Imputation of Assay Bioactivity Data Using Deep Learning.

We describe a novel deep learning neural network method and its application to impute assay pIC50 values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and commercial databases, enabling it to learn directly from correlations between activities measured in different assays. In two case studies on public domain data sets we show that the neural network method outperforms traditional quantitative structure-activity relationship (QSAR) models and other leading approaches. Furthermore, by focusing on only the most confident predictions the accuracy is increased to R2 > 0.9 using our method, as compared to R2 = 0.44 when reporting all predictions.

Direct evaluation of the force constant matrix in quantum Monte Carlo.

We develop a formalism to directly evaluate the matrix of force constants within a Quantum Monte Carlo calculation. We utilize the matrix of force constants to accurately relax the positions of atoms in molecules and determine their vibrational modes, using a combination of variational and diffusion Monte Carlo. The computed bond lengths differ by less than 0.007 Šfrom the experimental results for all four tested molecules. For hydrogen and hydrogen chloride, we obtain fundamental vibrational frequencies within 0.1% of experimental results and ∼10 times more accurate than leading computational methods. For carbon dioxide and methane, the vibrational frequency obtained is on average within 1.1% of the experimental result, which is at least 3 times closer than results using restricted Hartree-Fock and density functional theory with a Perdew-Burke-Ernzerhof functional and comparable or better than density functional theory with a semi-empirical functional.

Inhomogeneous state of few-fermion superfluids.

The few-fermion atomic gas is an ideal setting to explore inhomogeneous superfluid pairing analogous to the Larkin-Ovchinnikov state. Two up and one down-spin atom is the minimal configuration that displays an inhomogeneous pairing density, whereas imbalanced systems containing more fermions present a more complex pairing topology. With more than eight atoms trapped the system approaches the macroscopic superfluid limit. An oblate trap with a central barrier offers a direct experimental probe of pairing inhomogeneity.

Strategies for improving the efficiency of quantum Monte Carlo calculations.

We describe a number of strategies for minimizing and calculating accurately the statistical uncertainty in quantum Monte Carlo calculations. We investigate the impact of the sampling algorithm on the efficiency of the variational Monte Carlo method. We then propose a technique to maximize the efficiency of the linear extrapolation of diffusion Monte Carlo results to zero time step, finding that a relative time-step ratio of 1:4 is optimal. Finally, we discuss the removal of serial correlation from data sets by reblocking, setting out criteria for the choice of block length and quantifying the effects of the uncertainty in the estimated correlation length.

Repulsive atomic gas in a harmonic trap on the border of itinerant ferromagnetism.

Alongside superfluidity, itinerant (Stoner) ferromagnetism remains one of the most well-characterized phases of correlated Fermi systems. A recent experiment has reported the first evidence for novel phase behavior on the repulsive side of the Feshbach resonance in a two-component ultracold Fermi gas. By adapting recent theoretical studies to the atomic trap geometry, we show that an adiabatic ferromagnetic transition would take place at a weaker interaction strength than is observed in experiment. This discrepancy motivates a simple nonequilibrium theory that takes account of the dynamics of magnetic defects and three-body losses. The formalism developed displays good quantitative agreement with experiment.

Inhomogeneous phase formation on the border of itinerant ferromagnetism.

A variety of analytical techniques suggest that quantum fluctuations lead to a fundamental instability of the Fermi liquid that drives ferromagnetic transitions first order at low temperatures. We present both analytical and numerical evidence that, driven by the same quantum fluctuations, this first order transition is preempted by the formation of an inhomogeneous magnetic phase. This occurs in a manner that is closely analogous to the formation of the inhomogeneous superconducting Fulde-Ferrel-Larkin-Ovchinnikov state. We derive these results from a field-theoretical approach supplemented with numerical quantum Monte Carlo simulations.