Machine learning for the structure–energy–property landscapes of molecular crystals

Musil, Félix; De, Sandip; Yang, Jack; Campbell, Joshua E.; Day, Graeme M.; Ceriotti, Michele

doi:10.1039/c7sc04665k

Cited by 197 publications

(229 citation statements)

References 81 publications

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“…Due to the various competitive noncovalent intermolecular interactions, computational prediction is necessary for the design of molecular crystals. Musil et al reported a novel ML framework for high‐accuracy property prediction of polymorphs. With Gaussian Process Regression (GPR) built on the SOAP (Smooth Over of Atomic Positions)‐REMatch kernel, the model can be applied to predict relative energetics of crystal materials and the transfer integrals that are calculated to predict charge mobility of molecular crystals, and the promise of high accuracy can be satisfied which is demonstrated by cross‐validated predictions.…”

Section: Achievements Of ML In Energy Storage and Conversion Materialsmentioning

confidence: 99%

“…Moreover, the accuracy of the model was effectively improved by a novel material property, which is called excess Born effective charge. Another ML classifier reported by Musil can help researchers to understand the packing and the self‐assembly mechanism of molecular materials. Such automatic classification tool shows superiority compared with the heuristic classifications.…”

Section: Achievements Of ML In Energy Storage and Conversion Materialsmentioning

confidence: 99%

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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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Section: Achievements Of ML In Energy Storage and Conversion Materialsmentioning

confidence: 99%

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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“…To our knowledge, this is the largest set of molecules that has been subjected to CSP and prop- Figure 1: The known (1) and hypothetical (2-28) molecules studied. Symmetrical (C 2v ) isomers (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) are colored in orange, to contrast with the asymmetric (C s ) isomers (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28).…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

Yang

Campbell

et al. 2018

Chem. Mater.

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Predictive computational methods have the potential to significantly accelerate the discovery of new materials with targeted properties by guiding the choice of candidate materials for synthesis. Recently, a planar pyrrole-based azaphenacene molecule (pyrido[2,3b]pyrido [3 ,2 :4,5]pyrrolo[3,2-g]indole, 1) was synthesized and shown to have promising properties for charge transport, which relate to stacking of molecules in its crystal structure. Building on our methods for evaluating small molecule organic semiconductors using crystal structure prediction, we have screened a set of 27 structural isomers of 1 to assess charge mobility in their predicted crystal structures. Machine-learning techniques are used to identify structural classes across the landscapes of all molecules and we find that, despite differences in the arrangement of hydrogen bond functionality, the predicted crystal structures of the molecules studied here can be classified into a small number of packing types. We analyze the predicted property landscapes of the series of molecules and discuss several metrics that can be used to rank the molecules as promising semiconductors. The results suggest several isomers with superior predicted electron mobilities to 1 and suggest two molecules in particular that represent attractive synthetic targets. Supporting Information AvailableDetails of the crystal structure classification scheme, information on convergence of the crystal structure search and number of unique crystal structures per molecule, eigenvalue spectrum of the SOAP Similarity Kernel, details of the electron mobility calculations, energy-structure-function maps of all molecules, discussion of uncertainties in the electron mobility calculations.

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“…This density-based representation can be adapted to incorporate correlations between atoms to any order. It has been applied successfully to a vast number of machine learning investigations for physical properties of atomic structures [26][27][28]. After summarizing the derivation and efficient implementation of an extension to SOAP, called λ-SOAP, which is particularly well-suited to the learning of tensorial properties, we present a few examples to demonstrate its effectiveness for this task.…”

Section: Introductionmentioning

confidence: 99%

Atomic-Scale Representation and Statistical Learning of Tensorial Properties

Grisafi

Wilkins

Willatt

et al. 2019

ACS Symposium Series

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This chapter discusses the importance of incorporating three-dimensional symmetries in the context of statistical learning models geared towards the interpolation of the tensorial properties of atomic-scale structures. We focus on Gaussian process regression, and in particular on the construction of structural representations, and the associated kernel functions, that are endowed with the geometric covariance properties compatible with those of the learning targets. We summarize the general formulation of such a symmetry-adapted Gaussian process regression model, and how it can be implemented based on a scheme that generalizes the popular smooth overlap of atomic positions representation. We give examples of the performance of this framework when learning the polarizability and the ground-state electron density of a molecule.

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Machine learning for the structure–energy–property landscapes of molecular crystals

Cited by 197 publications

References 81 publications

Machine learning: Accelerating materials development for energy storage and conversion

Machine learning: Accelerating materials development for energy storage and conversion

Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

Atomic-Scale Representation and Statistical Learning of Tensorial Properties

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