Results 161 to 170 of about 478 (210)

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

Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction. [PDF]

J Chem Theory Comput
Zia M, Jones B, Feng H, Wei GW, Wei GW.
europepmc   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

Brain dynamics predictive of response to psilocybin for treatment-resistant depression. [PDF]

Brain Commun
Vohryzek J, Cabral J, Lord LD, Fernandes HM, Roseman L, Nutt DJ, Carhart-Harris RL, Deco G, Kringelbach ML.   +8 more
europepmc   +1 more source

Evolutionary Khovanov homology. [PDF]

AIMS Math
Shen L, Liu J, Wei GW.
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

Distinct Neuropsychiatric Profiles Associated With 17p11.2 Deletions and RAI1 Variants in Smith–Magenis Syndrome

American Journal of Medical Genetics Part A, EarlyView.
ABSTRACT Smith–Magenis syndrome (SMS) results from either a recurrent 17p11.2 deletion or pathogenic variants in the retinoic acid induced 1 gene (RAI1). While neurodevelopmental impairment and behavioral dysregulation are well recognized, systematic genotype‐stratified analyses across psychiatric domains remain limited.
Albin Blanc, Christophe Nemos, Céline Bonnet, Mathilde Renaud, Sandra Chapuis, Pauline Boiroux, Marie‐Noëlle Babinet, Caroline Demily   +7 more
wiley   +1 more source

Topological state-space estimation of functional human brain networks. [PDF]

PLoS Comput Biol
Chung MK, Huang SG, Carroll IC, Calhoun VD, Goldsmith HH.   +4 more
europepmc   +1 more source

Groups with many finitary cohomology functors

Kropholler, Peter
core  

Molecular mechanism of ischemic postconditioning in promoting diabetic ischemic brain injury repair via the microRNA‐34a–BDNF–SIX3 signaling axis

Animal Models and Experimental Medicine, EarlyView.
Diabetes combined with ischemic stroke (DMIS) exacerbates brain infarct size and neuronal damage compared to nondiabetic ischemic stroke (IS). This study reveals that microRNA‐34a (miR‐34a) plays a key role in DMIS pathogenesis: miR‐34a directly targets and suppresses brain‐derived neurotrophic factor (BDNF) and Sine oculis homeobox 3 (SIX3), promoting
Ling Zhao, Chunlan Zou, Junxian Li, Yang Yang, Yuanyuan Han, Tingyu Ke   +5 more
wiley   +1 more source