Engineering topics
Vashishta, Priya (ORCID:000000034683429X)
Publications and source records attributed to Vashishta, Priya (ORCID:000000034683429X).
Alkali hydroxide (LiOH, NaOH, KOH) in water: Structural and vibrational properties, including neutron scattering results
Structural and vibrational properties of aqueous solutions of alkali hydroxides (LiOH, NaOH, and KOH) are computed using quantum molecular dynamics simulations for solute concentrations ranging between 1 and 10M. Element-resolved partial radial distribution functions, neutron and x-ray structure factors, and angular distribution functions are computed for the three hydroxide solutions as a function of concentration. The vibrational spectra and frequency-dependent conductivity are computed from the Fourier transforms of velocity autocorrelation and current autocorrelation functions. Our results for the structure are validated with the available neutron data for 17M concentration of NaOH in water [Semrouni et al., Phys. Chem. Chem. Phys. 21, 6828 (2019)]. We found that the larger ionic radius [rLi+
INS spectra of NH3 and ND3 obtained at SEQUOIA spectrometer
These INS data were measured at SEQUOIA spectrometer. The samples used were NH3 and ND3, and empty container (background runs). The measurements were done with incident neutron energies Ei=30, 50, 85, 280 and 700 meV at T=5K, 50K, 100K, 180K, 200K for NH3 sample, and with Ei=30, 70, 200, 500 meV at T=5K, 50K, 100K, 180K, 200K and 230K for ND3.
Kibble–Zurek scaling of nonequilibrium phase transition in barium titanate
Far-from-equilibrium phase transition dynamics is one of the grand challenges in modern materials science. A theoretical landmark is the Kibble–Zurek (KZ) scaling to describe the relationship between the temperature quenching rate and the resulting defect density in the vicinity of symmetry-breaking phase transformations. Despite the confirmation of the KZ scaling in ferroic perovskite materials and macroscopic simulations, its atomistic mechanisms remain elusive. Here, we demonstrate the KZ scaling using all-atom molecular dynamics simulations for a prototypical ferroelectric perovskite, barium titanate, with the scaling exponent corresponding to the theoretical prediction for rapid quenching. Simulated diffuse neutron scattering data are presented to guide future experiments.
Review of strategies toward the development of alloy two-dimensional (2D) transition metal dichalcogenides
Not Available
Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials
Abstract Predictive materials synthesis is the primary bottleneck in realizing functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error and there are no known predictive schemes to design synthesis parameters for materials. We use offline reinforcement learning (RL) to predict optimal synthesis schedules, i.e., a time-sequence of reaction conditions like temperatures and concentrations, for the synthesis of semiconducting monolayer MoS 2 using chemical vapor deposition. The RL agent, trained on 10,000 computational synthesis simulations, learned threshold temperatures and chemical potentials for onset of chemical reactions and predicted previously unknown synthesis schedules that produce well-sulfidized crystalline, phase-pure MoS 2 . The model can be extended to multi-task objectives such as predicting profiles for synthesis of complex structures including multi-phase heterostructures and can predict long-time behavior of reacting systems, far beyond the domain of molecular dynamics simulations, making these predictions directly relevant to experimental synthesis.
Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials
Abstract Mechanical behavior of 2D materials such as MoS 2 can be tuned by the ancient art of kirigami. Experiments and atomistic simulations show that 2D materials can be stretched more than 50% by strategic insertion of cuts. However, designing kirigami structures with desired mechanical properties is highly sensitive to the pattern and location of kirigami cuts. We use reinforcement learning (RL) to generate a wide range of highly stretchable MoS 2 kirigami structures. The RL agent is trained by a small fraction (1.45%) of molecular dynamics simulation data, randomly sampled from a search space of over 4 million candidates for MoS 2 kirigami structures with 6 cuts. After training, the RL agent not only proposes 6-cut kirigami structures that have stretchability above 45%, but also gains mechanistic insight to propose highly stretchable (above 40%) kirigami structures consisting of 8 and 10 cuts from a search space of billion candidates as zero-shot predictions.
PND: Physics-informed neural-network software for molecular dynamics applications
Not Available
Mechanical behavior of ultralight nickel metamaterial
The mechanical response of ultralight kagomé structures consisting of hollow nickel (Ni) nanotubes and solid Ni nanorods to compression is studied using molecular dynamics simulations. In both kagomé architectures, 16[112] Shockley partial dislocations and twin formation are observed under compression. The structure made from solid nanorods shows deformation near both the nodes and beams of the kagomé lattice. The hollow kagomé architecture has a higher yield point than the solid kagomé structure. The deformation in the hollow nanotube structure is mostly localized in the nodal region for strains less than 11%. At higher strains, the deformation sets in all the struts and nodes of the hollow kagomé lattice. Owing to this two-stage deformation mechanism, the hollow Ni nanotube kagomé structure shows less bending and greater toughness than the solid Ni nanorod kagomé architecture.
Domain-specific compilers for dynamic simulations of quantum materials on quantum computers
Abstract Simulation of the dynamics of quantum materials is emerging as a promising scientific application for noisy intermediate-scale quantum (NISQ) computers. Due to their high gate-error rates and short decoherence times, however, NISQ computers can only produce high-fidelity results for those quantum circuits smaller than some given circuit size. Dynamic simulations, therefore, pose a challenge as current algorithms produce circuits that grow in size with each subsequent time-step of the simulation. This underscores the crucial role of quantum circuit compilers to produce executable quantum circuits of minimal size, thereby maximizing the range of physical phenomena that can be studied within the NISQ fidelity budget. Here, we present two domain-specific (DS) quantum circuit compilers for the Rigetti and IBM quantum computers, specifically designed to compile circuits simulating dynamics under a special class of time-dependent Hamiltonians. The compilers outperform state-of-the-art general-purpose compilers in terms of circuit size reduction by around 25%–30% as well as wall-clock compilation time by around 40% (dependent on system size and simulation time-step). Drawing on heuristic techniques commonly used in artificial intelligence, both compilers scale well with simulation time-step and system size. Code for both compilers is open-source and packaged into a full-stack quantum simulation software with tutorials included for ease of use for future researchers wishing to perform dynamic simulations of quantum materials on quantum computers. As our DS compilers provide significant improvements in both compilation time and simulation fidelity, they provide a building block for accelerating progress toward physical quantum supremacy.