Results 161 to 170 of about 12,639 (218)

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Round robin on structure analysis from 3D electron diffraction data. [PDF]

IUCrJ
Gemmi M, Palatinus L, Boullay P, Abrahams JP, Ben Meriem A, Cordero-Oyonarte E, Emerson Agbemeh V, Chintakindi H, Faye Diouf MD, Filipcik P, Gemmrich Hernandéz L, van Genderen E, Hadermann J, Hajizadeh A, Jeriga B, Kolb U, Matinyan S, Passuti S, Santucci M, Suresh A, Pérez O, Tai CW, Vypritskaia A, Wang L, Xu H, Zou X.   +25 more
europepmc   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

Advanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Anisotropic diffraction of materials with fibre symmetry: application to chitin cuticle. [PDF]

IUCrJ
Wang Y, Snow T, Terrill N, Gupta HS.
europepmc   +1 more source

Autonomous AI‐Driven Design for Skin Product Formulations

Advanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang, Yongrui Xiao, Chunlin Chen, Dimitrios Tsaoulidis, Kai Yang, Tao Chen   +5 more
wiley   +1 more source

Legged Locomotion in Lattices: Centipede Traversal of Obstacle-Rich Environments. [PDF]

Ann N Y Acad Sci
Pierce CJ, Soto D, Erickson E, Diaz K, Iaschi M, Lay A, Goldman DI.   +6 more
europepmc   +1 more source

Multimodal Learning with Rashomon Analysis for Battery Discharge Capacity Prediction

Advanced Intelligent Discovery, EarlyView.
Multimodal fusion integrates composition, crystal‐structure, and radial‐distribution descriptors to predict battery discharge capacity. Rashomon analysis across near‐optimal models reveals that explanatory variation is structured rather than arbitrary, separating stable mechanistic signals from model‐contingent attributions and providing a more ...
Jue Gong, Lejia Wang, Tianqing Zhu, Keith Butler, Sichao Li   +4 more
wiley   +1 more source

On the origin of the N c 1 scaling in the confined but chirally symmetric phase at high T. [PDF]

Eur Phys J C Part Fields
Glozman LY.
europepmc   +1 more source

Fluorescent Hydrogel‐Based Strain Sensor With Machine Learning‐Augmented Performance

Advanced Intelligent Discovery, EarlyView.
Fluorescent hydrogel strain sensor based on carbon quantum dots enabling optical readout of deformation through strain‐dependent emission changes, coupled with Random Forest analysis to capture nonlinear fluorescence‐concentration relationships and identify optimal sensing conditions. Hydrogels are ideal matrices for bio‐integrated wearable sensors due
Tailai Chen, Tianlong Liu, Chao Lu, Rimsha Malik, Jin Zhang   +4 more
wiley   +1 more source

Low-Latency Marine-Based OTFS Echo Parameter Estimation Enabled by AI. [PDF]

Sensors (Basel)
Hussain K, Yoo J.
europepmc   +1 more source