Results 71 to 80 of about 38,613 (267)
What Do Large Language Models Know About Materials?
Advanced Engineering Materials, EarlyView.If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer +2 more
wiley +1 more source
Zero shot molecular generation via similarity kernels
Nature CommunicationsGenerative modelling aims to accelerate the discovery of novel chemicals by directly proposing structures with desirable properties. Recently, score-based, or diffusion, generative models have significantly outperformed previous approaches.
Rokas Elijošius +6 more
doaj +1 more source
Generalized Zero Shot Learning via Synthesis Pseudo Features
IEEE Access, 2019 Compared with conventional zero-shot learning (ZSL), generalized ZSL (GZSL) is more challenging because the test instances may come from seen and unseen classes.
Chuanlong Li +5 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch +3 more
wiley +1 more source
ActuatorsPath planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process.
Liwei Mei, Pengjie Xu
doaj +1 more source
Advanced Engineering Materials, EarlyView.The present study investigates recycling of NiTi shape memory alloys via vacuum induction melting. An ingot was synthesized from elemental Ni and Ti and subjected to three subsequent remelting cycles. Remelting increases process durations and impurity levels and adversely affects microstructures and functional properties.
Sakia Sophia Noorzayee +7 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari +5 more
wiley +1 more source
Zero‐shot multi‐label learning via label factorisation
IET Computer Vision, 2019 This study considers the zero‐shot learning problem under the multi‐label setting where each test sample is associated with multiple labels that are unseen in training data.
Hang Shao +3 more
doaj +1 more source
A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
Advanced Engineering Materials, EarlyView.A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle +5 more
wiley +1 more source
Adaptivni Sistemi Avtomatičnogo UpravlinnâThis research presents a comprehensive evaluation of some of the modern computer vision models for grasp type determination in bionic prostheses. The study leverages pretrained models, including CLIP and ResNet, with zero-shot and few-shot learning ...
В. Олійник +1 more
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