Sulfurization of MoO 3 in the Chemical Vapor Deposition Synthesis of MoS 2 Enhanced by an H 2 S/H 2 Mixture
Not Available
Hong, Sungwook↗
Engineering topics
Tiwari, Subodh
Publications and source records attributed to Tiwari, Subodh.
EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics
Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines.
97 MATHEMATICS AND COMPUTING↗
Optically Induced Three-Stage Picosecond Amorphization in Low-Temperature SrTiO 3
Not provided.
Chemistry↗
Application of First-Principles-Based Artificial Neural Network Potentials to Multiscale-Shock Dynamics Simulations on Solid Materials
The use of artificial neural network (ANN) potentials trained with first-principles calculations has emerged as a promising approach for molecular dynamics (MD) simulations encompassing large space and time scales while retaining first-principles accuracy. To date, however, the application of ANN-MD has been limited to near-equilibrium processes. Here we combine first-principles-trained ANN-MD with multiscale shock theory (MSST) to successfully describe far-from-equilibrium shock phenomena. Overall, our ANN-MSST-MD approach describes shock-wave propagation in solids with first-principles accuracy but a 5000 times shorter computing time. Accordingly, ANN-MD-MSST was able to resolve fine, long-time elastic deformation at low shock speed, which was impossible with first-principles MD because of the high computational cost. This work thus lays a foundation of ANN-MD simulation to study a wide range of far-from-equilibrium processes.
36 MATERIALS SCIENCE↗