Results 151 to 160 of about 15,712,923 (297)

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

A Bayesian Model Selection Criterion for Selecting Pretraining Checkpoints

Accepted as an ICML 2025 ...
Michael Munn, Susan Wei
openaire   +3 more sources

Context-dependent score based Bayesian information criteria [PDF]

In a number of applications, we argue that standard Bayes factor model comparison and selection may be inappropriate for decision making under specific, utility-based, criteria.
Underhill, Nick, Smith, J. Q.
core  

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics. [PDF]

Genome Biol, 2020
Choi K, Chen Y, Skelly DA, Churchill GA.
europepmc   +1 more source

Forecasting in Large Macroeconomic Panels using Bayesian Model Averaging [PDF]

This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a single model, are given.
Gary Koop, Simon Potter
core  

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Bayesian model selection in linear mixed models for longitudinal data. [PDF]

J Appl Stat, 2020
Ariyo O, Quintero A, Muñoz J, Verbeke G, Lesaffre E.   +4 more
europepmc   +1 more source

Jointness in Bayesian variable selection with applications to growth regression [PDF]

The authors present a measure of jointness to explore dependence among regressors in the context of Bayesian model selection. The jointness measure they propose equals the posterior odds ratio between those models that include a set of variables and the ...
Ley, Eduardo, Steel, Mark F. J.
core  

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

Advanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen, Chao Xu, Jingyue Yu, Qi Wang, Hongze Fang, Shuo Yin, Turab Lookman, Ruirun Chen   +7 more
wiley   +1 more source