Results 151 to 160 of about 106,571 (319)

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Named Entity Recognition Models for Machine Learning Interatomic Potentials: A User‐Centric Approach to Knowledge Extraction from Scientific Literature

Advanced Intelligent Discovery, EarlyView.
Named entity recognition pipeline for knowledge extraction from scientific literature. Machine learning interatomic potential (MLIP) is an emerging technique that has helped achieve molecular dynamics simulations with unprecedented balance between efficiency and accuracy. Recently, the body of MLIP literature has been growing rapidly, which propels the
Bowen Zheng, Grace X. Gu
wiley   +1 more source

Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications

Advanced Intelligent Discovery, EarlyView.
Large language models are transforming microbiome research by enabling advanced sequence profiling, functional prediction, and association mining across complex datasets. They automate microbial classification and disease‐state recognition, improving cross‐study integration and clinical diagnostics.
Jieqi Xing, He Wang, Yangyang Sun, Xiaoquan Su, Shunyao Wu   +4 more
wiley   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Linear Concurrent Constraint Programming: Operational and Phase Semantics

, 2001
François Fages, Paul Ruet, Sylvain Soliman   +2 more
openalex   +2 more sources

Selective strictness and parametricity in structural operational semantics, inequationally [PDF]

, 2007
Janis Voigtländer, Patricia Johann
openalex   +1 more source

Caos: A Reusable Scala Web Animator of Operational Semantics (Extended With Hands-On Tutorial) [PDF]

, 2023
José Proença, Luc Edixhoven
openalex   +1 more source

The Necessity of Dynamic Workflow Managers for Advancing Self‐Driving Labs and Optimizers

Advanced Intelligent Discovery, EarlyView.
We assess the maturity and integration readiness of key methodologies for Materials Acceleration Platforms, highlighting the need for dynamic workflow managers. Demonstrating this, we integrate PerQueue into a color‐mixing robot, showing how flexible orchestration improves coordination and optimization.
Simon K. Steensen, Patrick W. V. Butler, Jesper R. Pedersen, Nis Fisker‐Bødker, Tejs Vegge, Jin Hyun Chang, Ivano E. Castelli   +6 more
wiley   +1 more source

Logical Predicates in Higher-Order Mathematical Operational Semantics [PDF]

Sergey Goncharov, Alessio Santamaria, Lutz Schröder, Stelios Tsampas, Henning Urbat   +4 more
openalex   +1 more source

Autonomous Machine Learning‐Based Classification and Arrangement of Submillimeter Objects Using a Capillary Force Gripper

Advanced Intelligent Discovery, EarlyView.
This study presents an automated system integrating a capillary force gripper and machine learning‐based object detection for sorting and placing submillimeter objects. The system achieved stable and simultaneous manipulation of four object types, with an average task time of 86.0 seconds and a positioning error of 157 ± 84 µm, highlighting its ...
Satoshi Ando, Naoto Watanabe, Chihiro Sekine, Shogen Sekiguchi, Ohmi Fuchiwaki   +4 more
wiley   +1 more source