Nonparametric Local Pseudopotentials with Machine Learning: A Tin Pseudopotential Built Using Gaussian Process Regression

Lüder, Johann; Manzhos, Sergei

doi:10.1021/acs.jpca.0c05723

Cited by 14 publications

(15 citation statements)

References 98 publications

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“…Similarly, local pseudopotentials have been constructed based on kernel ridge regression. 46 Another important decision in method selection is whether the problem of interest exhibits strong electron correlation (also referred to as multi-reference character or static correlation). In this case, a single configuration of electrons is no longer sufficient to describe the system and single-reference methods (e.g.…”

Section: ML Improves Model Building Methods Choice and Opens New Mult...mentioning

confidence: 99%

Perspective on integrating machine learning into computational chemistry and materials science

Westermayr,

Gastegger,

Schütt

et al. 2021

Preprint

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Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties -be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how various aspects of atomistic computational modelling are being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a deeper and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training.

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Section: ML Improves Model Building Methods Choice and Opens New Mult...mentioning

confidence: 99%

Perspective on integrating machine learning into computational chemistry and materials science

Westermayr,

Gastegger,

Schütt

et al. 2021

Preprint

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“…For other atoms beyond light metals new ideas are needed. Machine learning holds promise in this area [202].…”

Section: Real-time Td-ofdftmentioning

confidence: 99%

Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond

Manzhos

Giorgi

Lüder

et al. 2021

Advances in Physics: X

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Plasmonic particles and nanostructures are widely used in photovoltaic and photonics. Surface plasmons were found to enhance different types of solar cells including plasmonic DSSCs, plasmonic solid semiconductor solar cells, plasmonic organic solar cells, and plasmonic perovskite solar cell. Size, composition, and shape of plasmonic nanoparticles as well as nanometer-distance control between particles are key design factors of plasmonic nanostructures. Modeling is rapidly gaining in importance for mechanistic understanding and rational design of plasmonic nanostructures. We review the modeling approaches used to model plasmon resonance features of nanostructures, from classical approaches that can routinely handle most particle sizes used in solar cells to approaches beyond classical electrodynamics such as ab initio approaches based on time-dependent density functional theory (TD-DFT). We highlight recently emerging approaches which have the potential to significantly enhance modeling capabilities in the coming years, in particular, by allowing atomistic (ab initio) modeling at realistic length scales, i.e. of particle sizes beyond 10 nm which are of most interest to plasmonic solar cells but remain problematic with traditional DFT-based techniques, such as density functional tight binding (DFTB) based approaches, time-dependent orbital-free DFT, and machine learning-based approaches, as well as many-body perturbation theory which is expected to gain usage with advances in computing power.

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“…14–16 In this regard, there have been continuous efforts to develop local pseudopotentials (LPPs) by removing the orbital-dependence over the last few decades. 14,17–23…”

Section: Introductionmentioning

confidence: 99%

“…14,24–31 The KEDF works better for systems with smoother densities, making it difficult to apply OF-DFT to systems with rapidly changing electron densities, for example, obtained from all-electron calculations. 20,32 In fact, the performance of OF-DFT is usually good for metallic systems. 14,16,33–42 The application of OF-DFT to more diverse systems is limited because there are few elements that the present LPPs can cover.…”

Section: Introductionmentioning

confidence: 99%

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Neural network-based pseudopotential: development of a transferable local pseudopotential

Woo

Kim

2022

Phys. Chem. Chem. Phys.

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Nonparametric Local Pseudopotentials with Machine Learning: A Tin Pseudopotential Built Using Gaussian Process Regression

Cited by 14 publications

References 98 publications

Perspective on integrating machine learning into computational chemistry and materials science

Perspective on integrating machine learning into computational chemistry and materials science

Modeling of plasmonic properties of nanostructures for next generation solar cells and beyond

Neural network-based pseudopotential: development of a transferable local pseudopotential

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