Results 161 to 170 of about 4,789,872 (297)

Chromatin Profiling Reveals Distinct Male and Female Trajectories for Developmental Learning Potential. [PDF]

Dev Neurobiol
Kunzelman GW, Batistuzzo A, London SE.
europepmc   +1 more source

Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential. [PDF]

ACS Cent Sci, 2023
Axelrod S, Shakhnovich E, Gómez-Bombarelli R.   +2 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 foundation machine learning potential with polarizable long-range interactions for materials modelling. [PDF]

Nat Commun
Gao R, Yam C, Mao J, Chen S, Chen G, Hu Z.   +5 more
europepmc   +1 more source

Transferable Deep Learning Potential Reveals Intermediate-Range Ordering Effects in LiF-NaF-ZrF₄ Molten Salt. [PDF]

JACS Au, 2022
Chahal R, Roy S, Brehm M, Banerjee S, Bryantsev V, Lam ST.   +5 more
europepmc   +1 more source

Prediction of Surface Topography Parameters in Direct Laser Interference Patterning of Stainless Steel Using Infrared Monitoring and Convolutional Neural Networks

Advanced Engineering Materials, EarlyView.
This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky, Marcelo Sallese, Leander Kläber, Clemens Kuhn, Keming Du, Andrés Fabián Lasagni   +5 more
wiley   +1 more source

An automated framework for exploring and learning potential-energy surfaces. [PDF]

Nat Commun
Liu Y, Morrow JD, Ertural C, Fragapane NL, Gardner JLA, Naik AA, Zhou Y, George J, Deringer VL.   +8 more
europepmc   +1 more source

Adsorption Sites on Pd Nanoparticles Unraveled by Machine-Learning Potential with Adaptive Sampling. [PDF]

Molecules, 2022
Tereshchenko A, Pashkov D, Guda A, Guda S, Rusalev Y, Soldatov A.   +5 more
europepmc   +1 more source

Zein‐Based Adhesives: Sustainable Extraction and Application in Bioadhesive Technologies

Advanced Engineering Materials, EarlyView.
Zein is extracted from corn gluten meal using a simple and scalable process with high yield (~90%). The resulting protein is applied in bioadhesives modified with Ca2+ and Fe3+ ions, exhibiting substrate‐dependent adhesion. The findings demonstrate competitive bonding performance and highlight the role of ionic interactions in tuning adhesion ...
Paula Bertolino Sanvezzo, Daniela Winsley Valereto Friozi, Vinícius de Oliveira, Marcia Cristina Branciforti   +3 more
wiley   +1 more source

Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytes. [PDF]

Nat Commun
Wang F, Tang YH, Ma ZB, Jin YC, Cheng J.
europepmc   +1 more source