Results 131 to 140 of about 147,580 (306)

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Parameter estimation for allometric trophic network models: A variational Bayesian inverse problem approach

Methods in Ecology and Evolution
Differential equation models are powerful tools for predicting biological systems, capable of projecting far into the future and incorporating data recorded at arbitrary times.
Maria Tirronen, Anna Kuparinen
doaj   +1 more source

Bayesian Inference for PCFGs via Markov Chain Monte Carlo

, 2007
This paper presents two Markov chain Monte Carlo (MCMC) algorithms for Bayesian inference of probabilistic context free grammars (PCFGs) from terminal strings, providing an alternative to maximum-likelihood estimation using Inside-Outside algorithm.
Johnson, Mark, Griffiths, Thomas, Goldwater, Sharon; id_orcid   +2 more
core  

Mechanisms of Alkali Ionic Transport in Amorphous Oxyhalides Solid State Conductors

Advanced Energy Materials, EarlyView.
Large‐scale machine learning‐based molecular dynamics simulations are used to investigate isovalent amorphous oxyhalides, revealing a remarkable chemically independent ionic conductivity. A rigorous analysis of alkali residence times across different metal–anion environments identifies divalent anions as key diffusion bottlenecks.
Luca Binci, KyuJung Jun, Bowen Deng, Gerbrand Ceder   +3 more
wiley   +1 more source

Combining link and content-based information in a Bayesian inference model for entity search

, 2012
An architectural model of a Bayesian inference network to support entity search in semantic knowledge bases is presented. The model supports the explicit combination of primitive data type and object-level semantics under a single computational framework.
Koumenides, Christos, Salvadores, Manuel, Omitola, Temitope, Shadbolt, Nigel   +3 more
core  

The 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering

This reprint presents papers to the 42nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2023, which took place at the Max-Planck-Institute for Plasma Physics in Garching near Munich, Germany ...

core   +1 more source

A Kinetic–Energetic Bottleneck of Charge‐Transfer Injection Governs Energy Loss in Organic Solar Cells

Advanced Energy Materials, EarlyView.
Kinetic–energetic projection of time‐resolved photoluminescence reveals that charge‐transfer injection acts as a universal bottleneck in organic solar cells. A physics‐constrained Bayesian framework identifies an emergent effective CT injection rate governing the trade‐off between charge generation and nonradiative energy loss.
Rong Wang, Tobias Osterrieder, Atul Shukla, Christos L. Chochos, Vasilis G. Gregoriou, Yakun He, Hua Tang, Julian Matthias Haffner‐Schirmer, Karen Forberich, Ning Li, Thomas Heumueller, Frédéric Laquai, Jens Hauch, Safa Shoaee, Dieter Neher, Larry Lüer, Christoph J. Brabec   +16 more
wiley   +1 more source

Cortical hierarchies perform Bayesian causal inference in multisensory perception.

PLoS Biology, 2015
To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources.
Tim Rohe, Uta Noppeney
doaj   +1 more source

Some Remarks on Consistency and Strong Inconsistency of Bayesian Inference

The paper provides new sufficient conditions for consistent and coherent Bayesian inference when a model is invariant under some group of transformations.
Kociecki, Andrzej
core  

Bayesian Inference in the Seemingly Unrelated Regressions Models. [PDF]

The objective of this chapter is to provide a practical guide to computer-aided Bayesian inference for a variety of problems that arise in applications of the SUR model. We describe examples of problems, models and algorithms that have been placed within
Griffiths, W.E.
core