Weβre continuing the conversation β in a new place.
AIP Publishing is now posting on Bluesky: @ https://t.co/0F98H6Bkrg
Follow us there for the latest researchπ§ͺ
Weβre continuing the conversation β in a new place.
AIP Publishing is now posting on Bluesky: @ https://t.co/0F98H6Bkrg
Follow us there for the latest researchπ§ͺ
The construction of quantitative relationships between polymer structure and properties through machine learning is crucial for the design and preparation of high-performance polymer materials.
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https://t.co/wj4ofj4ADP
This review maps the landscape of excited-state methods: comparing performance, clarifying limitations, and guiding method selection for diverse molecular systems.
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https://t.co/Di3KPyTVW0
Chiral metal halide perovskites have rapidly advanced for chiral-induced spin selectivity. This article reviews the structural asymmetry, theories, experiments and state-of-the-art spin-LEDs.
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https://t.co/561CIG07da
This review introduces computational diabatization schemes for the Dexter type excitation energy transfer coupling and their applications for photosynthetic phenomena such as photoprotection.
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https://t.co/bVqBmJ9Bv6
With the relationships between all the most popular polariton Hamiltonians clearly derived in one place, cavity polariton theory becomes more accessible for both new and established researchers. @HuoGroup
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https://t.co/6vW7Ss7bvQ
A modern overview of spatial techniques for machine learning of slow collective variables and enhanced sampling in molecular dynamics simulations. @tugceegokdemir @JakubRydzewski
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https://t.co/U7iKxTi88B
In-operando SFG spectroscopy reveals stable CTAB surfactant monolayers across a wide potential range at electrochemical interfaces, providing new insights for interface engineering.
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https://t.co/AKg45k2Wpk
We review theory of kernel regressions & applications in materials informatics, highlighting relations between different flavors of the method and other ML methods. Kernel designs are also reviewed. @sciencetokyo_en
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https://t.co/ohYJrqgUhB
Exploring the potential of machine learning to predict material properties from chemical composition, with a focus on physics-guided ML for accurate, interpretable predictions in materials science. https://t.co/zI8IMKgkZe @MoALGq
Focusing on molecular materials, we outline the theoretical background of exchange coupling and review available methods for its characterization in the electronic ground and excited states https://t.co/pY8v1xt1YS @sabine_richert, @UniFreiburg
We discuss how 2D electronic spectroscopy can be applied to exciton-polaritons to reveal previously hidden information about the photophysics of energy relaxation in these hybrid photonic materials. @minjung_son@BUChemistry
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https://t.co/9Wj3IHLzFS
Modeling the Lithium dendrite formation sites/scenarios in the solid electrolyte interphase β a multi-component structure in Li-ion batteries, using high-throughput DFT-NEB and ML techniques. @UStateScience
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https://t.co/txirVeSajU
Incorporating quantum nuclear delocalization via the CNEO-QM/MM framework reveals significant differences in hydrogen bond geometries and dynamics compared to conventional QM/MM. @yangtheorychem@TCI_UW_Madison
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https://t.co/qcjvOVilCA
We review the different approaches to the modelling of the kinetics of exciton decay in materials that emit via thermally activated delayed fluorescence. @ezc_group@StAndrewsOSC@chemguy_eli@StAndrewsChem
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https://t.co/ImiMEM3218
Publishing #OpenAccess enhances the visibility and impact of research. Our author select program allows researchers to publish OA across our whole portfolio. Read one of the most downloaded articles from Chemical Physics Reviews as we celebrate #OAWeek!
π https://t.co/Eiuwe8Y5T4