Machine Learning of Molecular Electronic Properties in Chemical Compound Space

The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel, and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning (ML) model, trained on a data base of \textit{ab initio} calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity, and excitation energies. The ML model is based on a deep multi-task artificial neural network, exploiting underlying correlations between various molecular properties. The input is identical to \emph{ab initio} methods, \emph{i.e.} nuclear charges and Cartesian coordinates of all atoms. For small organic molecules the accuracy of such a "Quantum Machine" is similar, and sometimes superior, to modern quantum-chemical methods---at negligible computational cost

Asymptotically stable phase synchronization revealed by autoregressive circle maps

A new type of nonlinear time series analysis is introduced, based on phases, which are defined as polar angles in spaces spanned by a finite number of delayed coordinates. A canonical choice of the polar axis and a related implicit estimation scheme for the potentially underlying auto-regressive circle map (next phase map) guarantee the invertibility of reconstructed phase space trajectories to the original coordinates. The resulting Fourier approximated, Invertibility enforcing Phase Space map (FIPS map) is well suited to detect conditional asymptotic stability of coupled phases. This rather general synchronization criterion unites two existing generalisations of the old concept and can successfully be applied e.g. to phases obtained from ECG and airflow recordings characterizing cardio-respiratory interaction.Comment: PDF file, 232 KB, 24 pages, 3 figures; cheduled for Phys. Rev. E (Nov) 200

Design and Analysis of Cloaked Fluorophores for Rapid Detection and Visualization of Cancer Cells Containing NAD(P)H:Quinone Oxidoreductase-1

The development of fluorogenic substrates for real-time tumor cell detection has led to a vastly expanding field for personal oncology. Fluorophores have been studied as appendages to larger scaffolds leading to accumulation of these dyes in tumor cells or their surrounding environment, taking advantage of tumor anatomy. A new class of fluorophores has been developed in which the dye is an active participant in the mechanism of cancer cell detection. These dyes have been conjugated such that their fluorescence has been eliminated or altered and will undergo a change to reveal their fluorescent signal upon activation by a mechanism that is unique to tumor cells. The research presented in this dissertation encompasses the design, synthesis, properties, and utilization of latent fluorophores that are specifically activated by an enzyme that is highly upregulated in tumor cells, NAD(P)H:quinone oxidoreductase-1 (NQO1). These dyes utilize the 2-electron reduction of quinones to hydroquinones, which NQO1 specifically catalyzes. A dye’s fluorescence can be quenched by conjugating a quinone directly to the fluorophore, only to have its signal uncloaked after activation by NQO1. The objectives in this research will be achieved by: (1) the characterization of properties (stability in biological environments, quantum yields) of the quinone, dyes, and their conjugated counterparts; (2) determination of kinetic parameters (Michaelis constant (Km), theoretical maximum velocity (Vmax), catalytic constant (kcat), enzyme efficiency (kcat/Km) of the substrates towards NQO1 and the way solvent affects such parameters during assay conditions; and (3) utilization of a latent fluorophore for in vivo NQO1 analysis (widefield imaging, confocal single-/two-photon microscopy, flow cytometry) and determining the fate of the released fluorophore. Integration of these studies led to the development of two different latent fluorophores that are readily activated by NQO1. Of these two fluorogenic cancer sensors, one was found to possess a highly novel quenching mechanism between the quinone and the dye

Single-pixel, single-photon three-dimensional imaging

The 3D recovery of a scene is a crucial task with many real-life applications such as self-driving vehicles, X-ray tomography and virtual reality. The recent development of time-resolving detectors sensible to single photons allowed the recovery of the 3D information at high frame rate with unprecedented capabilities. Combined with a timing system, single-photon sensitive detectors allow the 3D image recovery by measuring the Time-of-Flight (ToF) of the photons scattered back by the scene with a millimetre depth resolution. Current ToF 3D imaging techniques rely on scanning detection systems or multi-pixel sensor. Here, we discuss an approach to simplify the hardware complexity of the current 3D imaging ToF techniques using a single-pixel, single-photon sensitive detector and computational imaging algorithms. The 3D imaging approaches discussed in this thesis do not require mechanical moving parts as in standard Lidar systems. The single-pixel detector allows to reduce the pixel complexity to a single unit and offers several advantages in terms of size, flexibility, wavelength range and cost. The experimental results demonstrate the 3D image recovery of hidden scenes with a subsecond acquisition time, allowing also non-line-of-sight scenes 3D recovery in real-time. We also introduce the concept of intelligent Lidar, a 3D imaging paradigm based uniquely on the temporal trace of the return photons and a data-driven 3D retrieval algorithm