Showing papers on "Interatomic potential published in 2020"
TL;DR: The present work demonstrates the ability to combine, in the same ML model, the previously attained flexibility required for amorphous carbon with the high numerical accuracy necessary for crystalline graphene, thereby providing an interatomic potential that will be applicable to a wide range of applications concerning diverse forms of bulk and nanostructured carbon.
Abstract: We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, named GAP-20, describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structures with an accuracy approaching that of direct ab initio simulation, but at a significantly reduced cost. We combine structural databases for amorphous carbon and graphene, which we extend substantially by adding suitable configurations, for example, for defects in graphene and other nanostructures. The final potential is fitted to reference data computed using the optB88-vdW density functional theory (DFT) functional. Dispersion interactions, which are crucial to describe multilayer carbonaceous materials, are therefore implicitly included. We additionally account for long-range dispersion interactions using a semianalytical two-body term and show that an improved model can be obtained through an optimization of the many-body smooth overlap of atomic positions descriptor. We rigorously test the potential on lattice parameters, bond lengths, formation energies, and phonon dispersions of numerous carbon allotropes. We compare the formation energies of an extensive set of defect structures, surfaces, and surface reconstructions to DFT reference calculations. The present work demonstrates the ability to combine, in the same ML model, the previously attained flexibility required for amorphous carbon [V. L. Deringer and G. Csanyi, Phys. Rev. B 95, 094203 (2017)] with the high numerical accuracy necessary for crystalline graphene [Rowe et al., Phys. Rev. B 97, 054303 (2018)], thereby providing an interatomic potential that will be applicable to a wide range of applications concerning diverse forms of bulk and nanostructured carbon.
121 citations
TL;DR: High harmonics are used to reconstruct images of the valence potential and electron density in crystalline magnesium fluoride and calcium fluoride with a spatial resolution of about 26 picometres, enabling direct probing of material properties.
Abstract: Valence electrons contribute a small fraction of the total electron density of materials, but they determine their essential chemical, electronic and optical properties. Strong laser fields can probe electrons in valence orbitals1–3 and their dynamics4–6 in the gas phase. Previous laser studies of solids have associated high-harmonic emission7–12 with the spatial arrangement of atoms in the crystal lattice13,14 and have used terahertz fields to probe interatomic potential forces15. Yet the direct, picometre-scale imaging of valence electrons in solids has remained challenging. Here we show that intense optical fields interacting with crystalline solids could enable the imaging of valence electrons at the picometre scale. An intense laser field with a strength that is comparable to the fields keeping the valence electrons bound in crystals can induce quasi-free electron motion. The harmonics of the laser field emerging from the nonlinear scattering of the valence electrons by the crystal potential contain the critical information that enables picometre-scale, real-space mapping of the valence electron structure. We used high harmonics to reconstruct images of the valence potential and electron density in crystalline magnesium fluoride and calcium fluoride with a spatial resolution of about 26 picometres. Picometre-scale imaging of valence electrons could enable direct probing of the chemical, electronic, optical and topological properties of materials. Laser-generated high-harmonic emission is used to image the valence potential and electron density in magnesium fluoride and calcium fluoride at the picometre scale, enabling direct probing of material properties.
95 citations
TL;DR: A scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials and application to liquid water shows that training the network with a relatively small number of molecular configurations is sufficient to predict the polarIZability of arbitrary liquid configurations.
Abstract: We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a relatively small number of molecular configurations is sufficient to predict the polarizability of arbitrary liquid configurations in close agreement with ab initio density functional theory calculations. In combination with a neural network representation of the interatomic potential energy surface, the scheme allows us to calculate the Raman spectra along 2-nanosecond classical trajectories at different temperatures for H2O and D2O. The vast gains in efficiency provided by the machine learning approach enable longer trajectories and larger system sizes relative to ab initio methods, reducing the statistical error and improving the resolution of the low-frequency Raman spectra. Decomposing the spectra into intramolecular and intermolecular contributions elucidates the mechanisms behind the temperature dependence of the low-frequency and stretch modes.
83 citations
TL;DR: Farkas and Caro as discussed by the authors developed a set of embedded atom model (EAM) interatomic potentials to represent highly idealized face-centered cubic (FCC) mixtures of Fe−Ni−Cr−Co−Al at nearequiatomic compositions.
Abstract: A set of embedded atom model (EAM) interatomic potentials was developed to represent highly idealized face-centered cubic (FCC) mixtures of Fe–Ni–Cr–Co–Al at near-equiatomic compositions. Potential functions for the transition metals and their crossed interactions are taken from our previous work for Fe–Ni–Cr–Co–Cu [D. Farkas and A. Caro: J. Mater. Res. 33 (19), 3218–3225, 2018], while cross-pair interactions involving Al were developed using a mix of the component pair functions fitted to known intermetallic properties. The resulting heats of mixing of all binary equiatomic random FCC mixtures not containing Al is low, but significant short-range ordering appears in those containing Al, driven by a large atomic size difference. The potentials are utilized to predict the relative stability of FCC quinary mixtures, as well as ordered L12 and B2 phases as a function of Al content. These predictions are in qualitative agreement with experiments. This interatomic potential set is developed to resemble but not model precisely the properties of this complex system, aiming at providing a tool to explore the consequences of the addition of a large size-misfit component into a high entropy mixture that develops multiphase microstructures.
67 citations
TL;DR: In this paper, the authors investigated the Hall-Petch effect of high-entropy alloy using the interatomic potential for the multi-element FeNiCrCoCu, and the transition from mechanical strengthening to softening was observed for the simulated samples with the mean grain size ranging from 28.44 to 5.33
53 citations
TL;DR: In this article, a machine learning approach was applied to obtain an interatomic potential for lattice dynamics properties calculation with accuracy close to the one of density functional theory (DFT), which allows one to access large time and length scales through molecular dynamics simulations.
48 citations
TL;DR: In this article, an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology, is presented, which describes the properties of the bulk crystalline and amorphous phases, crystal surfaces and defect structures with an accuracy approaching that of direct ab initio simulation, at a significantly reduced cost.
Abstract: We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology The potential, named GAP-20, describes the properties of the bulk crystalline and amorphous phases, crystal surfaces and defect structures with an accuracy approaching that of direct ab initio simulation, but at a significantly reduced cost We combine structural databases for amorphous carbon and graphene, which we extend substantially by adding suitable configurations, for example, for defects in graphene and other nanostructures The final potential is fitted to reference data computed using the optB88-vdW density functional theory (DFT) functional Dispersion interactions, which are crucial to describe multilayer carbonaceous materials, are therefore implicitly included We additionally account for long-range dispersion interactions using a semianalytical two-body term and show that an improved model can be obtained through an optimisation of the many-body smooth overlap of atomic positions (SOAP) descriptor We rigorously test the potential on lattice parameters, bond lengths, formation energies and phonon dispersions of numerous carbon allotropes We compare the formation energies of an extensive set of defect structures, surfaces and surface reconstructions to DFT reference calculations The present work demonstrates the ability to combine, in the same ML model, the previously attained flexibility required for amorphous carbon [Phys Rev B, 95, 094203, (2017)] with the high numerical accuracy necessary for crystalline graphene [Phys Rev B, 97, 054303, (2018)], thereby providing an interatomic potential that will be applicable to a wide range of applications concerning diverse forms of bulk and nanostructured carbon
45 citations
TL;DR: In this article, a neural network potential for the Al-Cu system is presented as a first example of a machine learning potential that can achieve near-first-principle accuracy for many different metallurgically important aspects of this alloy.
Abstract: High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic scale phenomena, particularly during early-stage nucleation and growth. Atomistic modeling using interatomic potentials is a desirable tool for understanding the detailed phenomena involved in precipitation and strengthening, which requires length and timescales far larger than those accessible by first-principles methods. Current interatomic potentials for alloys are not, however, sufficiently accurate for such studies. Here a family of neural-network potentials (NNPs) for the Al-Cu system are presented as a first example of a machine learning potential that can achieve near-first-principles accuracy for many different metallurgically important aspects of this alloy. High-fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate-matrix interfaces, generalized stacking fault energies and surfaces for slip in matrix and precipitates, antisite defect energies, and other quantities, are shown. The NNPs also captures the subtle entropically induced transition between ${\ensuremath{\theta}}^{\ensuremath{'}}$ and $\ensuremath{\theta}$ at temperatures around 600 K. Many comparisons are made with the state-of-the-art angular-dependent potential for Al-Cu, demonstrating the significant quantitative benefit of a machine learning approach. A preliminary kinetic Monte Carlo study shows the NNP to predict the emergence of GP zones in Al-4at%Cu at $T=300$ K in agreement with experiments. These studies show that the NNP has significant transferability to defects and properties outside the structures used to train the NNP but also shows some errors highlighting that the use of any interatomic potential requires careful validation in application to specific metallurgical problems of interest.
45 citations
TL;DR: The successful application of the GAP model to the phonon density of states of a 2500-atom β-Ga2O3 structure at elevated temperature indicates the strength of machine learning potentials to tackle large-scale atomic systems in long molecular simulations, which would be almost impossible to generate with DFT-based molecular simulations at present.
Abstract: The thermal properties of β-Ga2O3 can significantly affect the performance and reliability of high-power electronic devices. To date, due to the absence of a reliable interatomic potential, first-principles calculations based on density functional theory (DFT) have been routinely used to probe the thermal properties of β-Ga2O3. DFT calculations can only tackle small-scale systems due to the huge computational cost, while the thermal transport processes are usually associated with large time and length scales. In this work, we develop a machine learning based Gaussian approximation potential (GAP) for accurately describing the lattice dynamics of perfect crystalline β-Ga2O3 and accelerating atomic-scale simulations. The GAP model shows excellent convergence, which can faithfully reproduce the DFT potential energy surface at a training data size of 32 000 local atomic environments. The GAP model is then used to predict ground-state lattice parameters, coefficients of thermal expansion, heat capacity, phonon dispersions at 0 K, and anisotropic thermal conductivity of β-Ga2O3, which are all in excellent agreement with either the DFT results or experiments. The accurate predictions of phonon dispersions and thermal conductivities demonstrate that the GAP model can well describe the harmonic and anharmonic interactions of phonons. Additionally, the successful application of our GAP model to the phonon density of states of a 2500-atom β-Ga2O3 structure at elevated temperature indicates the strength of machine learning potentials to tackle large-scale atomic systems in long molecular simulations, which would be almost impossible to generate with DFT-based molecular simulations at present.
43 citations
TL;DR: In this paper, an interatomic potential is developed for β-Ga2O3 based on a deep neural network model to predict the thermal conductivity and phonon transport properties.
Abstract: β-Ga2O3 is a wide-bandgap semiconductor of significant technological importance for electronics, but its low thermal conductivity is an impeding factor for its applications. In this work, an interatomic potential is developed for β-Ga2O3 based on a deep neural network model to predict the thermal conductivity and phonon transport properties. Our potential is trained by the ab initio energy surface and atomic forces, which reproduces phonon dispersion in good agreement with first-principles calculations. We are able to use molecular dynamics (MD) simulations to predict the anisotropic thermal conductivity of β-Ga2O3 with this potential, and the calculated thermal conductivity values agree well with experimental results from 200 to 500 K. Green–Kubo modal analysis is performed to quantify the contributions of different phonon modes to the thermal transport, showing that optical phonon modes play a critical role in the thermal transport. This work provides a high-fidelity machine learning-based potential for MD simulation of β-Ga2O3 and serves as a good example of exploring thermal transport physics of complex semiconductor materials.
41 citations
TL;DR: A highly automated approach to dataset construction is presented and the final ANI-Al potential makes very accurate predictions of radial distribution function in melt, liquid-solid coexistence curve, and crystal properties such as defect energies and barriers.
Abstract: Accuracy of molecular dynamics simulations depends crucially on the interatomic potential used to generate forces. The gold standard would be first-principles quantum mechanics (QM) calculations, but these become prohibitively expensive at large simulation scales. Machine learning (ML) based potentials aim for faithful emulation of QM at drastically reduced computational cost. The accuracy and robustness of an ML potential is primarily limited by the quality and diversity of the training dataset. Using the principles of active learning (AL), we present a highly automated approach to dataset construction. The strategy is to use the ML potential under development to sample new atomic configurations and, whenever a configuration is reached for which the ML uncertainty is sufficiently large, collect new QM data. Here, we seek to push the limits of automation, removing as much expert knowledge from the AL process as possible. All sampling is performed using MD simulations starting from an initially disordered configuration, and undergoing non-equilibrium dynamics as driven by time-varying applied temperatures. We demonstrate this approach by building an ML potential for aluminum (ANI-Al). After many AL iterations, ANI-Al teaches itself to predict properties like the radial distribution function in melt, liquid-solid coexistence curve, and crystal properties such as defect energies and barriers. To demonstrate transferability, we perform a 1.3M atom shock simulation, and show that ANI-Al predictions agree very well with DFT calculations on local atomic environments sampled from the nonequilibrium dynamics. Interestingly, the configurations appearing in shock appear to have been well sampled in the AL training dataset, in a way that we illustrate visually.
TL;DR: In this article, the authors carried out the in situ transmission electron microscopy (HRTEM) observation of the martensitic transformation process and found surprisingly wide phase interface between the parent and the martenite in a typical high strength and high elongation metastable HEA.
TL;DR: This framework is the first application of MFGP to atomistic materials simulations fusing predictions between density functional theory and classical interatomic potential calculations, and the computational efficiency is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space.
Abstract: We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence in the predictions We used density functional theory as high-fidelity prediction, while a ML interatomic potential is used as low-fidelity prediction Practical materials’ design efficiency is demonstrated by reproducing the ternary composition dependence of a quantity of interest (bulk modulus) across the full aluminum–niobium–titanium ternary random alloy composition space The MFGP is then coupled to a Bayesian optimization procedure, and the computational efficiency of this approach is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space The framework presented in this manuscript is the first application of MFGP to atomistic materials simulations fusing predictions between density functional theory and classical interatomic potential calculations
TL;DR: In this article, the extreme chemical reactivity of molten alkali chlorides at high temperature and high humidity has been investigated in concentrated solar power and nuclear applications, and it has been shown that they are a critical component in concentrating solar power systems.
Abstract: Molten alkali chloride salts are a critical component in concentrated solar power and nuclear applications. Despite their ubiquity, the extreme chemical reactivity of molten alkali chlorides at hig...
TL;DR: It is shown that the obtained DNN model can well reproduce the energies and forces calculated by AIMD simulations and predicts the formation energies of the crystalline phases of the Al-Tb system with an accuracy comparable to ab initio calculations.
Abstract: An interatomic potential for the Al–Tb alloy around the composition of Al90Tb10 is developed using the deep neural network (DNN) learning method. The atomic configurations and the corresponding total potential energies and forces on each atom obtained from ab initio molecular dynamics (AIMD) simulations are collected to train a DNN model to construct the interatomic potential for the Al–Tb alloy. We show that the obtained DNN model can well reproduce the energies and forces calculated by AIMD simulations. Molecular dynamics (MD) simulations using the DNN interatomic potential also accurately describe the structural properties of the Al90Tb10 liquid, such as partial pair correlation functions (PPCFs) and bond angle distributions, in comparison with the results from AIMD simulations. Furthermore, the developed DNN interatomic potential predicts the formation energies of the crystalline phases of the Al–Tb system with an accuracy comparable to ab initio calculations. The structure factors of the Al90Tb10 metallic liquid and glass obtained by MD simulations using the developed DNN interatomic potential are also in good agreement with the experimental X-ray diffraction data. The development of short-range order (SRO) in the Al90Tb10 liquid and the undercooled liquid is also analyzed and three dominant SROs, i.e., Al-centered distorted icosahedron (DISICO) and Tb-centered ‘3661’ and ‘15551’ clusters, respectively, are identified.
TL;DR: Developed NNP allows us to describe the structure of the glassy silica with satisfactory accuracy even though no low-temperature configurations were included in the training procedure, and opens up prospects for simulating structural and dynamical properties of liquids and glasses via NNP.
Abstract: The use of machine learning to develop neural network potentials (NNP) representing the interatomic potential energy surface allows us to achieve an optimal balance between accuracy and efficiency in computer simulation of materials. A key point in developing such potentials is the preparation of a training dataset of ab initio trajectories. Here we apply a deep potential molecular dynamics (DeePMD) approach to develop NNP for silica, which is the representative glassformer widely used as a model system for simulating network-forming liquids and glasses. We show that the use of a relatively small training dataset of high-temperature ab initio configurations is enough to fabricate NNP, which describes well both structural and dynamical properties of liquid silica. In particular, we calculate the pair correlation functions, angular distribution function, velocity autocorrelation functions, vibrational density of states, and mean-square displacement and reveal a close agreement with ab initio data. We show that NNP allows us to expand significantly the time-space scales achievable in simulations and thus calculating dynamical and transport properties with more accuracy than that for ab initio methods. We find that developed NNP allows us to describe the structure of the glassy silica with satisfactory accuracy even though no low-temperature configurations were included in the training procedure. The results obtained open up prospects for simulating structural and dynamical properties of liquids and glasses via NNP.
TL;DR: In this paper, the authors report a new interatomic potential for atomistic simulation of a ternary Si-Au-Al system, based on the force-matching method that allowed them to create the potential without use of experimental data at the fitting.
TL;DR: In this paper, the thermal conductivity of GeTe crystalline nanowires has been computed by means of non-equilibrium molecular dynamics simulations employing a machine learning interatomic potential.
Abstract: Author(s): Bosoni, E; Campi, D; Donadio, D; Sosso, GC; Behler, J; Bernasconi, M | Abstract: The thermal conductivity of GeTe crystalline nanowires has been computed by means of non-equilibrium molecular dynamics simulations employing a machine learning interatomic potential. This material is of interest for application in phase change non-volatile memories. The resulting lattice thermal conductivity of an ultrathin nanowire (7.3 nm diameter) of 1.57 W m-1 K-1 is sizably lower than the corresponding bulk value of 3.15 W m-1 K-1 obtained within the same framework. The analysis of the phonon dispersion relations and lifetimes reveals that the lower thermal conductivity in the nanowire is mostly due to a reduction in the phonon group velocities. We further predict the presence of a minimum in the lattice thermal conductivity for thicker nanowires.
TL;DR: It is shown that the hydrodynamic description of thermally driven density fluctuations can be used to obtain the thermal conductivity of a bulk fluid unambiguously, thereby bypassing the need to define the heat flux.
Abstract: Equilibrium molecular dynamics simulations, in combination with the Green-Kubo (GK) method, have been extensively used to compute the thermal conductivity of liquids. However, the GK method relies on an ambiguous definition of the microscopic heat flux, which depends on how one chooses to distribute energies over atoms. This ambiguity makes it problematic to employ the GK method for systems with nonpairwise interactions. In this work, we show that the hydrodynamic description of thermally driven density fluctuations can be used to obtain the thermal conductivity of a bulk fluid unambiguously, thereby bypassing the need to define the heat flux. We verify that, for a model fluid with only pairwise interactions, our method yields estimates of thermal conductivity consistent with the GK approach. We apply our approach to compute the thermal conductivity of a nonpairwise additive water model at supercritical conditions, and of a liquid hydrogen system described by a machine-learning interatomic potential, at 33 GPa and 2000 K.
TL;DR: An interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology is introduced.
Abstract: We introduce an interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology. The potential is based on a training set of...
TL;DR: In this paper, a theory of superconductivity is presented where the effect of anharmonicity, as entailed in the acoustic or optical phonon damping, is explicitly considered in the pairing mechanism.
Abstract: A theory of superconductivity is presented where the effect of anharmonicity, as entailed in the acoustic, or optical, phonon damping, is explicitly considered in the pairing mechanism. The gap equation is solved including diffusive Akhiezer damping for longitudinal acoustic phonons or Klemens damping for optical phonons, with a damping coefficient which, in either case, can be directly related to the Gr\"uneisen parameter and hence to the anharmonic coefficients in the interatomic potential. The results show that the increase of anharmonicity has a strikingly nonmonotonic effect on the critical temperature ${T}_{c}$. The optimal damping coefficient yielding maximum ${T}_{c}$ is set by the velocity of the bosonic mediator. This theory may open up unprecedented opportunities for material design where ${T}_{c}$ may be tuned via the anharmonicity of the interatomic potential, and presents implications for the superconductivity in the recently discovered hydrides, where anharmonicity is very strong and for which the anharmonic damping is especially relevant.
TL;DR: In this article, the phase equilibrium between liquid water and ice Ih was modeled by the TIP4P/Ice interatomic potential using enhanced sampling molecular dynamics simulations, and the authors obtained a melting temperature of 270 K in the thermodynamic limit.
Abstract: We study the phase equilibrium between liquid water and ice Ih modeled by the TIP4P/Ice interatomic potential using enhanced sampling molecular dynamics simulations. Our approach is based on the calculation of ice Ih-liquid free energy differences from simulations that visit reversibly both phases. The reversible interconversion is achieved by introducing a static bias potential as a function of an order parameter. The order parameter was tailored to crystallize the hexagonal diamond structure of oxygen in ice Ih. We analyze the effect of the system size on the ice Ih-liquid free energy differences, and we obtain a melting temperature of 270 K in the thermodynamic limit. This result is in agreement with estimates from thermodynamic integration (272 K) and coexistence simulations (270 K). Since the order parameter does not include information about the coordinates of the protons, the spontaneously formed solid configurations contain proton disorder as expected for ice Ih.
Posted Content•
TL;DR: In this paper, an interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology is introduced.
Abstract: We introduce an interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology. The potential is based on a training set of configurations collected from density functional theory (DFT) simulations and is capable of treating bulk and multilayer hBN as well as nanotubes of arbitrary chirality. The developed force field faithfully reproduces the potential energy surface predicted by DFT while improving the efficiency by several orders of magnitude. We test our potential by comparing formation energies, geometrical properties, phonon dispersion spectra and mechanical properties with respect to benchmark DFT calculations and experiments. In addition, we use our model and a recently developed graphene-GAP to analyse and compare thermally and mechanically induced rippling in large scale two-dimensional (2D) hBN and graphene. Both materials show almost identical scaling behaviour with an exponent of $\eta \approx 0.85$ for the height fluctuations agreeing well with the theory of flexible membranes. Based on its lower resistance to bending, however, hBN experiences slightly larger out-of-plane deviations both at zero and finite applied external strain. Upon compression a phase transition from incoherent ripple motion to soliton-ripples is observed for both materials. Our potential is freely available online at [this http URL].
TL;DR: In this paper, a modified embedded atom method interatomic potential was developed to study semi-coherent metal/ceramic interfaces involving Cu, Ti and N. A genetic algorithm was used to fit the model parameters to the physical properties of the materials.
TL;DR: In this article, the thermal-transport properties of hexagonal single-layer, zinc-blend, and wurtzite phases of all materials were investigated by means of classical molecular dynamics simulations.
Abstract: In this study, by means of classical molecular dynamics simulations, we investigate the thermal-transport properties of hexagonal single-layer, zinc-blend, and wurtzite phases of $\mathrm{BN}$, $\mathrm{Al}\mathrm{N}$, and $\mathrm{Ga}\mathrm{N}$ crystals, which are very promising for the application and design of high-quality electronic devices. With this in mind, we generate fully transferable Tersoff-type empirical interatomic potential parameter sets by utilizing an optimization procedure based on particle-swarm optimization. The predicted thermal properties as well as the structural, mechanical, and vibrational properties of all materials are in very good agreement with existing experimental and first-principles data. The impact of isotopes on thermal transport is also investigated and between approximately $10$ and 50% reduction in phonon thermal transport with random isotope distribution is observed in $\mathrm{BN}$ and $\mathrm{Ga}\mathrm{N}$ crystals. Our investigation distinctly shows that the generated parameter sets are fully transferable and very useful in exploring the thermal properties of systems containing these nitrides.
TL;DR: An artificial neural network containing a single hidden layer of 20 nodes which provides a semi-empirical force field potential for elemental titanium and is able to achieve a number of results in agreement with DFT calculations which surpass classical potential formalisms with comparable computational performance.
TL;DR: A self-learning method for constructing T_{e}-dependent interatomic potentials which permit ultralarge-scale atomistic simulations of systems suddenly brought to extreme nonthermal states with density-functional theory (DFT) accuracy.
Abstract: Large-scale simulations using interatomic potentials provide deep insight into the processes occurring in solids subject to external perturbations. The atomistic description of laser-induced ultrafast nonthermal phenomena, however, constitutes a particularly difficult case and has so far not been possible on experimentally accessible length scales and timescales because of two main reasons: (i) ab initio simulations are restricted to a very small number of atoms and ultrashort times and (ii) simulations relying on electronic temperature- (${T}_{e}$) dependent interatomic potentials do not reach the necessary ab initio accuracy. Here we develop a self-learning method for constructing ${T}_{e}$-dependent interatomic potentials which permit ultralarge-scale atomistic simulations of systems suddenly brought to extreme nonthermal states with density-functional theory (DFT) accuracy. The method always finds the global minimum in the parameter space. We derive a highly accurate analytical ${T}_{e}$-dependent interatomic potential $\mathrm{\ensuremath{\Phi}}({T}_{e})$ for silicon that yields a remarkably good description of laser-excited and -unexcited Si bulk and Si films. Using $\mathrm{\ensuremath{\Phi}}({T}_{e})$ we simulate the laser excitation of Si nanoparticles and find strong damping of their breathing modes due to nonthermal melting.
TL;DR: In this paper, a silicon Gaussian approximation machine learning potential suitable for radiation effects was developed and used for the first ab initio simulation of primary damage and evolution of collision collision in a collision scenario.
Abstract: We develop a silicon Gaussian approximation machine learning potential suitable for radiation effects, and use it for the first ab initio simulation of primary damage and evolution of collision cas...
Posted Content•
TL;DR: In this paper, a self-consistent approach based on crystal structure prediction formalism and guided by unsupervised data analysis is proposed to construct an accurate, inexpensive and transferable artificial neural network potential for Carbon.
Abstract: Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modelling. Artificial neural network based approaches for generating potentials are promising; however neural network training requires large amounts of data, sampled adequately from an often unknown potential energy surface. Here we propose a self-consistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis, to construct an accurate, inexpensive and transferable artificial neural network potential. Using this approach, we construct an interatomic potential for Carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond, graphite and graphene, as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.
TL;DR: In this article, the authors used the molecular Monte Carlo method combined with the CRG interatomic potential to investigate stoichiometric mixed oxides fuel U1-yPuyO2 (with y in the range 0-1).