Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

Jinnouchi, Ryosuke; Lahnsteiner, Jonathan; Karsai, Ferenc; Kresse, Georg; Bokdam, Menno

doi:10.1103/physrevlett.122.225701

Cited by 358 publications

(413 citation statements)

References 47 publications

(42 reference statements)

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“…The machine-learned force field (MLFF) of MAPbI 3 as presented in Ref. 45 is used to generate MD trajectories. The MLFF was trained on-the-fly during FPMD calculations with the SCAN (Strongly Constrained Appropriately Normed) 53 exchange-correlation functional, as its relative total energies agree well with many-body perturbation theory calculations in random phase approximation 54 .…”

Section: Molecular Dynamicsmentioning

confidence: 99%

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Long-range order imposed by short-range interactions in methylammonium lead iodide: Comparing point-dipole models to machine-learning force fields

2019

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The crystal structure of the MAPbI3 hybrid perovskite forms an intricate electrostatic puzzle with different ordering patterns of the MA molecules at elevated temperatures. For this perovskite three published model Hamiltonians based on the point-dipole (pd) approximation combined with shortrange effective interactions are compared to a recently developed machine-learning force field. A molecular order parameter is used to consistently compare the transformation of the anti-ferroelectric ordering in the orthorhombic phase upon raising the temperature. We show that the ground states and the order-disorder transition of the three models are completely different. Our analysis indicates that the long-range order in the low-temperature orthorhombic phase can be captured by pd-based models with a short cutoff radius, including the nearest and next-nearest neighbor molecules. By constructing effective atomic interactions the ordering can already be described within a 6Å radius. By extracting the coupling energetics of the molecules from density functional theory calculations on MAxCs1−xPbI3 test systems, we show that the pd-approximation holds at least for static structures. To improve the accuracy of the pd-interaction an Ewald summation is applied combined with a distance dependent electronic screening function.

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Section: Molecular Dynamicsmentioning

confidence: 99%

“…In order to straightforwardly compare the ordering patterns of the MA molecules we calculate the molecular order parameter as briefly presented Ref. 45 . We assume an integer cubic grid q = (i, j, k) of dimensions N = N x × N y × N z that has on each site a unit vector p q describing the orientation of the MA molecule.…”

Section: A Molecular Order Parametermentioning

confidence: 99%

Long-range order imposed by short-range interactions in methylammonium lead iodide: Comparing point-dipole models to machine-learning force fields

2019

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“…Podryabinkin et al showed that their approach can identify various existing and hypothetical boron allotropes 20 . Finally, Jinnouchi et al demonstrated how ab initio molecular dynamics (AIMD) simulations of specific systems can be sped up by active learning of the computed forces (in a modified GAP framework), using the predicted error of the Gaussian process to select new datapoints and to improve the speed of AIMD 37,39 .…”

Section: Introductionmentioning

confidence: 99%

De novo exploration and self-guided learning of potential-energy surfaces

2019

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Interatomic potential models based on machine learning (ML) are rapidly developing as tools for materials simulations. However, because of their flexibility, they require large fitting databases that are normally created with substantial manual selection and tuning of reference configurations. Here, we show that ML potentials can be built in a largely automated fashion, exploring and fitting potential-energy surfaces from the beginning (de novo) within one and the same protocol. The key enabling step is the use of a configuration-averaged kernel metric that allows one to select the few most relevant structures at each step. The resulting potentials are accurate and robust for the wide range of configurations that occur during structure searching, despite only requiring a relatively small number of single-point DFT calculations on small unit cells. We apply the method to materials with diverse chemical nature and coordination environments, marking a milestone toward the more routine application of ML potentials in physics, chemistry, and materials science.

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“…We also mention on-the-fly or "onlinetrained" ML potentials: [80][81][82] these start with a quantummechanical (again, normally DFT-based) MD simulation and train an ML potential while the simulation is being run. We also mention on-the-fly or "onlinetrained" ML potentials: [80][81][82] these start with a quantummechanical (again, normally DFT-based) MD simulation and train an ML potential while the simulation is being run.…”

Section: Ingredient 3: Regression Tools and Implementationsmentioning

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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Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

Cited by 358 publications

References 47 publications

Long-range order imposed by short-range interactions in methylammonium lead iodide: Comparing point-dipole models to machine-learning force fields

Long-range order imposed by short-range interactions in methylammonium lead iodide: Comparing point-dipole models to machine-learning force fields

De novo exploration and self-guided learning of potential-energy surfaces

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

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