Active learning and neural network potentials accelerate molecular screening of ether-based solvate ionic liquids

Wang, Wujie; Yang, Tzuhsiung; Harris, William H.; Gómez‐Bombarelli, Rafael

doi:10.1039/d0cc03512b

Cited by 45 publications

(43 citation statements)

References 28 publications

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“…There are few published works on graph-based frameworks for encoding chemical structures for ILs, however these works tend to focus solely on one family of anions or cations and therefore their extension and generalisation might still be limited. [66][67][68] Another family of descriptors used in QSPR methods are those of quantum chemical (QC) or thermodynamic nature. QC descriptors use values from quantum calculations, such as HOMO and LUMO energies, polarity, electron affinity, electronegativity etc.…”

Section: Ils As Input Datamentioning

confidence: 99%

A review on machine learning algorithms for the ionic liquid chemical space

et al. 2021

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Section: Ils As Input Datamentioning

confidence: 99%

A review on machine learning algorithms for the ionic liquid chemical space

et al. 2021

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“…Minimum Li adsorption energies from GCN and DFT. In this work, we choose graph convolutional networks (GCN) as the machine learning architecture for learning Li site energies at different adsorption sites, because it has been shown to encode atomic and geometric information with high transferability 34,43,45 , and has been utilized as a model form of interatomic potentials 30, 42 . In order to efficiently learn Li adsorption energies at different sites, we iteratively sample sites from each material with site energies calculated by DFT, and then train GCN on all the calculated energies.…”

Section: Resultsmentioning

confidence: 99%

Screening and understanding Li adsorption on 2-dimensional metallic materials by learning physics

Gong

Wang

Zhu

et al. 2021

Preprint

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Two-dimensional (2D) materials have received considerable attention as possible electrodes in Li-ion batteries (LIBs), although a deeper understanding of the Li adsorption behavior as well as broad screening of the materials space is still needed. In this work, we build a high-throughput screening scheme that incorporates a learned interaction. First, density functional theory and graph convolution networks are utilized to calculate minimum Li adsorption energies for a small set of 2D metallic materials. The data is then used to find a dependence of the minimum Li adsorption energies on the sum of ionization potential, work function of the 2D metal, and coupling energy between Li+ and substrate. Our results show that variances of elemental properties and density are the most correlated features with coupling. To illustrate the applicability of this approach, the model is employed to show that some fluorides and chromium oxides are potential high-voltage materials with adsorption energies < -7 eV, and the found physics is used as the design principle to enhance the Li adsorption ability of graphene. This physics-driven approach shows higher accuracy and transferability compared with purely data-driven models.

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“…To this end, machine learning potentials can accelerate screening tasks when the required high‐level calculations become computationally prohibitive. For example, Wang et al [130] . combined active learning with GCNN to explore the configurational space for ether‐lithium complexes.…”

Section: Selected Examplesmentioning

confidence: 99%

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

Shao

Knijff

Dietrich

et al. 2021

Batteries & Supercaps

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Batteries and supercapacitors are electrochemical energy storage systems which involve multiple time‐scales and length‐scales. In terms of the electrolyte which serves as the ionic conductor, a molecular‐level understanding of the corresponding transport phenomena, electrochemical (thermal) stability and interfacial properties is crucial for optimizing the device performance and achieving safety requirements. To this end, atomistic machine learning is a promising technology for bridging microscopic models and macroscopic phenomena. Here, we provide a timely snapshot of recent advances in this area. This includes technical considerations that are particularly relevant for modelling electrolytes as well as specific examples of both bulk electrolytes and associated interfaces. A perspective on methodological challenges and new applications is also discussed.

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Active learning and neural network potentials accelerate molecular screening of ether-based solvate ionic liquids

Abstract: Solvate ionic liquids (SIL) have promising applications as electrolyte materials and machine learning can help accelerate the virtual screening of candidate molecules for SIL.

Cited by 45 publications

References 28 publications

A review on machine learning algorithms for the ionic liquid chemical space

A review on machine learning algorithms for the ionic liquid chemical space

Screening and understanding Li adsorption on 2-dimensional metallic materials by learning physics

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

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