Chemical shifts in molecular solids by machine learning

Paruzzo, Federico M.; Hofstetter, Albert; Musil, Félix; De, Sandip; Ceriotti, Michele; Emsley, Lyndon

doi:10.1038/s41467-018-06972-x

Cited by 216 publications

(323 citation statements)

References 72 publications

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“…Finally, an application of ML models that has received far less attention in other reviews is their potential to enhance experimental characterization. The interpretation and labeling of experimental images (e.g., scanning transmission electron microscopy (STEM)) and spectra (e.g., X‐ray diffraction (XRD), X‐ray absorption near‐edge structure (XANES), nuclear magnetic resonance (NMR), etc.) are today still mostly painstakingly carried out by humans.…”

Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

399

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

399

281

View full text Add to dashboard Cite

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“…In a system with relatively uniform atom density, the overlap between environments X j |X k is dominated by the region farthest from the center. This could be regarded as rather unphysical, since interactions between atoms decay with distance and the closest atoms should therefore give the most significant contribution to properties, which is reflected in the observation that multi-scale kernels tend to perform best when very low weights are assigned to the long-range kernels 3,57,65 . This effect can be counteracted by multiplying the atomic probability amplitude Eq.…”

Section: B Radially-scaled Kernelsmentioning

confidence: 99%

Atom-density representations for machine learning

Willatt

Musil

Ceriotti

2019

The Journal of Chemical Physics

Self Cite

184

237

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The applications of machine learning techniques to chemistry and materials science become more numerous by the day. The main challenge is to devise representations of atomic systems that are at the same time complete and concise, so as to reduce the number of reference calculations that are needed to predict the properties of different types of materials reliably. This has led to a proliferation of alternative ways to convert an atomic structure into an input for a machine-learning model. We introduce an abstract definition of chemical environments that is based on a smoothed atomic density, using a bra-ket notation to emphasize basis set independence and to highlight the connections with some popular choices of representations for describing atomic systems. The correlations between the spatial distribution of atoms and their chemical identities are computed as inner products between these feature kets, which can be given an explicit representation in terms of the expansion of the atom density on orthogonal basis functions, that is equivalent to the smooth overlap of atomic positions (SOAP) power spectrum, but also in real space, corresponding to n-body correlations of the atom density. This formalism lays the foundations for a more systematic tuning of the behavior of the representations, by introducing operators that represent the correlations between structure, composition, and the target properties. It provides a unifying picture of recent developments in the field and indicates a way forward towards more effective and computationally affordable machine-learning schemes for molecules and materials. arXiv:1807.00408v3 [physics.chem-ph]

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“…Moreover, compared with traditional computational methods, the ML methods can provide cheap and highly accurate simulation processes. Paruzzo et al reported a ML workflow for simulating the results of chemical shifts in solids which are always gained by NMR. ML models are based on computational data from Cambridge Structural Database (CSD), and the models have acceptable accuracy even though no data of experimental shifts are used for the training step.…”

Section: Achievements Of ML In Energy Storage and Conversion Materialsmentioning

confidence: 99%

Machine learning: Accelerating materials development for energy storage and conversion

Chen

Zhou

2020

InfoMat

247

171

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With the development of modern society, the requirement for energy has become increasingly important on a global scale. Therefore, the exploration of novel materials for renewable energy technologies is urgently needed. Traditional methods are difficult to meet the requirements for materials science due to long experimental period and high cost. Nowadays, machine learning (ML) is rising as a new research paradigm to revolutionize materials discovery.In this review, we briefly introduce the basic procedure of ML and common algorithms in materials science, and particularly focus on latest progress in applying ML to property prediction and materials development for energyrelated fields, including catalysis, batteries, solar cells, and gas capture. Moreover, contributions of ML to experiments are involved as well. We highly expect that this review could lead the way forward in the future development of ML in materials science. K E Y W O R D Sbig data, energy storage and conversion, machine learning, property prediction

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Chemical shifts in molecular solids by machine learning

Cited by 216 publications

References 72 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Atom-density representations for machine learning

Machine learning: Accelerating materials development for energy storage and conversion

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