Results 201 to 210 of about 122,064 (264)

Implicit association tests for all: Using iatgen for non-English and offline samples. [PDF]

PLoS One
Santos JO, Do Bú E, Hara T, Mendonça C, Hagá S, Pogacar R, Kouril M.   +6 more
europepmc   +1 more source

A Critical Assessment of Bonding Descriptors for Predicting Materials Properties

Advanced Intelligent Discovery, EarlyView.
The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik, Nidal Dhamrait, Katharina Ueltzen, Christina Ertural, Philipp Benner, Gian‐Marco Rignanese, Janine George   +6 more
wiley   +1 more source

Community Web Portal for Open Collaboration in the Martini Force Field Initiative

Ramirez-Echemendia DP, Borges-Araújo L, Brown CM, Alessandri R, Marrink SJ, Souza PCT, Tieleman DP.   +6 more
europepmc   +1 more source

Community-curated Galaxy interfaces with the Galaxy Labs Engine. [PDF]

Gigascience
Hyde CJ, Syme A, Batut B, Zierep PF, Mok W, Bacon WA, Price GR.   +6 more
europepmc   +1 more source

MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design

Advanced Intelligent Discovery, EarlyView.
MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa, Tejs Vegge, Jes Frellsen   +2 more
wiley   +1 more source

A system-wide snapshot: A multi-campus survey of open source contributors at the University of California. [PDF]

PLoS One
Scarlett VT, Curty RG, Gomez J, Langdon L, Janée G, Budden AE.   +5 more
europepmc   +1 more source

GitHub mania

Polit’bistro , 2013
openaire   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Correction: A data-driven approach to establishing cell motility patterns as predictors of macrophage subtypes and their relation to cell morphology. [PDF]

PLoS One
Kesapragada M, Sun YH, Zhu K, Recendez C, Fregoso D, Yang HY, Rolandi M, Isseroff R, Zhao M, Gomez M.   +9 more
europepmc   +1 more source

Enhancing Atomic‐Resolution in Electron Microscopy: A Deep Learning Denoiser Operating in the Frequency Domain

Advanced Intelligent Systems, EarlyView.
The newly developed AI‐automated Fast Fourier Transform denoising algorithm surpasses conventional real‐space methods by revealing even light atoms otherwise hidden in noisy backgrounds. Atomic resolution electron microscopy has become an essential tool for many scientific fields, when direct visualization of atomic arrangements and defects is needed ...
Ivan Pinto‐Huguet, Marc Botifoll, Xuli Chen, Martin Børstad Eriksen, Jing Yu, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Jordi Arbiol   +8 more
wiley   +1 more source