Publications by authors named "Jonathan P Mailoa"

Ab-initio methods such as density functional theory (DFT) is useful for fundamental atomistic-level study and is widely used across many scientific fields, including for the discovery of electrochemical reaction byproducts. However, many DFT steps may be needed to discover rare electrochemical reaction byproducts, which limits DFT's scalability. In this work, we demonstrate that it is possible to generate many elementary electrochemical reaction byproducts in-silico using just a small number of ab-initio energy minimization steps if it is done in a multi-scale manner, such as via previously reported tiered tensor transform (3T) method.

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TenCirChem is an open-source Python library for simulating variational quantum algorithms for quantum computational chemistry. TenCirChem shows high-performance in the simulation of unitary coupled-cluster circuits, using compact representations of quantum states and excitation operators. Additionally, TenCirChem supports noisy circuit simulation and provides algorithms for variational quantum dynamics.

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Article Synopsis
  • - NequIP is a new neural network model that uses E(3)-equivariant convolutions to learn interatomic potentials from quantum mechanical calculations, improving how atomic environments are represented.
  • - This approach achieves top-tier accuracy across various molecules and materials, showing the ability to work effectively with much less training data than traditional models.
  • - By requiring significantly fewer training samples, NequIP challenges the notion that deep learning networks need large datasets, enabling accurate simulations of molecular dynamics over extended time periods.
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The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules.

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Electrochemical stability windows of electrolytes largely determine the limitations of operating regimes of lithium-ion batteries, but the degradation mechanisms are difficult to characterize and poorly understood. Using computational quantum chemistry to investigate the oxidative decomposition that govern voltage stability of multi-component organic electrolytes, we find that electrolyte decomposition is a process involving the solvent and the salt anion and requires explicit treatment of their coupling. We find that the ionization potential of the solvent-anion system is often lower than that of the isolated solvent or the anion.

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We show that strong cation-anion interactions in a wide range of lithium-salt/ionic liquid mixtures result in a negative lithium transference number, using molecular dynamics simulations and rigorous concentrated solution theory. This behavior fundamentally deviates from that obtained using self-diffusion coefficient analysis and explains well recent experimental electrophoretic nuclear magnetic resonance measurements, which account for ion correlations. We extend these findings to several ionic liquid compositions.

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Quasi-elastic neutron scattering experiments on mixtures of poly(ethylene oxide) and lithium bis(trifluoromethane)sulfonimide salt, a standard polymer electrolyte, led to the quantification of the effect of salt on segmental dynamics in the 1-10 Å length scale. The monomeric friction coefficient characterizing segmental dynamics on these length scales increases exponentially with salt concentration. More importantly, we find that this change in monomeric friction alone is responsible for all of the observed nonlinearity in the dependence of ionic conductivity on salt concentration.

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The power conversion efficiency of solar cells based on copper (I) oxide (Cu2 O) is enhanced by atomic layer deposition of a thin gallium oxide (Ga2 O3 ) layer. By improving band-alignment and passivating interface defects, the device exhibits an open-circuit voltage of 1.20 V and an efficiency of 3.

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Room-temperature infrared sub-band gap photoresponse in silicon is of interest for telecommunications, imaging and solid-state energy conversion. Attempts to induce infrared response in silicon largely centred on combining the modification of its electronic structure via controlled defect formation (for example, vacancies and dislocations) with waveguide coupling, or integration with foreign materials. Impurity-mediated sub-band gap photoresponse in silicon is an alternative to these methods but it has only been studied at low temperature.

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