Efficient Training and Implementation of Gaussian Process Potentials

Molecular simulations are a powerful tool for translating information about the intermolecular interactions within a system to thermophysical properties via statistical mechanics. However, the accuracy of any simulation is limited by the potentials that model the microscopic interactions. Most first principles methods are too computationally expensive for use at every time-step or cycle of a simulation, which require typically thousands of energy evaluations. Meanwhile, cheaper semi-empirical potentials give rise to only qualitatively accurate simulations. Consequently, methods for efficient first principles predictions in simulations are of interest. Machine-learned potentials (MLPs) have shown promise in this area, offering first principles predictions at a fraction of the cost of ab initio calculation. Of particular interest are Gaussian process (GP) potentials, which achieve equivalent accuracy to other MLPs with smaller training sets. They therefore offer the best route to employing information from expensive ab initio calculations, for which building a large data set is time-consuming. GP potentials, however, are among the most computationally intensive MLPs. Thus, they are far more costly to employ in simulations than semi-empirical potentials. This work addresses the computational expense of GP potentials by both reducing the training set size at a given accuracy and developing a method to invoke GP potentials efficiently for first principles prediction in simulations. By varying the cross-over distance between the GP and a long-range function with the accuracy of the former, training by sequential design requires up to 40 % fewer training points at fixed accuracy. This method was applied successfully to the CO-Ne, HF-Ne, HF-Na+, CO2-Ne, 2CO, 2HF and 2HCl systems, and can be extended easily to other interactions and methods of prediction. Meanwhile, a significant reduction in the time taken for Monte Carlo displacement and volume change moves is achieved by parallelisation of the requisite GP calculations. Though this exploits in part the framework of GP regression, the distribution of the calculations themselves is general to other methods of prediction. The work also shows that current kernels and input transforms for modelling intermolecular interactions are not improved easily

Efficient Training and Implementation of Gaussian Process Potentials

Molecular simulations are a powerful tool for translating information about the intermolecular interactions within a system to thermophysical properties via statistical mechanics. However, the accuracy of any simulation is limited by the potentials that model the microscopic interactions. Most first principles methods are too computationally expensive for use at every time-step or cycle of a simulation, which require typically thousands of energy evaluations. Meanwhile, cheaper semi-empirical potentials give rise to only qualitatively accurate simulations. Consequently, methods for efficient first principles predictions in simulations are of interest. Machine-learned potentials (MLPs) have shown promise in this area, offering first principles predictions at a fraction of the cost of ab initio calculation. Of particular interest are Gaussian process (GP) potentials, which achieve equivalent accuracy to other MLPs with smaller training sets. They therefore offer the best route to employing information from expensive ab initio calculations, for which building a large data set is time-consuming. GP potentials, however, are among the most computationally intensive MLPs. Thus, they are far more costly to employ in simulations than semi-empirical potentials. This work addresses the computational expense of GP potentials by both reducing the training set size at a given accuracy and developing a method to invoke GP potentials efficiently for first principles prediction in simulations. By varying the cross-over distance between the GP and a long-range function with the accuracy of the former, training by sequential design requires up to 40 % fewer training points at fixed accuracy. This method was applied successfully to the CO-Ne, HF-Ne, HF-Na+, CO2-Ne, 2CO, 2HF and 2HCl systems, and can be extended easily to other interactions and methods of prediction. Meanwhile, a significant reduction in the time taken for Monte Carlo displacement and volume change moves is achieved by parallelisation of the requisite GP calculations. Though this exploits in part the framework of GP regression, the distribution of the calculations themselves is general to other methods of prediction. The work also shows that current kernels and input transforms for modelling intermolecular interactions are not improved easily

Gaussian process models of potential energy surfaces with boundary optimization

A strategy is outlined to reduce the number of training points required to model intermolecular potentials using Gaussian processes, without reducing accuracy. An asymptotic function is used at a long range, and the crossover distance between this model and the Gaussian process is learnt from the training data. The results are presented for different implementations of this procedure, known as boundary optimization, across the following dimer systems: CO-Ne, HF-Ne, HF-Na+, CO2-Ne, and (CO2)2. The technique reduces the number of training points, at fixed accuracy, by up to ∼49%, compared to our previous work based on a sequential learning technique. The approach is readily transferable to other statistical methods of prediction or modeling problems

The Potential of Subsampling and Inpainting for Fast Low-Dose Cryo FIB-SEM Imaging and Tomography

Traditional image acquisition for cryo focused ion-beam scanning electron microscopy tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the FIB-SEM without an appreciable low in image quality. In this work, experimental data is presented which validates subsampling and inpainting as a useful tool for convenient and reliable data acquisition in a FIB-SEM, with new methods of handling 3-dimensional data being employed in context of dictionary learning and inpainting algorithms using a newly developed microscope control software and data recovery algorithm.Comment: In submission to "Microscopy and Microanalysis" journal. Authorship reviewed from previous submissio

Activation of Muscarinic M1 Acetylcholine Receptors Induces Long-Term Potentiation in the Hippocampus

Muscarinic M1 acetylcholine receptors (M1Rs) are highly expressed in the hippocampus, and their inhibition or ablation disrupts the encoding of spatial memory. It has been hypothesized that the principal mechanism by which M1Rs influence spatial memory is by the regulation of hippocampal synaptic plasticity. Here, we use a combination of recently developed, well characterized, selective M1R agonists and M1R knock-out mice to define the roles of M1Rs in the regulation of hippocampal neuronal and synaptic function. We confirm that M1R activation increases input resistance and depolarizes hippocampal CA1 pyramidal neurons and show that this profoundly increases excitatory postsynaptic potential-spike coupling. Consistent with a critical role for M1Rs in synaptic plasticity, we now show that M1R activation produces a robust potentiation of glutamatergic synaptic transmission onto CA1 pyramidal neurons that has all the hallmarks of long-term potentiation (LTP): The potentiation requires NMDA receptor activity and bi-directionally occludes with synaptically induced LTP. Thus, we describe synergistic mechanisms by which acetylcholine acting through M1Rs excites CA1 pyramidal neurons and induces LTP, to profoundly increase activation of CA1 pyramidal neurons. These features are predicted to make a major contribution to the pro-cognitive effects of cholinergic transmission in rodents and humans

In silico Ptychography of Lithium-ion Cathode Materials from Subsampled 4-D STEM Data

High quality scanning transmission electron microscopy (STEM) data acquisition and analysis has become increasingly important due to the commercial demand for investigating the properties of complex materials such as battery cathodes; however, multidimensional techniques (such as 4-D STEM) which can improve resolution and sample information are ultimately limited by the beam-damage properties of the materials or the signal-to-noise ratio of the result. subsampling offers a solution to this problem by retaining high signal, but distributing the dose across the sample such that the damage can be reduced. It is for these reasons that we propose a method of subsampling for 4-D STEM, which can take advantage of the redundancy within said data to recover functionally identical results to the ground truth. We apply these ideas to a simulated 4-D STEM data set of a LiMnO2 sample and we obtained high quality reconstruction of phase images using 12.5% subsampling

C-tactile afferents: Cutaneous mediators of oxytocin release during affiliative tactile interactions?

Low intensity, non-noxious, stimulation of cutaneous somatosensory nerves has been shown to trigger oxytocin release and is associated with increased social motivation, plus reduced physiological and behavioural reactivity to stressors. However, to date, little attention has been paid to the specific nature of the mechanosensory nerves which mediate these effects. In recent years, the neuroscientific study of human skin nerves (microneurography studies on single peripheral nerve fibres) has led to the identification and characterisation of a class of touch sensitive nerve fibres named C-tactile afferents. Neither itch nor pain receptive, these unmyelinated, low threshold mechanoreceptors, found only in hairy skin, respond optimally to low force/velocity stroking touch. Notably, the speed of stroking which c-tactile afferents fire most strongly to is also that which people perceive to be most pleasant. The social touch hypothesis posits that this system of nerves has evolved in mammals to signal the rewarding value of physical contact in nurturing and social interactions. In support of this hypothesis, in this paper we review the evidence that cutaneous stimulation directly targeted to optimally activate c-tactile afferents reduces physiological arousal, carries a positive affective value and, under healthy conditions, inhibits responses to painful stimuli. These effects mirror those, we also review, which have been reported following endogenous release and exogenous administration of oxytocin. Taken together this suggests C-tactile afferent stimulation may mediate oxytocin release during affiliative tactile interactions

Colliding Worlds: Asteroid research and the legitimization of war in space

Accepted versio

Effectiveness of a national quality improvement programme to improve survival after emergency abdominal surgery (EPOCH): a stepped-wedge cluster-randomised trial

Background: Emergency abdominal surgery is associated with poor patient outcomes. We studied the effectiveness of a national quality improvement (QI) programme to implement a care pathway to improve survival for these patients. Methods: We did a stepped-wedge cluster-randomised trial of patients aged 40 years or older undergoing emergency open major abdominal surgery. Eligible UK National Health Service (NHS) hospitals (those that had an emergency general surgical service, a substantial volume of emergency abdominal surgery cases, and contributed data to the National Emergency Laparotomy Audit) were organised into 15 geographical clusters and commenced the QI programme in a random order, based on a computer-generated random sequence, over an 85-week period with one geographical cluster commencing the intervention every 5 weeks from the second to the 16th time period. Patients were masked to the study group, but it was not possible to mask hospital staff or investigators. The primary outcome measure was mortality within 90 days of surgery. Analyses were done on an intention-to-treat basis. This study is registered with the ISRCTN registry, number ISRCTN80682973. Findings: Treatment took place between March 3, 2014, and Oct 19, 2015. 22 754 patients were assessed for elegibility. Of 15 873 eligible patients from 93 NHS hospitals, primary outcome data were analysed for 8482 patients in the usual care group and 7374 in the QI group. Eight patients in the usual care group and nine patients in the QI group were not included in the analysis because of missing primary outcome data. The primary outcome of 90-day mortality occurred in 1210 (16%) patients in the QI group compared with 1393 (16%) patients in the usual care group (HR 1·11, 0·96–1·28). Interpretation: No survival benefit was observed from this QI programme to implement a care pathway for patients undergoing emergency abdominal surgery. Future QI programmes should ensure that teams have both the time and resources needed to improve patient care. Funding: National Institute for Health Research Health Services and Delivery Research Programme