Exploiting the potential energy landscape to sample free energy

We review a number of recently developed strategies for enhanced sampling of complex systems based on knowledge of the potential energy landscape. We describe four approaches, replica exchange, Kirkwood sampling, superposition-enhanced nested sampling, and basin sampling, and show how each of them can exploit information for low-lying potential energy minima obtained using basin-hopping global optimization. Characterizing these minima is generally much faster than equilibrium thermodynamic sampling, because large steps in configuration space between local minima can be used without concern for maintaining detailed balance.The authors gratefully acknowledge financial support from the EPSRC and the ERC. S.M acknowledges financial support from the Gates Cambridge Scholarship.This is the accepted manuscript. The final published version is available at http://onlinelibrary.wiley.com/doi/10.1002/wcms.1217/abstract

Monte Carlo sampling for stochastic weight functions

Conventional Monte Carlo simulations are stochastic in the sense that the acceptance of a trial move is decided by comparing a computed acceptance probability with a random number, uniformly distributed between 0 and 1. Here, we consider the case that the weight determining the acceptance probability itself is fluctuating. This situation is common in many numerical studies. We show that it is possible to construct a rigorous Monte Carlo algorithm that visits points in state space with a probability proportional to their average weight. The same approach may have applications for certain classes of high-throughput experiments and the analysis of noisy datasets.D. F. acknowledges support by EPSRC Programme Grant EP/I001352/1 and EPSRC grant EP/I000844/1. K. J. S. acknowledges support by the Swiss National Science Foundation (Grant No. P2EZP2-152188 and No. P300P2-161078). S. M. acknowledges financial support from the Gates Cambridge Scholarship

Superposition enhanced nested sampling

The theoretical analysis of many problems in physics, astronomy and applied mathematics requires an efficient numerical exploration of multimodal parameter spaces that exhibit broken ergodicity. Monte Carlo methods are widely used to deal with these classes of problems, but such simulations suffer from a ubiquitous sampling problem: the probability of sampling a particular state is proportional to its entropic weight. Devising an algorithm capable of sampling efficiently the full phase space is a long-standing problem. Here we report a new hybrid method for the exploration of multimodal parameter spaces exhibiting broken ergodicity. Superposition enhanced nested sampling (SENS) combines the strengths of global optimization with the unbiased/athermal sampling of nested sampling, greatly enhancing its efficiency with no additional parameters. We report extensive tests of this new approach for atomic clusters that are known to have energy landscapes for which conventional sampling schemes suffer from broken ergodicity. We also introduce a novel parallelization algorithm for nested sampling

Numerical test of the Edwards conjecture shows that all packings are equally probable at jamming

In the late 1980s, Sam Edwards proposed a possible statistical-mechanical framework to describe the properties of disordered granular materials1. A key assumption underlying the theory was that all jammed packings are equally likely. In the intervening years it has never been possible to test this bold hypothesis directly. Here we present simulations that provide direct evidence that at the unjamming point, all packings of soft repulsive particles are equally likely, even though generically, jammed packings are not. Typically, jammed granular systems are observed precisely at the unjamming point since grains are not very compressible. Our results therefore support Edwards’ original conjecture. We also present evidence that at unjamming the configurational entropy of the system is maximal.S.M. acknowledges financial support by the Gates Cambridge Scholarship. K.J.S. acknowledges support by the Swiss National Science Foundation under Grant No. P2EZP2-152188 and No. P300P2-161078. D.F. acknowledges support by EPSRC Programme Grant EP/I001352/1 and EPSRC grant EP/I000844/1. K.R. and B.C. acknowledge the support of NSF-DMR 1409093 and the W. M. Keck Foundation

ColabFit Exchange: open-access datasets for data-driven interatomic potentials

Data-driven (DD) interatomic potentials (IPs) trained on large collections of first principles calculations are rapidly becoming essential tools in the fields of computational materials science and chemistry for performing atomic-scale simulations. Despite this, apart from a few notable exceptions, there is a distinct lack of well-organized, public datasets in common formats available for use with IP development. This deficiency precludes the research community from implementing widespread benchmarking, which is essential for gaining insight into model performance and transferability, while also limiting the development of more general, or even universal, IPs. To address this issue, we introduce the ColabFit Exchange, the first database providing open access to a large collection of systematically organized datasets from multiple domains that is especially designed for IP development. The ColabFit Exchange is publicly available at \url{https://colabfit.org/}, providing a web-based interface for exploring, downloading, and contributing datasets. Composed of data collected from the literature or provided by community researchers, the ColabFit Exchange consists of 106 datasets spanning nearly 70,000 unique chemistries, and is intended to continuously grow. In addition to outlining the software framework used for constructing and accessing the ColabFit Exchange, we also provide analyses of data, quantifying the diversity and proposing metrics for assessing the relative quality and atomic environment coverage of different datasets. Finally, we demonstrate an end-to-end IP development pipeline, utilizing datasets from the ColabFit Exchange, fitting tools from the KLIFF software package, and validation tests provided by the OpenKIM framework

Energy landscapes for machine learning

Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding machine learning landscape. Methods to explore and visualise molecular potential energy landscapes can be applied to these machine learning landscapes to gain new insight into the solution space involved in training and the nature of the corresponding predictions. In particular, we can define quantities analogous to molecular structure, thermodynamics, and kinetics, and relate these emergent properties to the structure of the underlying landscape. This Perspective aims to describe these analogies with examples from recent applications, and suggest avenues for new interdisciplinary research.This research was funded by EPSRC grant EP/I001352/1, the Gates Cambridge Trust, and the ERC. DM was in the Department of Applied and Computational Mathematics and Statistics when this work was performed, and his current affiliation is Department of Systems, United Technologies Research Center, East Hartford, CT, USA

Numerical test of the Edwards conjecture shows that all packings are equally probable at jamming

In the late 1980s, Sam Edwards proposed a possible statistical-mechanical framework to describe the properties of disordered granular materials1. A key assumption underlying the theory was that all jammed packings are equally likely. In the intervening years it has never been possible to test this bold hypothesis directly. Here we present simulations that provide direct evidence that at the unjamming point, all packings of soft repulsive particles are equally likely, even though generically, jammed packings are not. Typically, jammed granular systems are observed precisely at the unjamming point since grains are not very compressible. Our results therefore support Edwards’ original conjecture. We also present evidence that at unjamming the configurational entropy of the system is maximal.S.M. acknowledges financial support by the Gates Cambridge Scholarship. K.J.S. acknowledges support by the Swiss National Science Foundation under Grant No. P2EZP2-152188 and No. P300P2-161078. D.F. acknowledges support by EPSRC Programme Grant EP/I001352/1 and EPSRC grant EP/I000844/1. K.R. and B.C. acknowledge the support of NSF-DMR 1409093 and the W. M. Keck Foundation

Lenalidomide reduces microglial activation and behavioral deficits in a transgenic model of Parkinson’s disease

BACKGROUND: Parkinson’s disease (PD) is one of the most common causes of dementia and motor deficits in the elderly. PD is characterized by the abnormal accumulation of the synaptic protein alpha-synuclein (α-syn) and degeneration of dopaminergic neurons in substantia nigra, which leads to neurodegeneration and neuroinflammation. Currently, there are no disease modifying alternatives for PD; however, targeting neuroinflammation might be a viable option for reducing motor deficits and neurodegeneration. Lenalidomide is a thalidomide derivative designed for reduced toxicity and increased immunomodulatory properties. Lenalidomide has shown protective effects in an animal model of amyotrophic lateral sclerosis, and its mechanism of action involves modulation of cytokine production and inhibition of NF-κB signaling. METHODS: In order to assess the effect of lenalidomide in an animal model of PD, mThy1-α-syn transgenic mice were treated with lenalidomide or the parent molecule thalidomide at 100 mg/kg for 4 weeks. RESULTS: Lenalidomide reduced motor behavioral deficits and ameliorated dopaminergic fiber loss in the striatum. This protective action was accompanied by a reduction in microgliosis both in striatum and hippocampus. Central expression of pro-inflammatory cytokines was diminished in lenalidomide-treated transgenic animals, together with reduction in NF-κB activation. CONCLUSION: These results support the therapeutic potential of lenalidomide for reducing maladaptive neuroinflammation in PD and related neuropathologies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12974-015-0320-x) contains supplementary material, which is available to authorized users