Results 231 to 240 of about 1,401,348 (279)

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

Inverse Design of Molecular Qudits for Quantum Circuitry. [PDF]

Inorg Chem
Latham E, Bowen AM, Cox N, Chilton NF.
europepmc   +1 more source

Aqueous Two‐Phase Bioinks for Discrete Packing and Compartmentalization of 3D Bioprinted Cells

Advanced Healthcare Materials, EarlyView.
Aqueous two‐phase systems (ATPS) enable the formation of biomimetic interfaces crucial for tissue engineering. However, clinical translation remains limited by the challenge of precisely controlling cellular compartmentalization. Here, we developed ATPS biomaterial inks for 3D bioprinting allowing tuneable droplet formation via NaCl modulation.
Martina Marcotulli, Arianna Iacomino, Federico Serpe, Lucia Iafrate, Marco Bastioli, Giorgia Montalbano, Michele D'Orazio, Biagio Palmisano, Silvia Franco, Arianna Mencattini, Roberta Angelini, Eugenio Martinelli, Alessandro Corsi, Mara Riminucci, Giancarlo Ruocco, Chiara Scognamiglio, Andrea Barbetta, Gianluca Cidonio   +17 more
wiley   +1 more source

Quantized Inverse Design for Photonic Integrated Circuits. [PDF]

ACS Omega
Schubert F, Mahlau Y, Bethmann K, Hartmann F, Caspary R, Munderloh M, Ostermann J, Rosenhahn B.   +7 more
europepmc   +1 more source

Inverse design of curved mechanical metamaterials with geometric AI: a generative diffusion operates in compact latent space of cellular structures. [PDF]

NPJ Metamater
Abu-Mualla M, Huang J.
europepmc   +1 more source

Inverse Design of Highly Deformable Mechanical Metamaterial Based on Partitional Semi-Random Optimization. [PDF]

Adv Sci (Weinh)
Cao X, Zhao Z, Wang P, Duan S, Lei H.
europepmc   +1 more source

Inverse design optimization of MIS nanorod arrays to enhance wavelength-selective light extraction of OLEDs through guided SPCE. [PDF]

Nanoscale Adv
Zahin K, Hoque AKMH, Karim MJ, Tsui YY, Islam MZ.   +4 more
europepmc   +1 more source

Exploring AI in metasurface structures with forward and inverse design. [PDF]

iScience
Yang G, Xiao Q, Zhang Z, Yu Z, Wang X, Lu Q.   +5 more
europepmc   +1 more source

Creation of Low-Loss Triple-Ring Optical Filter via Direct Binary Search Inverse Design. [PDF]

Sensors (Basel)
Hu Y, Wang T, Zhou W, Hu B.
europepmc   +1 more source

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Inverse Design via PLS Model Inversion

Ultimately, product formulation requires being able to answer a simple question: what needs to be done in order to guarantee the desired product characteristics? The answer and how to get to it, however, is not as easy to state, and several approaches have been developed in the past to tackle it.
Daniel Palací-López, Joan Borràs-Ferrís, Pierantonio Facco, Massimiliano Barolo, Alberto Ferrer   +4 more
openaire   +1 more source