Accelerating high-throughput searches for new alloys with active learning of interatomic potentials

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Hart, Gus L. W.; Shapeev, Alexander V.

doi:10.1016/j.commatsci.2018.09.031

Cited by 298 publications

(235 citation statements)

References 53 publications

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“…It is expected that by including more parameters in the fitted MLIPs the agreement for the phonon group velocity will be greatly improved (find figure S3 for the case of silicon). For this aim other methodologies like the learning on-the-fly [39,40] can be also considered. Comparing the phonon dispersion and group velocity results, it is apparent that C 3 N monolayer exhibits the maximal disagreement in the MLIP and DFT estimations.…”

Section: Resultsmentioning

confidence: 99%

“…We employed moment tensor potential (MTP) [40,50] as an accurate and computationally efficient model of describing interatomic interaction. MTPs are based on the representation of atomic environments in the form of inertia tensors of various ranks multiplied by radial polynomial functions (Chebyshev polynomials).…”

Section: Training Of Interatomic Potentialsmentioning

confidence: 99%

“…The training procedure is thus a nonlinear optimization which is solved using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. MTPs have been tested on a number of atomic modeling applications [51], where they demonstrate excellent characteristics for reproducing the properties of both single-component [41,52] and multicomponent materials [40,43].…”

Section: Training Of Interatomic Potentialsmentioning

confidence: 99%

“…In recent years, the astonishing advances in machine-learning techniques have opened new possibilities to address critical challenges in various fields, also in materials science [9,[34][35][36][37][38]. For example, actively trained machine-learning interatomic potentials [39] have been successfully employed to predict novel materials [40,41] and examine lattice dynamics [42] and thermal conductivity [43] of bulk materials. As shown in the latest studies [42,43], such machine learning-based interatomic potentials bring a DFT level accuracy for the computed energies and forces using classical molecular dynamics simulations.…”

Section: Introductionmentioning

confidence: 99%

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Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

et al. 2020

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It is well-known that the calculation of thermal conductivity using classical molecular dynamics (MD) simulations strongly depends on the choice of the appropriate interatomic potentials. As proven for the case of graphene, while most of the available interatomic potentials estimate the structural and elastic constants with high accuracy, when employed to predict the lattice thermal conductivity they however lead to a variation of predictions by one order of magnitude. Here we present our results on using machine-learning interatomic potentials (MLIPs) passively fitted to computationally inexpensive ab-initio molecular dynamics trajectories without any tuning or optimizing of hyperparameters. These first-attempt potentials could reproduce the phononic properties of different two-dimensional (2D) materials obtained using density functional theory (DFT) simulations. To illustrate the efficiency of the trained MLIPs, we consider polyaniline C 3 N nanosheets. C 3 N monolayer was selected because the classical MD and different first-principles results contradict each other, resulting in a scientific dilemma. It is shown that the predicted thermal conductivity of 418 ± 20 W mK −1 for C 3 N monolayer by the non-equilibrium MD simulations on the basis of a first-attempt MLIP evidences an improved accuracy when compared with the commonly employed MD models. Moreover, MLIP-based prediction can be considered as a solution to the debated reports in the literature. This study highlights that passively fitted MLIPs can be effectively employed as versatile and efficient tools to obtain accurate estimations of thermal conductivities of complex materials using classical MD simulations. In response to remarkable growth of 2D materials family, the devised modeling methodology could play a fundamental role to predict the thermal conductivity.

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Section: Resultsmentioning

confidence: 99%

Section: Training Of Interatomic Potentialsmentioning

confidence: 99%

Section: Training Of Interatomic Potentialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

et al. 2020

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“…We will also need to employ smart sampling methods and identify data points that are most important for the training of ML models. Active learning strategies offer a path towards this goal [45][46][47]. Many of these techniques are of general-purpose utility, but some will have to be tailored towards the specific problem settings of data-derived models for chemistry.…”

Section: B Machine Learning For Small Datamentioning

confidence: 99%

Advances of machine learning in molecular modeling and simulation

Haghighatlari

Hachmann

2019

Current Opinion in Chemical Engineering

108

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In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we discuss how machine learning relates to, supports, and augments more traditional physics-based approaches in computational research. We conclude by outlining challenges and future research directions that need to be addressed in order to make machine learning a mainstream chemical engineering tool.

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