Results 201 to 210 of about 21,681 (299)

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Tuning electronic and magnetic properties of Fe-doped MgCl₂ monolayer by charge doping: a first-principles study. [PDF]

Nanoscale Adv
Van Ngoc H, Ha CV, Ponce-Pérez R, Phuong Thuy HT, An PM, Guerrero-Sanchez J, Hoat DM.   +6 more
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

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

The Mott-Jones Electron Crystal: Patterning Atomic Positions for Pseudogap Formation in Hume-Rothery Phases. [PDF]

Inorg Chem
Garman LC, Fredrickson DC.
europepmc   +1 more source

Probing Machine Learning Interatomic Potentials on Ion Transport Properties

Advanced Intelligent Discovery, EarlyView.
We perform a systematic benchmark of six state‐of‐the‐art universal machine learning interatomic potentials on their ability to predict ion transport properties in lithium‐ and sodium‐based superionic conductors relevant to all‐solid‐state batteries.
Ogheneyoma Aghoghovbia, Ming Hu, Adji Bousso Dieng   +2 more
wiley   +1 more source

Insights into the Degradation Mechanism of Aqueous LiMn2O4–Cathode Interfaces by Computational Vibrational Spectroscopy

Angewandte Chemie, EarlyView.
LiMn2O4${\rm LiMn}_2{\rm O}_4$ is of high interest for sustainable energy storage and conversion. Its mixed‐valence Mn3+${\rm Mn}^{3+}$/Mn4+${\text{Mn}}^{4+}$ structure drives site‐specific behavior at aqueous interfaces, featuring proton‐generating Mn4+${\text{Mn}}^{4+}$ centers and electron‐rich Mn3+${\rm Mn}^{3+}$ sites that render the surrounding ...
K. Nikolas Lausch, Redouan El Haouari, Philipp Schienbein, Jörg Behler   +3 more
wiley   +2 more sources

BCC Structural Stability and Deformation Behavior of Ti-Mo Alloys: A Cluster-Model-Embedded First-Principles Study. [PDF]

Materials (Basel)
Li X, Zhang Q, Zhao Y, Niu B, Ma C, Xiao W, Wang Q.   +6 more
europepmc   +1 more source

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source

Compositional and Structural Diversity in Cesium Superhydrides at High Pressure

Angewandte Chemie, EarlyView.
Through high pressure‐high temperature diamond anvil cell experiments combined with density functional theory calculations, cesium is demonstrated to have remarkably complex and diverse hydride chemistry culminating in the formation of CsH25${\rm CsH}_{25}$, the most hydrogen‐rich metal hydride reported to date.
Mikhail A. Kuzovnikov, Francesco Belli, Hannah A. Shuttleworth, Tomas Marqueño, Israel Osmond, Miriam Peña‐Alvarez, Eva Zurek, Ross T. Howie   +7 more
wiley   +2 more sources