Results 161 to 170 of about 41,596 (260)

Replacing Quantum Chemistry With Machine-Learned Interatomic Potentials: Revolution or Evolution? [PDF]

ACS Cent Sci
Medford AJ, Sholl DS.
europepmc   +1 more source

Understanding and Designing Phase Change Materials: Insights From Atom Probe Tomography

Advanced Science, EarlyView.
Atom probe tomography can distinguish metallic, covalent, and metavalent bonds based upon their bond rupture; in particular, the probability to form more than one ion (PME) in laser‐assisted field evaporation. These findings open new avenues in understanding and designing phase change materials (PCMs) since they allow quantification of bonds in solids ...
Jan Köttgen, Nils von den Driesch, Alexander Pawlis, Matthias Wuttig   +3 more
wiley   +1 more source

Chemically Interpretable Prediction Models for Silicon and Germanium Bond Dissociation Enthalpies. [PDF]

ACS Omega
Araujo Dias AJ, Nagashima Y.
europepmc   +1 more source

Interlayer‐Sliding‐Enabled Multiferroicity and Giant Switchable Anomalous Hall Conductivity in RuO2Zn2F2 Bilayer

Advanced Science, EarlyView.
Interlayer sliding in the RuO2Zn2F2 bilayer induces ferroelectricity and enables reversible valley polarization switching. The electric dipole and valley‐resolved band edges are intimately coupled, revealing sliding ferroelectricity as a powerful mechanism for electrical control of valley degrees of freedom in 2D materials.
Djamel Bezzerga, Imran Khan, Ganie Suhail Ahmad, Jisang Hong   +3 more
wiley   +1 more source

Quantum Chemical Insights Into Glycine-Mediated Stepwise Transformation of Halobenzoquinones. [PDF]

Chemphyschem
Li P, Yang Y, Wang Y, Feng W, Jia C, Wang W.   +5 more
europepmc   +1 more source

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

Advanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su, Yanda Zhu, Hao Peng, Manman Li, Xuzihan Zhang, Meixiao Wu, Siyuan Zhang, Yuhan Zeng, Jiwen Liao, Ming Luo, Hetaishan Huang, Yutong Wang, Sean Li, Wenxian Li   +13 more
wiley   +1 more source

Simulating Infrared Spectra of Matrix Isolation Experiments: What Works Best? [PDF]

J Comput Chem
Graneri MHV, Spagnoli D, Wild DA, McKinley AJ.   +3 more
europepmc   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

Modeling Peptides in Aqueous Ionic Liquid Mixtures: A Quantum Mechanics/Molecular Dynamics Study of Structural and NMR Properties. [PDF]

J Chem Theory Comput
Murnikova Ž, Aidas K.
europepmc   +1 more source