Results 51 to 60 of about 109,600 (254)
Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang +3 more
wiley +1 more source
This study explores the effectiveness of prompt optimization techniques for legal case outcome extraction using Large Language Models (LLMs). Two state-of-the-art LLMs, LLaMA3 70b and Mixtral 8x7b, are used in a zero-shot data extraction task on a ...
Guillaume Zambrano
doaj +1 more source
Schematic illustration of LNP‐MPG nuclei‐targeting delivery of HMW‐FGF2 promoting histone acetylation to regulate the fate of DPSCs and treat spinal cord injury. LNPs components include pHMW‐FGF2 plasmid, DSPC, Dlin‐MC3‐DMA, cholesterol, and PEG2000, and are modified with MPG to form HMW‐FGF2@LNP‐MPG (HLM). HLM nuclei‐targets DPSCs to deliver HMW‐FGF2,
Heng Zhou +6 more
wiley +1 more source
Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam +6 more
wiley +1 more source
Large Language Models: А Socio-Philosophical Essay
Neural networks have filled the information space. On the one hand, this indicates the scientific and technological movement of contemporary society (perhaps, AGI is already waiting for us outside the door). On the other hand, in everyday discourse there
Regina V. Penner
doaj +1 more source
Organic Materials of Tomorrow: Horizons of Artificial Intelligence
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
This paper analyses the latest novel by Rocco Tanica, Non siamo mai stati sulla Terra, published by il Saggiatore in 2022, the first Italian novel to be written in collaboration with AI.
Maira Martini
doaj +1 more source
Data‐Driven Materials Science for Energy‐Sustainable Applications
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley +1 more source
Conversing with Robots: Building LLM Assistants to Understand and Utilize Autonomous Systems [PDF]
openQuesta tesi esplora come costruire assistenti conversazionali basati su modelli linguistici di grandi dimensioni (LLM) per facilitare la comprensione e l’utilizzo di sistemi autonomi complessi, come veicoli robotici o imbarcazioni intelligenti.
PIZZATO, FRANCESCO
core
Closed‐Loop Solid‐State Synthesis Planning for Materials Discovery With Large Language Models
Leveraging literature data, we build a large‐language‐model‐driven workflow that extracts synthesis steps from 4407 papers, retrieves similar precedents, and generates candidate solid‐state synthesis recipes. The system benchmarks against ground‐truth and then operates in a closed loop with experiments to synthesize oxy‐selenide electrolyte materials ...
Dong Won Jeon +9 more
wiley +1 more source

