Chemical Physics Reviews (@chemphysrev) 's Twitter Profile
Chemical Physics Reviews

@chemphysrev

CPR is a new journal featuring reviews and original research covering all areas of chemical physics. Published by @AIP_Publishing.

ID: 1194624850377220096

linkhttps://pubs.aip.org/aip/cha calendar_today13-11-2019 14:35:50

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Chemical Physics Reviews (@chemphysrev) 's Twitter Profile Photo

Atomic clusters are intriguing to researchers. We present details of their structure-property relationships to rationalise the observations and guide future applications. ChemE IIT Jodhpur Learn more 👇 aippub.org/4dsrarS

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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! 👉 aippub.org/3YjBJZ7

Chemical Physics Reviews (@chemphysrev) 's Twitter Profile Photo

We review the different approaches to the modelling of the kinetics of exciton decay in materials that emit via thermally activated delayed fluorescence. EZ-C Group @StAndrewsOSC Eli Zysman-Colman St Andrews School of Chemistry Learn more 👇 aippub.org/4gI0Blx

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Incorporating quantum nuclear delocalization via the CNEO-QM/MM framework reveals significant differences in hydrogen bond geometries and dynamics compared to conventional QM/MM. Yang Group TCI UW-Madison Learn more 👇 aippub.org/3OqPznY

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Modeling the Lithium dendrite formation sites/scenarios in the solid electrolyte interphase – a multi-component structure in Li-ion batteries, using high-throughput DFT-NEB and ML techniques. Utah State Science Learn more 👇 aippub.org/495EDp3

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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 BU Chemistry Learn more 👇 aippub.org/4iYjBgU

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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 aippub.org/3WBRsCD Sabine Richert, Universität Freiburg

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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. aippub.org/3En7N7O Mohammed Alghadeer

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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. Science Tokyo (Institute of Science Tokyo) Learn more 👇 aippub.org/4kroQq0

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In-operando SFG spectroscopy reveals stable CTAB surfactant monolayers across a wide potential range at electrochemical interfaces, providing new insights for interface engineering. Learn more 👇 aippub.org/4bst8sW

Chemical Physics Reviews (@chemphysrev) 's Twitter Profile Photo

A modern overview of spatial techniques for machine learning of slow collective variables and enhanced sampling in molecular dynamics simulations. Tuğçe Jakub Rydzewski @jkrd.bsky.social Learn more 👇 aippub.org/3XsQxEQ

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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. Huo Group @UofR Learn more 👇 aippub.org/4kDY8L0

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This review introduces computational diabatization schemes for the Dexter type excitation energy transfer coupling and their applications for photosynthetic phenomena such as photoprotection. Learn more 👇 aippub.org/44k2IZ5

Chemical Physics Reviews (@chemphysrev) 's Twitter Profile Photo

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. Learn more 👇 aippub.org/4d6I6pF

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This review maps the landscape of excited-state methods: comparing performance, clarifying limitations, and guiding method selection for diverse molecular systems. Learn more 👇 aippub.org/4iHaXSl

Chemical Physics Reviews (@chemphysrev) 's Twitter Profile Photo

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. Learn more 👇 aippub.org/4cYrJes

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We’re continuing the conversation — in a new place. AIP Publishing is now posting on Bluesky: @ aip-publishing.bsky.social Follow us there for the latest research🧪