Results 121 to 130 of about 309,932 (262)

Structure-Enhanced Stress Attenuation in Magnetically Tunable Microstructures: A Numerical Study of Engineered BCT Lattices. [PDF]

Micromachines (Basel)
Feng KP, Liang CC, Li YH.
europepmc   +1 more source

Bifurcation diagrams for Hamiltonian linear type centers of linear plus cubic homogeneous polynomial vector fields

, 2015
Ilker E. Colak, J. Llibre, C. Valls
semanticscholar   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Basic concepts of grain-boundary structure and phase behavior: From theory and experiments to material properties. [PDF]

MRS Bull
Dillon S, Dehm G.
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

Discovery of Novel Materials with Giant Dielectric Constants via First‐Principles Phonon Calculations and Machine Learning

Advanced Intelligent Discovery, EarlyView.
We discovered novel materials with giant dielectric constants by combining first‐principles phonon calculations and machine learning. Screening 525 perovskites identified six candidates. RbNbO3 was synthesized under pressure and showed ε ≈ 800–1000. This validates our framework as a powerful tool for high‐performance dielectric materials discovery.
Hiroki Moriwake, Kazuki Shitara, Rie Yokoi, Takeharu Kato, Isao Tanaka, Ayako Yamamoto, Kimitoshi Murase, Yosuke Hamasaiki, Yoshitaka Ehara, Mitsuru Itoh   +9 more
wiley   +1 more source

Einstein aggregation operators for multicriteria group decision making in uncertain environments using cubic picture fuzzy sets. [PDF]

Sci Rep
Tanoli MNK, Rafique K, Mahmood Z, Abdalla NSE, Popa IL, Kumar A.   +5 more
europepmc   +1 more source

Accelerating Biosensor Discovery: A Computationally‐Driven Pipeline for Microplastics Monitoring

Advanced Intelligent Discovery, EarlyView.
A computationally guided pipeline unites molecular simulation, synthetic biology, electrochemical engineering, and machine learning to accelerate biosensor discovery. A Bacillus anthracis carbohydrate‐binding module is used to develop a high‐performance micro‐ and nanoplastics sensor with greatly reduced error and variability.
Gabriel X. Pereira, Marcos de C. Leite, Pedro H. M. Zanineli, Juliana N. Y. Costa, Débora M. Cunha, Sophia P. Serra, Pedro H. Sophia, Flávio M. Shimizu, Yasmin W. Moura, Célio C. Oliveira, Gabriel R. Schleder, Gabriela F. Persinoti, Leandro Merces, Renato S. Lima   +13 more
wiley   +1 more source

Magnetostructural Transition in Spin Frustrated Halide Double Perovskites. [PDF]

Chem Mater
Mopoung K, Tao Q, Orlandi F, Mukhuti K, Ramsamoedj KS, Singh U, Khamkaeo S, Zhang M, de Dreu MW, Dilmieva E, Ammerlaan ELQN, Ottenbros T, Wiedmann S, Boothroyd AT, Christianen PCM, Simak SI, Rosen J, Gao F, Buyanova IA, Chen WM, Puttisong Y.   +20 more
europepmc   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source