Results 191 to 200 of about 1,009,382 (298)

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

Advanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

Evolutionary Genomics Unravels the Responses and Adaptation to Climate Change in a Key Alpine Forest Tree Species. [PDF]

Mol Biol Evol
Long Z, Sang Y, Feng J, Zhang X, Shi T, Zhang L, Mao K, Rieseberg LH, Liu J, Wang J.   +9 more
europepmc   +1 more source

Forest trees and forest scenery

, 1901
openaire   +5 more sources

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Editorial: Research advances on forest tree functional genomics and breeding. [PDF]

Front Plant Sci
Du J, Ye T, An Y, Chen Y, Wang J, Wang J, Lu M, Li Q.   +7 more
europepmc   +1 more source

Bayesian Exploration of Metal‐Organic Framework‐Derived Nanocomposites for High‐Performance Supercapacitors

Advanced Intelligent Discovery, EarlyView.
An AI‐assisted approach is introduced to decode synthesis–performance relationships in metal‐organic framework‐derived supercapacitor materials using Bayesian optimization and predictive modeling, streamlining the search for optimal energy storage properties.
David Gryc, Pritam Banerjee, Felipe Jacques Lima Duarte, Joerg R. Jinschek, Premkumar Murugesan, Babak Rezaei, Stephan Sylvest Keller, Roland A. Fischer, Mian Zahid Hussain   +8 more
wiley   +1 more source

Assessing the spatial scale of synchrony in forest tree population dynamics. [PDF]

Proc Biol Sci
Chisholm RA, Fung T, Anderson-Teixeira KJ, Bourg NA, Brockelman WY, Bunyavejchewin S, Chang-Yang CH, Chen YY, Chuyong GB, Condit R, Dattaraja HS, Davies SJ, Ediriweera S, Ewango CEN, Fernando ES, Gunatilleke IAUN, Gunatilleke CVS, Hao Z, Howe RW, Kenfack D, Yao TL, Makana JR, McMahon SM, Mi X, Bt Mohamad M, Myers JA, Nathalang A, Pérez ÁJ, Phumsathan S, Pongpattananurak N, Ren H, Rodriguez LJV, Sukumar R, Sun IF, Suresh HS, Thomas DW, Thompson J, Uriarte M, Valencia R, Wang X, Wolf AT, Zimmerman JK.   +41 more
europepmc   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Low-input breeding potential in stone pine, a multipurpose forest tree with low genome diversity. [PDF]

G3 (Bethesda)
Olsson S, Macaya-Sanz D, Guadaño-Peyrot C, Pinosio S, Bagnoli F, Avanzi C, Vendramin GG, Aletà N, Alía R, González-Martínez SC, Mutke S, Grivet D.   +11 more
europepmc   +1 more source

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source