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Found 6 papers, 1 papers with code
Construction of Functional Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model
The convergence of materials science and artificial intelligence has unlocked new opportunities for gathering, analyzing, and generating novel materials sourced from extensive scientific literature.
ASCEND: Accurate yet Efficient End-to-End Stochastic Computing Acceleration of Vision Transformer
To overcome the compatibility challenges, ASCEND proposes a novel deterministic SC block for GELU and leverages an SC-friendly iterative approximate algorithm to design an accurate and efficient softmax circuit.
Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation
Data attribution methods trace model behavior back to its training dataset, offering an effective approach to better understand ''black-box'' neural networks.
DARWIN Series: Domain Specific Large Language Models for Natural Science
To add new capabilities in natural science, enabling the acceleration and enrichment of automation of the discovery process, we present DARWIN, a series of tailored LLMs for natural science, mainly in physics, chemistry, and material science.
Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT
The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches.
Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks
The material science literature contains up-to-date and comprehensive scientific knowledge of materials.
