G. Conduit

Publications92
h-index17
Citations1,049
Highly Influential Citations23
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Inhomogeneous phase formation on the border of itinerant ferromagnetism.
TLDR
Analytical and numerical evidence is presented that, driven by the same quantum fluctuations, this first order transition is preempted by the formation of an inhomogeneous magnetic phase in the Fulde-Ferrel-Larkin-Ovchinnikov state.
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Predicting the state of charge and health of batteries using data-driven machine learning
TLDR
How machine learning methods and high-throughput experimentation provide a data-driven approach to this problem are discussed, and challenges in building models which provide fast and accurate battery state predictions are highlighted.
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Superfluidity at the BEC-BCS crossover in two-dimensional Fermi gases with population and mass imbalance
We explore the zero-temperature phase behavior of a two-dimensional two-component atomic Fermi gas with population and mass imbalance in the regime of the BEC-BCS crossover. Working in the mean-field…
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Design of a nickel-base superalloy using a neural network
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OPTIMADE, an API for exchanging materials data
The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We…
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Imputation of Assay Bioactivity Data Using Deep Learning
TLDR
In two case studies on public domain data sets, it is shown that the neural network method outperforms traditional quantitative structure-activity relationship (QSAR) models and other leading approaches and by focusing on only the most confident predictions the accuracy is increased.
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Strategies for improving the efficiency of quantum Monte Carlo calculations.
TLDR
A technique to maximize the efficiency of the linear extrapolation of diffusion Monte Carlo results to zero time step is proposed, finding that a relative time-step ratio of 1:4 is optimal.
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Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data
TLDR
A state-of-the-art deep learning method is used, Alchemite™, to impute data from drug discovery projects, including multi-target biochemical activities, phenotypic activities in cell-based assays, and a variety of absorption, distribution, metabolism, and excretion endpoints.
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Microscopic theory of the magnetoresistance of disordered superconducting films
Experiments on disordered superconducting thin films have revealed a magnetoresistance peak of several orders of magnitude. Starting from the disordered negative-U Hubbard model, we employ an ab…
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Ferromagnetic spin correlations in a few-fermion system
We study the spin correlations of a few fermions in a quasi one-dimensional trap. Exact diagonalization calculations demonstrate that repulsive interactions between the two species drives…
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