Results 131 to 140 of about 51,825 (244)

Combinatorial asymmetric acoustic metamaterials with real-time programmability. [PDF]

Proc Natl Acad Sci U S A
Keogh MR, Bilal OR.
europepmc   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Higher Dimensionality in the Mg-Co-B System: Synthesis and Structure of Incommensurate Composite Mg_1+εCo₄B₄. [PDF]

J Am Chem Soc
Oftedahl P, Bhaskar G, Parvez NJ, Zhang Z, Sun Y, Xiao JQ, Proffen T, Lebedev OI, Zaikina JV.   +8 more
europepmc   +1 more source

The Interoperability Challenge in DFT Workflows Across Implementations

Advanced Intelligent Discovery, EarlyView.
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen, Tushar Singh Thakur, Manuel Dillenz, Johan M. Carlsson, Celso R. C. Rêgo, Eibar Flores, Hamidreza Hajiyani, Felix Hanke, Juan María García Lastra, Wolfgang Wenzel, Nicola Marzari, Tejs Vegge, Giovanni Pizzi, Ivano E. Castelli   +13 more
wiley   +1 more source

European supercell thunderstorms-A prevalent current threat and an increasing future hazard. [PDF]

Sci Adv
Feldmann M, Blanc M, Brennan KP, Thurnherr I, Velasquez P, Martius O, Schär C.   +6 more
europepmc   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Realization of a Bilayer Elastic Topological Insulator. [PDF]

Adv Sci (Weinh)
Ma C, Song Z, Cheng Z, Wu JH, Zhang B.
europepmc   +1 more source

Predicting Crystal Structures and Ionic Conductivities in Li3 YCl6−x Brx Halide Solid Electrolytes Using a Fine‐Tuned Machine Learning Interatomic Potential

Advanced Intelligent Systems, EarlyView.
This study refines the Crystal Hamiltonian Graph Network to predict energies, structures, and lithium‐ion dynamics in halide electrolytes. By generating ordered structural models and using an iterative fine‐tuning workflow, we achieve near‐ab initio accuracy for phase stability and ionic transport predictions.
Jonas Böhm, Aurélie Champagne
wiley   +1 more source

Molecular Insights of 7‑Azaindole Drugs and Their Intercalation in the Confined Space of Montmorillonite. [PDF]

ACS Omega
Borrego-Sánchez A, García-Frutos EM, Darder M, Sainz-Díaz CI.   +3 more
europepmc   +1 more source

Bridging the gap between molecules and materials in quantum chemistry with localized active spaces. [PDF]

Nat Commun
King DS, Jangid B, Hermes MR, Gagliardi L.   +3 more
europepmc   +1 more source