Constructing first-principles phase diagrams of amorphous Li<i>x</i>Si using machine-learning-assisted sampling with an evolutionary algorithm

Artrith, Nongnuch; Urban, Alexander; Ceder, Gerbrand

doi:10.1063/1.5017661

Cited by 160 publications

(141 citation statements)

References 83 publications

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“…The neural network model used in the NNP generally requires large data sets for optimal performance. [152] Artrith et al [217] have proposed an iterative optimization strategy combining genetic algorithm and an NNP (schematic provided in Figure 6c), which only required ≈1000 DFT reference data. At each step, the NNP was used to identify the energetics of different configurations at specific composition of delithiated amorphous Li 15−x Si 4 , based on the assumption that it was able to sample the near-ground-state Li/vacancy arrangements, and the genetic algorithm was applied in finding the optimal configuration.…”

Section: Machine Learning Interatomic Potentialsmentioning

confidence: 99%

“…At each delithiation step, GA was used to identify the most stable Li/vacancy arrangement of that composition, with specialized NNP determining the energetics of different arrangements. [217] d) The predicted Haven ratio and e) the Arrhenius plot of Li diffusion diffusivity in α-Li 3 N from eSNAP MD simulations. [131] Reproduced with permission.…”

Section: Machine Learning Interatomic Potentialsmentioning

confidence: 99%

Section: Machine Learning Interatomic Potentialsmentioning

confidence: 99%

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A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

420

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: Machine Learning Interatomic Potentialsmentioning

confidence: 99%

Section: Machine Learning Interatomic Potentialsmentioning

confidence: 99%

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A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

420

281

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“…[153,164] Building on a computational description of pure disordered carbon, it was also shown how the insertion of Li ions in carbon can be described by a difference potential. [165] In this context, it is interesting to mention two separate studies that develop NN models for Li in amorphous silicon anodes: [166,167] these works provide a thematic link to the wide importance of silicon (Section 3) and underline the possibilities of ML potentials especially for energy materials modeling.…”

Section: Carbon Nanomaterialsmentioning

confidence: 99%

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

2019

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In pursuit of this goal, atomic-scale computer simulations have long been a central approach, and two major families of methods are routinely used today. On the one hand, there are quantum-mechanical simulations, in which we solve Schrödinger's equation for the electronic structure of molecular and periodic systems, most widely based on density-functional theory (DFT). [6][7][8] These methods provide (largely) reliable results for structural models of materials that normally contain a few tens or hundreds of atoms. State-of-the-art DFT methods can be applied to many material classes, and they are increasingly used for high-throughput screening and "in silico" (computer-based) design of materials: new compositions and previously unknown structures have been identified in DFT searches and subsequently experimentally realized. [5,[9][10][11] On the other hand, interatomic potential models ("force fields"), parameterizing interactions between atoms with (relatively) simple functional forms, are widely used in materials science to describe matter in molecular dynamics (MD) simulations. These simulations grant access to larger time and length scales, reaching system sizes of up to hundreds of thousands of atoms. [12] In parameterizing these potentials, a certain physical form of the atomic interactions is assumed, often in terms of bond distances, angles, and so on, and physical properties such as equilibrium lattice parameters or elastic constants enter the fitting of the potential. For this reason, such potentials are often called "empirical." They are several orders of magnitude faster than DFT, but necessarily less accurate and less easily transferable.In this Progress Report, we highlight recent developments in "machine-learned" interatomic potentials, which represent a rapidly growing field that promises to do away with the aforementioned trade-offs. Over the last year, there has been a surge of interest in machine learning (ML) methodology: part of it is due to the dramatic growth of ML throughout the scientific disciplines, and part of it is due to tangible success stories of ML-based interatomic potentials that are now beginning to emerge. We will argue that this is an exciting development with very practical implications, currently on the verge of moving from a somewhat specialized new technology to everyday applicability, poised to enhance and complement the communities' existing strengths in computational materials modeling. We will show selected applications of ML potentials to problems in materials science, discuss the current limitations (and possible pitfalls), and outline what we expect to be interesting directions for the development of the field in the coming years.Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materi...

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“…Melt-quench simulation protocols model the formation of an amorphous phase from a melt or at high temperatures. Artrith et al developed a methodology that couples a genetic algorithm (GA) with a specialized ANN potential for the efficient construction of phase diagrams of electrochemical amorphization [69]. This methodology was applied to amorphous LiSi alloys that are prospective high-capacity anode materials for Li-ion batteries.…”

Section: Nanostructured and Amorphous Phasesmentioning

confidence: 99%

Machine learning for the modeling of interfaces in energy storage and conversion materials

Artrith

2019

J. Phys. Energy

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The properties and atomic-scale dynamics of interfaces play an important role for the performance of energy storage and conversion devices such as batteries and fuel cells. In this topical review, we consider recent progress in machine-learning (ML) approaches for the computational modeling of materials interfaces. ML models are computationally much more efficient than first principles methods and thus allow to model larger systems and extended timescales, a necessary prerequisites for the accurate description of many interface properties. Here we review the recent major developments of ML-based interatomic potentials for atomistic modeling and ML approaches for the direct prediction of materials properties. This is followed by a discussion of ML applications to solid-gas, solid-liquid, and solid-solid interfaces as well as to nanostructured and amorphous phases that commonly form in interface regions. We then highlight how ML has been used to obtain important insights into the structure and stability of interfaces, interfacial reactions, and mass transport at interfaces. Finally, we offer a perspective on the current state of ML potential development and identify future directions and opportunities for this exciting research field.

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Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm

Cited by 160 publications

References 83 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Machine learning for the modeling of interfaces in energy storage and conversion materials

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