxi Fan, Jingjie Ge, Qiyuan He, Zhuangchai Lai, Jiawei Liu, Chaoliang Tan, Hua Zhang   +7 more
wiley   +1 more source

Physics-Informed Machine Learning for Optimized and Sustainable Biochar Water Treatment. [PDF]

Molecules
Liu Q, Bai B.
europepmc   +1 more source

Accelerated Materials Discovery Through In Situ X‐ray Diffraction: Solvothermal Formation of PdxMy (M═Si, Ge, Sn, Pb) Intermetallic Nanoparticles

Advanced Materials, EarlyView.
In situ PXRD data on solvothermal reactions accelerate materials discovery while simultaneously providing fundamental insights into complex crystal formation processes. Using PdxMy (M = Si, Ge, Sn, Pb) as an example, a general formation mechanism is identified, and specific synthesis strategies for ex situ preparation of SnPd2, Sn13Pd20, and SnPd ...
Anders Bæk Borup, Bo Brummerstedt Iversen   +1 more
wiley   +1 more source

Physics-Informed Machine Learning for Computational Imaging

, 2022
A key aspect of many computational imaging systems, from compressive cameras to low light photography, are the algorithms used to uncover the signal from encoded or noisy measurements. Some computational cameras encode higher-dimensional information (e.g.\ different wavelengths of light, 3D, time) onto a 2-dimensional sensor, then use algorithms to ...
openaire   +1 more source

Demagnetization‐Driven Nanoscale Chirality‐Selective Phonon Transport

Advanced Materials, EarlyView.
Chiral heat flow is switched in a ferromagnet/chiral α‐SiO2 heterostructure by controlling the angular momentum of demagnetization‐driven chiral phonons. When phonon handedness matches the crystal chirality, nanoscale thermal transport is enhanced; the opposite handedness suppresses transport, establishing a magnetization‐tunable chiral phonon diode ...
In Hyeok Choi, Daeheon Kim, Yeon Jong Jin, Seungmo Yang, Tae‐Seong Ju, Changsoo Kim, Chanyong Hwang, Dongbin Shin, Jong Seok Lee   +8 more
wiley   +1 more source

Bridging Physics and Machine Learning: Exploring SEIR Epidemiological Modeling using Physics-Informed Neural Networks

Scientific Machine Learning (SciML) represents a transformative intersection of artificial intelli- gence, physics, and computational science, offering new approaches to solving complex physical sys- tems.
Soni, Abhishek
core  

Physics-Informed Machine Learning for Structural Integrity Assessment of Critical Infrastructure

Structural integrity assessment is essential for ensuring the safety and reliability of critical infrastructure such as bridges, buildings, dams, tunnels, pipelines, and power plants.
John Backus, Grace Hopper
core   +1 more source

Quantitative Prediction of Coal-Gangue Content Using Terahertz Time-Domain Spectroscopy and Physics-Informed Machine Learning. [PDF]

Materials (Basel)
Liu Z, Hu L, Xu J, Yang Y, Li S, Xu Z, Liu L, Li J, Liu H, Ye D.   +9 more
europepmc   +1 more source