Aditya Savara

Coarse-grained (CG) molecular dynamics (MD) simulations have emerged as a powerful and cost-effective approach for modeling materials by simplifying atomic structures into CG beads. However, accurately parametrizing interatomic potential models (force fields, FFs) that can reliably reproduce material properties and quantifying the uncertainties associated with both the model parameters and their predictions remains a major challenge. In this study, we developed coarse-grained embedded atom method (CG EAM) potentials to model interatomic interactions in face-centered cubic (FCC) metals, including palladium (Pd), gold (Au), silver (Ag), copper (Cu), and platinum (Pt). The CG EAM potentials combine the physical interpretability of a traditional EAM with the computational efficiency of coarse-graining. We first employed a Particle Swarm Optimization (PSO) framework integrated with CG MD simulations to explore a 14-dimensional parameter space and identify CG EAM parameters that reproduce key physical, mechanical, and thermodynamic properties, such as cohesive energy, lattice constants, and elastic moduli. These parameters were subsequently refined using a Bayesian uncertainty quantification (BUQ) approach, which allowed the systematic assessment of uncertainties in both the FF parameters and the predicted properties. For all five metals, this framework yielded robust parameter ranges within which the predicted properties generally remained within their 95% confidence intervals. Overall, this integrated parameter optimization and BUQ approach provides an effective strategy for developing accurate and reliable interatomic potentials while offering a generalizable framework for designing both hard and soft materials with targeted properties.

Silica-supported ZnO efficiently catalyzes the nonoxidative dehydrogenation of ethanol to acetaldehyde, which is relevant for production of 1,3-butadiene from bioethanol. Characterization with in situ spectroscopies under dehydrated conditions (high sensitivity-low energy ion scattering (HS-LEIS), diffuse reflectance (DR) UV–vis, X-ray absorption spectroscopy (XAS), diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS), inelastic neutron scattering (INS), and UV Raman), and ammonia adsorption probed by temperature-programmed desorption followed by DRIFTS and mass spectrometry (DRIFTS-MS NH₃-TPD), and DFT calculations revealed that the supported ZnO _x phase was present as isolated surface ZnO _x sites on SiO₂, with the vast majority coordinated by two siloxane bonds and one silicon atom with two nonbridging oxygens ((SiO)₂Zn²⁺O₂Si=), anchored at 4-, 5-, and 6-membered siloxane rings. A minor fraction of surface ZnO _x sites possessed Lewis acidity, and even fewer sites possessed a Bro̷nsted acidic Zn­(OH)⁺Si moiety. Ethanol temperature-programmed surface reaction-mass spectrometry (TPSR-MS) with various oxidative or ethanol reaction pretreatments indicated that only sites with Lewis and Bro̷nsted acidic character (Zn­(OH)⁺Si) were active for ethanol dehydrogenation, while the majority surface (SiO)₂Zn²⁺O₂Si= sites were inactive. Greater heterogeneity among all surface ZnO _x sites, as assessed by in situ DR UV–vis spectroscopy, was associated with a greater number of ZnO _x sites that were active for ethanol dehydrogenation as well as lower enthalpic barriers for acetaldehyde production among the most active surface ZnO _x sites. Turnover frequencies and the apparent activation energy for ethanol dehydrogenation were determined from steady-state kinetics. Together, these findings suggested that anchoring inactive surface (SiO)₂Zn²⁺O₂Si= sites on the silica support caused a greater number of active surface ZnO _x sites to adopt a more strained configuration, promoting ethanol dehydrogenation catalysis. Pretreatments and catalysts that promoted desorption of ethanol during TPSR, taken as a marker of surface dehydroxylation, were associated with an increased number of the most active surface (Zn­(OH)⁺Si) sites. Such findings suggested that inactive surface ZnO _x sites were activated for ethanol dehydrogenation by dehydroxylation of the support and/or decreased coordination to hemilabile siloxane ligands.

New Bayesian parameter estimation methods have the capability to enable more physically realistic and reliable molecular dynamics (MD) simulations by providing accurate estimates of uncertainties of force-field (FF) parameters and associated properties. However, the choice of which Bayesian parameter estimation algorithm to use has not been widely investigated, despite its impact on the effective exploration of parameter space. Here, using a case example of the Embedded Atom Method (EAM) FF parameters, we investigated the ramifications of several of the algorithm choices. We found that Ensemble Slice Sampling (ESS) and Affine-Invariant Ensemble Sampling (AIES) demonstrate a new level of superior performance, culminating in more accurate parameter and property estimations with tighter uncertainty bounds, compared to traditional methods such as Metropolis-Hastings (MH), Gradient Search (GS), and Uniform Random Sampler (URS). We demonstrate that Bayesian Uncertainty Quantification with ESS and AIES leads to significantly more accurate and reliable predictions of the FF parameters and properties. The results suggest that ESS and AIES should be used to obtain more accurate parameter and uncertainty estimations while providing deeper physical insights.

Platinum nanoparticles (Pt-NPs) supported on titania surfaces are costly but indispensable heterogeneous catalysts because of their highly effective and selective catalytic properties. Therefore, it is vital to understand their physicochemical processes during catalysis to optimize their use and to further develop better catalysts. However, simulating these dynamic processes is challenging due to the need for a reliable quantum chemical method to describe chemical bond breaking and bond formation during the processes but, at the same time, fast enough to sample a large number of configurations required to compute the corresponding free energy surfaces. Density functional theory (DFT) is often used to explore Pt-NPs; nonetheless, it is usually limited to some minimum-energy reaction pathways on static potential energy surfaces because of its high computational cost. We report here a combination of the density functional tight binding (DFTB) method as a fast but reliable approximation to DFT, the steered molecular dynamics (SMD) technique, and the Jarzynski equality to construct free energy surfaces of the temperature-dependent diffusion and growth of platinum particles on a titania surface. In particular, we present the parametrization for Pt-X (X = Pt, Ti, or O) interactions in the framework of the second-order DFTB method, using a previous parametrization for titania as a basis. The optimized parameter set was used to simulate the surface diffusion of a single platinum atom (Pt₁) and the growth of Pt₆ from Pt₅ and Pt₁ on the rutile (110) surface at three different temperatures (T = 400, 600, 800 K). The free energy profile was constructed by using over a hundred SMD trajectories for each process. We found that increasing the temperature has a minimal effect on the formation free energy; nevertheless, it significantly reduces the free energy barrier of Pt atom migration on the TiO₂ surface and the transition state (TS) of its deposition. In a concluding remark, the methodology opens the pathway to quantum chemical free energy simulations of Pt-NPs' temperature-dependent growth and other transformation processes on the titania support.