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Preferential Bayesian optimization (PBO) is a framework for optimizing a decision-maker's latent preferences over available design choices. While preferences often involve multiple conflicting objectives, existing work in PBO assumes that preferences can be encoded by a single objective function. For example, in robotic assistive devices, technicians often attempt to maximize user comfort while simultaneously minimizing mechanical energy consumption for longer battery life. Similarly, in autonomous driving policy design, decision-makers wish to understand the trade-offs between multiple safety and performance attributes before committing to a policy. To address this gap, we propose the first framework for PBO with multiple objectives. Within this framework, we present dueling scalarized Thompson sampling (DSTS), a multi-objective generalization of the popular dueling Thompson algorithm, which may be of interest beyond the PBO setting. We evaluate DSTS across four synthetic test functions and two simulated exoskeleton personalization and driving policy design tasks, showing that it outperforms several benchmarks. Finally, we prove that DSTS is asymptotically consistent. As a direct consequence, this result provides, to our knowledge, the first convergence guarantee for dueling Thompson sampling in the PBO setting.

While all of the polymorphs of pure NH$_4$I and KI are non-polar, we identify that (NH$_4$)$_{0.95}$K$_{0.05}$I is ferroelectric and (NH$_4$)$_{0.87}$K$_{0.13}$I and (NH$_4$)$_{0.83}$K$_{0.17}$I are pyroelectric through measurements of their pyroelectric current and complex dielectric constant. The order to disorder phase transitions occur near 245 K. Magnetic susceptibility measurements indicate that the proton orbitals of the NH$_4$$^+$ continue to become ordered in the ground state in the (NH$_4$)$_{1-x}$K$_x$I system up to x <= 0.17. The polar phases are proposed to stem from K$^+$ ions disrupting the symmetry of proton-orbital-lattice interactions between the NH$_4$$^+$ and I$^-$ ions. Our work introduces a new pathway for the ordered phases of ammonium-based compounds to potentially become ferroelectric., Comment: 13 pages, 5 figures

The microscopic origin of the remarkable optoelectronic properties of one of the most studied contemporary materials remains unclear. Here, we identify the existence of magnetic interactions between intermolecular proton orbitals in CH$_3$NH$_3$PbI$_3$ and CH$_3$NH$_3$PbBr$_3$. In particular, a unique sharp drop and a pronounced step-up discontinuity in the magnetic susceptibility at the tetragonal-to-cubic phase transitions are identified in CH$_3$NH$_3$PbI$_3$ and CH$_3$NH$_3$PbBr$_3$, respectively. The magnetic interactions in the orthorhombic and tetragonal phases are dependent on thermal history and lattice orientation while nearly independent of the applied external magnetic field. In CH$_3$NH$_3$PbBr$_3$, the CH$_3$ and NH$_3$$^+$ components reorient in an uncorrelated fashion resulting the cubic phase to also exhibit magnetic anisotropy. Our findings provide a potential link connecting the highly light-absorbing CH$_3$NH$_3$$^+$ and the exceptional properties of the charge carriers of the inorganic framework in hybrid perovskite solar cells., Comment: Manuscript + Supplementary Material file (17 + 6 pages, 4 + 2 figures)

The $\beta$ and $\gamma$ phases of methylammonium chloride CH$_3$NH$_3$Cl and methylammonium bromide CH$_3$NH$_3$Br are identified to be ferroelectric $via$ pyroelectric current and dielectric constant measurements. The magnetic susceptibility also exhibits pronounced discontinuities at the Curie temperatures. We attribute the origin of spontaneous polarization to the emergence of two groups of proton orbital magnetic moments from the uncorrelated motion of the CH$_3$ and NH$_3$ groups in the $\beta$ and $\gamma$ phases. The two inequivalent frameworks of intermolecular orbital resonances interact with each other to distort the lattice in a non-centrosymmetric fashion. Our findings indicate that the structural instabilities in molecular frameworks are magnetic in origin as well as provide a new pathway toward uncovering new organic ferroelectrics., Comment: 5 pages, 5 figures

Sequential optimization methods are often confronted with the curse of dimensionality in high-dimensional spaces. Current approaches under the Gaussian process framework are still burdened by the computational complexity of tracking Gaussian process posteriors and need to partition the optimization problem into small regions to ensure exploration or assume an underlying low-dimensional structure. With the idea of transiting the candidate points towards more promising positions, we propose a new method based on Markov Chain Monte Carlo to efficiently sample from an approximated posterior. We provide theoretical guarantees of its convergence in the Gaussian process Thompson sampling setting. We also show experimentally that both the Metropolis-Hastings and the Langevin Dynamics version of our algorithm outperform state-of-the-art methods in high-dimensional sequential optimization and reinforcement learning benchmarks.