Results 201 to 210 of about 1,642,949 (300)

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Reforestation Under Climate Change-A Cross-Regional Deadwood Retention Experiment to Develop Multifunctional Forests on Disturbed Norway Spruce Areas. [PDF]

Ecol Evol
Putzenlechner B, Zwanzig M, Bässler C, Bernhardt-Römermann M, Ebert J, Grieger S, Koal P, Oechler H, Peter D, Profft I, Spaleck S, Steinebrunner F, Tischer A, Thorn S, Wehnert A, Zhou X, Huth F.   +16 more
europepmc   +1 more source

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

Habitat association, forest cover, and region mediate forest disturbance effects on European bird abundances

Bakx T, Pocull G, Plumanns-Pouton E, Aunins A, Boom MP, Brambilla M, Chodkiewicz T, Escandell V, Fontaine B, Fraixedas S, Gamero A, Hallman T, Herrando S, Heywood J, Hristov I, Kačergytė I, Kamp J, Lehikoinen A, Lindström Å, Montanyès M, O'Flaherty C, Øien IJ, Eskildsen DP, Pärt T, Pavón-Jordán D, Ramirez I, Reif J, Rigal S, Sandercock B, Sattler T, Senf C, Soultan A, Strebel N, Szép T, Viana-Soto A, Xu Y, Zavattoni G, Brotons L.   +37 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source

Insect defoliators trading spaces in a hemiboreal forest: Implications for diversity-stability and ecosystem resilience. [PDF]

Ecology
Sturtevant BR, Cooke BJ, Kneeshaw D, Robert LE, James PMA, Fortin MJ, Wolter PT, Townsend PA.   +7 more
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Spider (Araneae) Abundance and Diversity Across Land-Use Gradients. [PDF]

Ecol Evol
Sullivan NJ, Stringer LD, Butler RC, Black A, Waipara N, Vink CJ.   +5 more
europepmc   +1 more source