Results 161 to 170 of about 1,176,061 (314)

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, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Computational methods for the identification of suicidal ideation: a systematic review. [PDF]

Front Artif Intell
Gil Arias BS, Blandón Andrade JC, Sidorov G, Morales-Ríos A.   +3 more
europepmc   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

A comparison of computational methods for expression forecasting. [PDF]

Genome Biol
Kernfeld E, Yang Y, Weinstock JS, Battle A, Cahan P.   +4 more
europepmc   +1 more source

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Kostiantyn Hubaiev, Jörg Waitelonis, Harald Sack   +4 more
wiley   +1 more source

AI-driven computational methods and benchmarking for T-cell antigen identification. [PDF]

Brief Bioinform
Deng Y, Que J, Xue G, Cai Y, Yang W, Wang Y, Hui Y, Wang Z, Lin Y, Zhou W, Xu Z, Jiang Q, Sun H.   +12 more
europepmc   +1 more source

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Editorial: Exploration of the role of heme proteins in biology with experimental and computational methods. [PDF]

Front Chem
Popović DM, Siletsky SA, He C, Gunner MR.   +3 more
europepmc   +1 more source

Combined Process‐ and Phase‐Field‐Simulation to Predict the Microstructure of Single‐Crystal Ni‐Based Superalloys

Advanced Engineering Materials, EarlyView.
A combined finite element and phase‐field approach predicts the evolution of microstructure during the directional solidification of Ni‐based superalloys. The model reveals how withdrawal rate, temperature gradient, and wall thickness control the dendrite spacing, highlighting the strong effect of surface regions in thin sections where dendrite growth ...
Sean Böhm, André B. Baldissera, Rainer Völkl, Uwe Glatzel   +3 more
wiley   +1 more source

Assessment of computational methods in predicting TCR-epitope binding recognition. [PDF]

Nat Methods
Lu Y, Wang Y, Xu M, Xie B, Yang Y, Xu H, Suo S.   +6 more
europepmc   +1 more source