Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption

Much of machine learning relies on the use of large amounts of data to train models to make predictions. When this data comes from multiple sources, for example when evaluation of data against a machine learning model is offered as a service, there can be privacy issues and legal concerns over the sharing of data. Fully homomorphic encryption (FHE) allows data to be computed on whilst encrypted, which can provide a solution to the problem of data privacy. However, FHE is both slow and restrictive, so existing algorithms must be manipulated to make them work efficiently under the FHE paradigm. Some commonly used machine learning algorithms, such as Gaussian process regression, are poorly suited to FHE and cannot be manipulated to work both efficiently and accurately. In this paper, we show that a modular approach, which applies FHE to only the sensitive steps of a workflow that need protection, allows one party to make predictions on their data using a Gaussian process regression model built from another party's data, without either party gaining access to the other's data, in a way which is both accurate and efficient. This construction is, to our knowledge, the first example of an effectively encrypted Gaussian process

An optimized intermolecular force field for hydrogen bonded organic molecular crystals using atomic multipole electrostatics

We present a re-parameterization of the a popular intermolecular force field for describing intermolecular interactions in the organic solid state. Specifically, we optimize the performance of the exp-6 force field when used in conjunction with atomic multipole electrostatics. We also parameterize force fields that are optimized for use with multipoles derived from polarized molecular electron densities, to account for induction effects in molecular crystals. Parameterization is performed against a set of 186 experimentally determined, low temperature crystal structures and 53 measured sublimation enthalpies of hydrogen bonding organic molecules. The resulting force fields are tested on a validation set of 129 crystal structures and show improved reproduction of the structures and lattice energies of a range of organic molecular crystals compared to the original force field with atomic partial charge electrostatics. Unit cell dimensions of the validation set are typically reproduced to within 3% with the re-parameterized force fields. Lattice energies, which were all included during parameterisation, are systematically underestimated when compared to measured sublimation enthalpies, with mean absolute errors of between 7.4 and 9.0%

An optimized intermolecular force field for hydrogen bonded organic molecular crystals using atomic multipole electrostatics

This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by the International Union of Crystallography.We present a re-parameterization of the a popular intermolecular force field for describing intermolecular interactions in the organic solid state. Specifically, we optimize the performance of the exp-6 force field when used in conjunction with atomic multipole electrostatics. We also parameterize force fields that are optimized for use with multipoles derived from polarized molecular electron densities, to account for induction effects in molecular crystals. Parameterization is performed against a set of 186 experimentally determined, low temperature crystal structures and 53 measured sublimation enthalpies of hydrogen bonding organic molecules. The resulting force fields are tested on a validation set of 129 crystal structures and show improved reproduction of the structures and lattice energies of a range of organic molecular crystals compared to the original force field with atomic partial charge electrostatics. Unit cell dimensions of the validation set are typically reproduced to within 3% with the re-parameterized force fields. Lattice energies, which were all included during parameterisation, are systematically underestimated when compared to measured sublimation enthalpies, with mean absolute errors of between 7.4 and 9.0%.Engineering and Physical Sciences Research Council (Doctoral Training Account studentship

A Bayesian approach to calibrating high-throughput virtual screening results and application to organic photovoltaic materials

A novel approach for calibrating quantum-chemical properties determined as part of a high-throughput virtual screen to experimental analogs is presented. Information on the molecular graph is extracted through the use of extended connectivity fingerprints, and exploited using a Gaussian process to calibrate both electronic properties such as frontier orbital energies, and optical gaps and device properties such as short circuit current density, open circuit voltage and power conversion efficiency. The Bayesian nature of this process affords a value for uncertainty in addition to each calibrated value. This allows the researcher to gain intuition about the model as well as the ability to respect its bounds.Chemistry and Chemical Biolog