Towards a universal exchange enhancement factor in density functional theory

Horowitz, C. M.; Proetto, C. R.; Pitarke, J. M.

doi:10.1103/physrevb.107.195120

Cited by 8 publications

(1 citation statement)

References 40 publications

(58 reference statements)

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“…[134] Moreover, even though a universal exchange-correlation functional, which describes the electron density, theoretically exists, the explicit form of the functional is unknown. [135] The investigation of the molecular or atomic level is conducted by MD or/and Monte Carlo simulations. MD simulations use the laws of motion, energy, and forces to model interactions between all atoms in a system.…”

Section: Machine Learning For Simulations: Make It Faster Make It Bettermentioning

confidence: 99%

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Kuznetsova,

Coogan,

Botov

et al. 2024

Advanced Materials

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Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating the design of novel materials, and reducing the need for time‐consuming and labour‐intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials and nanostructures is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarised luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, we provide an analysis of machine learning methods used in studying achiral nanomaterials, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. We present an overview of chiral nanomaterials within the framework of synthesis‐structure‐property‐application relationships and provide insights on how to leverage machine learning for the study of these highly complex relationships. We also review and discuss some key recent publications on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.This article is protected by copyright. All rights reserved

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Section: Machine Learning For Simulations: Make It Faster Make It Bettermentioning

confidence: 99%