Quantum-chemical insights from deep tensor neural networks

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems

The Influence of Carbon Coatings on the Functional Properties of X39Cr13 and 316LVM Steels Intended for Biomedical Applications

[EN] Carbon coatings are used in many different industrial areas, for example in cutting, electronics, or medical applications. On the one hand, carbon coatings have improved the functional properties of medical products because of their high biotolerance, which makes them an important material for implant coatings. On the other hand, high rigidity and abrasion resistance are properties needed in case of surgical tools. Thus, the aim of this research was to study the influence of mechanical abrasion by tumbling and chemical passivation on carbon coatings deposited by reactive magnetron sputtering (RMS) and radio frequency plasma activated chemical vapor deposition (RF PACVD) of X39Cr13 (mainly used for surgical tools) and 316LVM (mainly used for implants). Functional properties, such as roughness, coatings adhesion (scratch test), and wettability were investigated. As a result, DLC coatings applied by magnetron sputtering were found to be the optimum surface treatment in terms of adhesion and wettability properties, being more appropriate for the use of X39Cr13 base than 316LVM for carbon layer deposition.S

Spin and orbital magnetic moments of size-selected iron, cobalt, and nickel clusters and their link to the bulk phase diagrams

Spin and orbital magnetic moments of cationic iron, cobalt, and nickel clusters have been determined from x-ray magnetic circular dichroism spectroscopy. In the size regime of $n = 10 - 15$ atoms, these clusters show strong ferromagnetism with maximized spin magnetic moments of 1~$\mu_B$ per empty $3d$ state because of completely filled $3d$ majority spin bands. The only exception is $\mathrm{Fe}_{13}^+$ where an unusually low average spin magnetic moment of $0.73 \pm 0.12$~$\mu_B$ per unoccupied $3d$ state is detected; an effect, which is neither observed for $\mathrm{Co}_{13}^+$ nor $\mathrm{Ni}_{13}^+$.\@ This distinct behavior can be linked to the existence and accessibility of antiferromagnetic, paramagnetic, or nonmagnetic phases in the respective bulk phase diagrams of iron, cobalt, and nickel. Compared to the experimental data, available density functional theory calculations generally seem to underestimate the spin magnetic moments significantly. In all clusters investigated, the orbital magnetic moment is quenched to $5 - 25$\,\% of the atomic value by the reduced symmetry of the crystal field. The magnetic anisotropy energy is well below 65 $\mu$eV per atom

Variability of CO2, CH4, and O2 concentration in the vicinity of a closed mining shaft in the light of extreme weather events—numerical simulations

This is the final version. Available from MDPI via the DOI in this record. Data Availability Statement: Data are contained within the article.With climate change, more intense weather phenomena can be expected, including pressure drops related to the arrival of an atmospheric front. Such drops of pressure are the main reason for gas emissions from closed mines to the surface, and a closed, empty mine shaft is the most likely route of this emission. Among the gases emitted, the most important are carbon dioxide and methane, creating a twofold problem—greenhouse gas emissions and gas hazards. The work presented in this paper simulated the spread of the mentioned gases near such an abandoned shaft for four variants: model validation, the most dangerous situations found during measurements with or without wind, and a forecast variant for a possible future pressure drop. It was found that a momentary CO2 emission of 0.69 m3/s and a momentary CH4 emission of 0.29 m3/s are possible, which for one hour of the appropriate drop would give hypothetically 2484 m3 CO2 and 1044 m3 CH4. In terms of gas hazards, the area that should be monitored and protected may exceed 25 m from a closed shaft in the absence of wind influence. The wind spreads the emitted gases to distances exceeding 50 m but dilutes them significantly.Research Fund for Coal and Steel

Building nonparametric nn-body force fields using Gaussian process regression

Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task. This chapter presents a framework under which this problem can be tackled, based on the Bayesian construction of nonparametric force fields of a given order using Gaussian process (GP) priors. The formalism of GP regression is first reviewed, particularly in relation to its application in learning local atomic energies and forces. For accurate regression it is fundamental to incorporate prior knowledge into the GP kernel function. To this end, this chapter details how properties of smoothness, invariance and interaction order of a force field can be encoded into corresponding kernel properties. A range of kernels is then proposed, possessing all the required properties and an adjustable parameter $n$ governing the interaction order modelled. The order $n$ best suited to describe a given system can be found automatically within the Bayesian framework by maximisation of the marginal likelihood. The procedure is first tested on a toy model of known interaction and later applied to two real materials described at the DFT level of accuracy. The models automatically selected for the two materials were found to be in agreement with physical intuition. More in general, it was found that lower order (simpler) models should be chosen when the data are not sufficient to resolve more complex interactions. Low $n$ GPs can be further sped up by orders of magnitude by constructing the corresponding tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments

A Hepatic Protein, Fetuin-A, Occupies a Protective Role in Lethal Systemic Inflammation

A liver-derived protein, fetuin-A, was first purified from calf fetal serum in 1944, but its potential role in lethal systemic inflammation was previously unknown. This study aims to delineate the molecular mechanisms underlying the regulation of hepatic fetuin-A expression during lethal systemic inflammation (LSI), and investigated whether alterations of fetuin-A levels affect animal survival, and influence systemic accumulation of a late mediator, HMGB1.LSI was induced by endotoxemia or cecal ligation and puncture (CLP) in fetuin-A knock-out or wild-type mice, and animal survival rates were compared. Murine peritoneal macrophages were challenged with exogenous (endotoxin) or endogenous (IFN-γ) stimuli in the absence or presence of fetuin-A, and HMGB1 expression and release was assessed. Circulating fetuin-A levels were decreased in a time-dependent manner, starting between 26 h, reaching a nadir around 24-48 h, and returning towards base-line approximately 72 h post onset of endotoxemia or sepsis. These dynamic changes were mirrored by an early cytokine IFN-γ-mediated inhibition (up to 50-70%) of hepatic fetuin-A expression. Disruption of fetuin-A expression rendered animals more susceptible to LSI, whereas supplementation of fetuin-A (20-100 mg/kg) dose-dependently increased animal survival rates. The protection was associated with a significant reduction in systemic HMGB1 accumulation in vivo, and parallel inhibition of IFN-γ- or LPS-induced HMGB1 release in vitro.These experimental data suggest that fetuin-A is protective against lethal systemic inflammation partly by inhibiting active HMGB1 release

XLIV Konferencja Komitetu Nauk o Żywności i Żywieniu PAN: nauka, technologia i innowacje w żywności i żywieniu

Streszczenia w jęz. angielskimWydarzenie: XLIV Konferencja Komitetu Nauk o Żywności i Żywieniu PAN; Łódź, 3-4 lipca 2019 r.; http://pan.binoz.p.lodz.plOrganizator konferencji: Wydział Biotechnologii i Nauk o Żywności PŁ; Komitet Nauk o Żywności i Żywieniu PAN; Polskie Towarzystwo Technologów ŻywnościProjekt graficzny okładki: Grzelczyk, J.Projekt graficzny okładki: Klewicki, R.Skład: Oracz, J.Za treść zamieszczonych materiałów odpowiadają ich autorzy.Sesje Naukowe Komitetu Nauk o Żywności i Żywieniu Polskiej Akademii Nauk (KNoŻiŻ PAN) są organizowane przez krajowe ośrodki akademickie związane z naukami o żywności i żywieniu w dwuletnich cyklach. Sesje te stanowią największe w skali kraju forum prezentacji najnowszych osiągnięć naukowych i technologicznych w dziedzinie technologii żywności i żywienia człowieka, jak również wymiany poglądów oraz doświadczeń pracowników jednostek naukowych i przedstawicieli przemysłu spożywczego. Tematyka XLIV Sesji dotyczyć będzie szeroko pojętej problematyki związanej z oddziaływaniem żywności i odżywiania na zdrowie człowieka