3,008 research outputs found
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
Atomic-scale representation and statistical learning of tensorial properties
This chapter discusses the importance of incorporating three-dimensional
symmetries in the context of statistical learning models geared towards the
interpolation of the tensorial properties of atomic-scale structures. We focus
on Gaussian process regression, and in particular on the construction of
structural representations, and the associated kernel functions, that are
endowed with the geometric covariance properties compatible with those of the
learning targets. We summarize the general formulation of such a
symmetry-adapted Gaussian process regression model, and how it can be
implemented based on a scheme that generalizes the popular smooth overlap of
atomic positions representation. We give examples of the performance of this
framework when learning the polarizability and the ground-state electron
density of a molecule
Hypersexuality, gender, and sexual orientation: a large-scale psychometric survey study
Criteria for Hypersexual Disorder (HD) were proposed for consideration in the DSM-5 but ultimately excluded for a variety of reasons. Regardless, research continues to investigate hypersexual behavior (HB). The Hypersexual Behavior Inventory (HBI) is one of the most robust scales assessing HB, but further examination is needed to explore its psychometric properties among different groups. Therefore, the aim of the present study was to examine the generalizability of the HBI in a large, diverse, nonclinical sample (N = 18,034 participants; females = 6132; 34.0%; Mage = 33.6 years, SDage = 11.1) across both gender and sexual orientation. Measurement invariance testing was carried out to ensure gender- and sexual-orintation based comparisons were meaningful. Results demonstrated when both gender and sexual-orientation were considered (i.e., heterosexual males vs. LGBTQ males vs. heterosexual females vs. LGBTQ females), LGBTQ males had significantly higher latent means on the HBI factors. Results also demonstrated LGBTQ males had the highest scores on other possible indicators of hypersexuality (e.g., frequency of masturbation, number of sexual partners, or frequency of pornography viewing). These findings suggest LGBTQ males may be a group most at risk of engaging in hypersexual behavior and LGBTQ females are at a higher risk of engaging in hypersexual activities due to coping problems. Given the largescale nature of the study, the findings significantly contribute to the currently growing body of literature on hypersexuality
Building nonparametric -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
governing the interaction order modelled. The order 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 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
Nested sampling for materials: the case of hard spheres
The recently introduced nested sampling algorithm allows the direct and
efficient calculation of the partition function of atomistic systems. We
demonstrate its applicability to condensed phase systems with periodic boundary
conditions by studying the three dimensional hard sphere model. Having obtained
the partition function, we show how easy it is to calculate the compressibility
and the free energy as functions of the packing fraction and local order,
verifying that the transition to crystallinity has a very small barrier, and
that the entropic contribution of jammed states to the free energy is
negligible for packing fractions above the phase transition. We quantify the
previously proposed schematic phase diagram and estimate the extent of the
region of jammed states. We find that within our samples, the maximally random
jammed configuration is surprisingly disordered
Insight into liquid polymorphism from the complex phase behavior of a simple model
We systematically explored the phase behavior of the hard-core two-scale ramp model suggested by Jagla [Phys. Rev. E 63, 061501 (2001)] using a combination of the nested sampling and free energy methods. The sampling revealed that the phase diagram of the Jagla potential is significantly richer than previously anticipated, and we identified a family of new crystalline structures, which is stable over vast regions in the phase diagram. We showed that the new melting line is located at considerably higher temperature than the boundary between the low- and high-density liquid phases, which was previously suggested to lie in a thermodynamically stable region. The newly identified crystalline phases show unexpectedly complex structural features, some of which are shared with the high-pressure ice VI phase
A collinear-spin machine learned interatomic potential for Fe\textsubscript{7}Cr\textsubscript{2}Ni alloy
We have developed a new machine learned interatomic potential for the
prototypical austenitic steel FeCrNi, using the Gaussian
approximation potential (GAP) framework. This new GAP can model the alloy's
properties with higher accuracy than classical interatomic potentials like
embedded atom models (EAM), while also allowing us to collect much more
statistics than expensive first-principles methods like density functional
theory (DFT). We also extended the GAP input descriptors to approximate the
effects of collinear spins (Spin GAP), and demonstrate how this extended model
successfully predicts low temperature structural distortions due to the
antiferromagnetic spin state. We demonstrate the application of the Spin GAP
model for bulk properties and vacancies and validate against DFT. These results
are a step towards modelling ageing in austenitic steels with close to DFT
accuracy but at a fraction of its cost
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