. (2015) A framework for machine-learning-augmented multiscale atomistic simulations on parallel supercomputers. International Journal of Quantum Chemistry. Permanent WRAP url: http://wrap.warwick.ac.uk/68012 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work of researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-forprofit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher statement:"This is the peer reviewed version of the following article: Caccin, Marco, Li, Zhenwei, Kermode, James R and De Vita, Alessandro. (2015) A framework for machine-learningaugmented multiscale atomistic simulations on parallel supercomputers. International Journal of Quantum Chemistry, which has been published in final form at http://dx.doi.org/10.1002/qua.24952 . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. AbstractRecent advances in quantum mechanical(QM)-based molecular dynamics simulations have used machine-learning (ML) to predict, rather than re-calculate, QMaccurate forces in atomic configurations sufficiently similar to previously encountered ones. Here, we discuss how ML approaches can be deployed within large-scale QM/MM materials simulations on massively parallel supercomputers, making QM zones of 1000 atoms routinely attainable. We argue that the ML approach allows computational effort to be concentrated on the most chemically active subregions of the QM zone, significantly improving the overall efficiency of the simulation. We thus propose a novel method to partition large QM regions into multiple subregions which can be computed in parallel to achieve optimal scaling. Then we review a recently proposed QM/ML MD scheme [Z. Li et al., Phys. Rev. Lett. 114(9), 096405 (2015)], discussing how this could be efficiently combined with QM-zone partitioning.

The continuous effort towards topological quantum devices calls for an efficient and non-invasive method to assess the conformity of components in different topological phases. Here, we show that machine learning paves the way towards noninvasive topological quality control. To do so, we use a local topological marker, able to discriminate between topological phases of one-dimensional wires. The direct observation of this marker in solid state systems is challenging, but we show that an artificial neural network can learn to approximate it from the experimentally accessible local density of states. Our method distinguishes different non-trivial phases, even for systems where direct transport measurements are not available and for composite systems. This new approach could find significant use in experiments, ranging from the study of novel topological materials to high-throughput automated material design.

This paper aims at showing the capabilities of thermoeconomic analysis for solving cost assessments in district heating systems both at user and producer sides. In the near future it is expected that multiple producers are allowed to supply heat to the same district heating network, similarly to what happens in the case of the electric grid. Not only the amount of heat they may produce should be properly accounted, but also its quality, and also the pumping power that is requested to supply a unity of thermal energy to the endusers. Moreover, buildings equipped with low temperature heating system allow better use of the thermal energy vector, thus allowing larger efficiency of thermal plants.In the present work, the use of thermoeconomics for the analysis of these aspects is proposed. The approach allows one performing cost assessment in district heating, taking into account the effects of investment and operating costs and thermodynamic irreversibilities in the cost formation of heat from its production in the plants to its use in the buildings. Simple examples are analyzed in order to provide a quantitative evaluation of the various cost terms, depending on the operating conditions, topology and characteristics of the users/producers.