106 research outputs found
Automated Calculation of Thermal Rate Coefficients using Ring Polymer Molecular Dynamics and Machine-Learning Interatomic Potentials with Active Learning
We propose a methodology for fully automated calculation of thermal rate
coefficients of gas phase chemical reactions, which is based on combining the
ring polymer molecular dynamics (RPMD) with the machine-learning interatomic
potentials actively learning on-the-fly. Based on the original computational
procedure implemented in the RPMDrate code, our methodology gradually and
automatically constructs the potential energy surfaces (PESs) from scratch with
the data set points being selected and accumulated during the RPMDrate
simulation. Such an approach ensures that our final machine-learning model
provides reliable description of the PES which avoids artifacts during
exploration of the phase space by RPMD trajectories. We tested our methodology
on two representative thermally activated chemical reactions studied recently
by RPMDrate at temperatures within the interval of 300--1000~K. The
corresponding PESs were generated by fitting to only a few thousands
automatically generated structures (less than 5000) while the RPMD rate
coefficients retained the deviation from the reference values within the
typical convergence error of RPMDrate. In future, we plan to apply our
methodology to chemical reactions which proceed via complex-formation thus
providing a completely general tool for calculating RPMD thermal rate
coefficients for any polyatomic gas phase chemical reaction
Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials
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 CN nanosheets. CN 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 for CN 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
Exploring Phononic Properties of Two-Dimensional Materials using Machine Learning Interatomic Potentials
Phononic properties are commonly studied by calculating force constants using
the density functional theory (DFT) simulations. Although DFT simulations offer
accurate estimations of phonon dispersion relations or thermal properties, but
for low-symmetry and nanoporous structures the computational cost quickly
becomes very demanding. Moreover, the computational setups may yield
nonphysical imaginary frequencies in the phonon dispersion curves, impeding the
assessment of phononic properties and the dynamical stability of the considered
system. Here, we compute phonon dispersion relations and examine the dynamical
stability of a large ensemble of novel materials and compositions. We propose a
fast and convenient alternative to DFT simulations which derived from
machine-learning interatomic potentials passively trained over computationally
efficient ab-initio molecular dynamics trajectories. Our results for diverse
two-dimensional (2D) nanomaterials confirm that the proposed computational
strategy can reproduce fundamental thermal properties in close agreement with
those obtained via the DFT approach. The presented method offers a stable,
efficient, and convenient solution for the examination of dynamical stability
and exploring the phononic properties of low-symmetry and porous 2D materials
Precision Gauge Unification from Extra Yukawa Couplings
We investigate the impact of extra vector-like GUT multiplets on the
predicted value of the strong coupling. We find in particular that Yukawa
couplings between such extra multiplets and the MSSM Higgs doublets can resolve
the familiar two-loop discrepancy between the SUSY GUT prediction and the
measured value of alpha_3. Our analysis highlights the advantages of the
holomorphic scheme, where the perturbative running of gauge couplings is
saturated at one loop and further corrections are conveniently described in
terms of wavefunction renormalization factors. If the gauge couplings as well
as the extra Yukawas are of O(1) at the unification scale, the relevant
two-loop correction can be obtained analytically. However, the effect persists
also in the weakly-coupled domain, where possible non-perturbative corrections
at the GUT scale are under better control.Comment: 26 pages, LaTeX. v6: Important early reference adde
Exploring van der Waals materials with high anisotropy: geometrical and optical approaches
The emergence of van der Waals (vdW) materials resulted in the discovery of
their giant optical, mechanical, and electronic anisotropic properties,
immediately enabling countless novel phenomena and applications. Such success
inspired an intensive search for the highest possible anisotropic properties
among vdW materials. Furthermore, the identification of the most promising
among the huge family of vdW materials is a challenging quest requiring
innovative approaches. Here, we suggest an easy-to-use method for such a survey
based on the crystallographic geometrical perspective of vdW materials followed
by their optical characterization. Using our approach, we found As2S3 as a
highly anisotropic vdW material. It demonstrates rare giant in-plane optical
anisotropy, high refractive index and transparency in the visible range,
overcoming the century-long record set by rutile. Given these benefits, As2S3
opens a pathway towards next-generation nanophotonics as demonstrated by an
ultrathin true zero-order quarter-waveplate that combines classical and the
Fabry-Perot optical phase accumulations. Hence, our approach provides an
effective and easy-to-use method to find vdW materials with the utmost
anisotropic properties.Comment: 11 pages, 5 figure
- …