arXiv subjects

Jin Soo Lim

Publications and source records attributed to Jin Soo Lim.

3 recordsLinked to original sources

arXiv2022

Micron-scale heterogeneous catalysis with Bayesian force fields from first principles and active learning

Quantum-mechanically accurate reactive molecular dynamics (MD) at the scale of billions of atoms has been achieved for the heterogeneous catalytic system of H$_2$/Pt(111) using the FLARE Bayesian force field. This achievement provides accelerated time-to-solution from first principles, with Bayesian active learning enabling efficient and autonomous training of the machine learning model. The resulting model is then deployed in LAMMPS on GPUs using the Kokkos performance portability library. The Bayesian force field provides quantitative uncertainty of predictions on every atomic environment, critical for detecting configurations in large reactive simulations that are outside of the training set. Scaling benchmarks were performed using real-application MD of the H$_2$/Pt(111) heterogeneous catalysis on the Summit supercomputer, with simulations reaching 0.5 trillion atoms on 4556 GPU nodes.

physics.comp-ph↗

arXiv2021

Active learning of reactive Bayesian force fields: Application to heterogeneous hydrogen-platinum catalysis dynamics

Accurate modeling of chemically reactive systems has traditionally relied on either expensive ab initio approaches or flexible bond-order force fields such as ReaxFF that require considerable time, effort, and expertise to parameterize. Here, we introduce FLARE++, a Bayesian active learning method for training reactive many-body force fields on the fly during molecular dynamics (MD) simulations. During the automated training loop, the predictive uncertainties of a sparse Gaussian process (SGP) force field are evaluated at each timestep of an MD simulation to determine whether additional ab initio data are needed. Once trained, the SGP is mapped onto an equivalent and much faster model that is polynomial in the local environment descriptors and whose prediction cost is independent of the training set size. We apply our method to a canonical reactive system in the field of heterogeneous catalysis, hydrogen splitting and recombination on a platinum (111) surface, obtaining a trained model within three days of wall time that is twice as fast as a recent Pt/H ReaxFF force field and considerably more accurate. Our method is fully open source and is expected to reduce the time and effort required to train fast and accurate reactive force fields for complex systems.

cond-mat.mtrl-sci↗

arXiv2016

Improper magnetic ferroelectricity of nearly pure electronic nature in cycloidal spiral CaMn$_{7}$O$_{12}$

The noncollinear cycloidal magnetic order breaks the inversion symmetry in CaMn$_{7}$O$_{12}$, generating one of the largest spin-orbit driven ferroelectric polarizations measured to date. In this Letter, the microscopic origin of the polarization, including its direction, charge density redistribution, magnetic exchange interactions, and its coupling to the spin helicity, is explored via first principles calculations. The Berry phase computed polarization exhibits almost pure electronic behavior, as the Mn displacements are negligible, $\approx$~0.7~m\textrmÅ. The polarization magnitude and direction are both determined by the Mn spin current, where the \emph{p}-\emph{d} orbital mixing is driven by the inequivalent exchange interactions within the \emph{B}-site Mn cycloidal spiral chains along each Cartesian direction. We employ the generalized spin-current model with Heisenberg-exchange Dzyaloshinskii-Moriya interaction energetics to provide insight into the underlying physics of this spin-driven polarization. Persistent electronic polarization induced by helical spin order in nearly inversion-symmetric ionic crystal lattices suggests opportunities for ultrafast magnetoelectric response.

cond-mat.mtrl-sci↗