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

Association Testing Of Copy Number Variants in Schizophrenia and Autism Spectrum Disorders

Background: Autism spectrum disorders and schizophrenia have been associated with an overlapping set of copynumber variant loci, but the nature and degree of overlap in copy number variants (deletions compared toduplications) between these two disorders remains unclear.Methods: We systematically evaluated three lines of evidence: (1) the statistical bases for associations of autismspectrum disorders and schizophrenia with a set of the primary CNVs thus far investigated, from previous studies;(2) data from case series studies on the occurrence of these CNVs in autism spectrum disorders, especially amongchildren, and (3) data on the extent to which the CNVs were associated with intellectual disability anddevelopmental, speech, or language delays. We also conducted new analyses of existing data on these CNVs inautism by pooling data from seven case control studies.Results: Four of the CNVs considered, dup 1q21.1, dup 15q11-q13, del 16p11.2, and dup 22q11.21, showed clearstatistical evidence as autism risk factors, whereas eight CNVs, del 1q21.1, del 3q29, del 15q11.2, del 15q13.3, dup16p11.2, dup 16p13.1, del 17p12, and del 22q11.21, were strongly statistically supported as risk factors forschizophrenia. Three of the CNVs, dup 1q21.1, dup 16p11.2, and dup 16p13.1, exhibited statistical support as riskfactors for both autism and schizophrenia, although for each of these CNVs statistical significance was nominal fortests involving one of the two disorders. For the CNVs that were statistically associated with schizophrenia but werenot statistically associated with autism, a notable number of children with the CNVs have been diagnosed withautism or ASD; children with these CNVs also demonstrate a high incidence of intellectual disability anddevelopmental, speech, or language delays.Conclusions: These findings suggest that although CNV loci notably overlap between autism and schizophrenia,the degree of strongly statistically supported overlap in specific CNVs at these loci remains limited. These analysesalso suggest that relatively severe premorbidity to CNV-associated schizophrenia in children may sometimes bediagnosed as autism spectrum disorder

Trends and connections across the Antarctic cryosphere

Satellite observations have transformed our understanding of the Antarctic cryosphere. The continent holds the vast majority of Earth’s fresh water, and blankets swathes of the Southern Hemisphere in ice. Reductions in the thickness and extent of floating ice shelves have disturbed inland ice, triggering retreat, acceleration and drawdown of marine-terminating glaciers. The waxing and waning of Antarctic sea ice is one of Earth’s greatest seasonal habitat changes, and although the maximum extent of the sea ice has increased modestly since the 1970s, inter-annual variability is high, and there is evidence of longer-term decline in its extent

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 generalizations 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. (This chapter is adapted with permission from Ceriotti et al. (Handbook of materials modeling. Springer, Cham, 2019).)

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

Incompleteness of Atomic Structure Representations.

Many-body descriptors are widely used to represent atomic environments in the construction of machine-learned interatomic potentials and more broadly for fitting, classification, and embedding tasks on atomic structures. There is a widespread belief in the community that three-body correlations are likely to provide an overcomplete description of the environment of an atom. We produce several counterexamples to this belief, with the consequence that any classifier, regression, or embedding model for atom-centered properties that uses three- (or four)-body features will incorrectly give identical results for different configurations. Writing global properties (such as total energies) as a sum of many atom-centered contributions mitigates the impact of this fundamental deficiency-explaining the success of current "machine-learning" force fields. We anticipate the issues that will arise as the desired accuracy increases, and suggest potential solutions

Low incidence of UPD in spontaneous abortions beyond the 5th gestational week

Approximately 15-20% of all clinically recognised pregnancies abort, most commonly between 8-12 gestational weeks. While the majority of early pregnancy losses is attributed to cytogenetic abnormalities, the aetiology of approximately 40% of early abortions remains unclear. To determine additional factors causing spontaneous abortions we retrospectively searched for uniparental disomies (UPD) in 77 cytogenetically normal diploid spontaneous abortions. In all cases an unbalanced chromosome anomaly was ruled out by cytogenetic investigation of chorionic/amniotic membranes and/or chorionic villi. For UPD screening microsatellite analyses were performed on DNA of abortion specimens and parental blood using highly polymorphic markers showing UPD in two cases. The distribution of markers analysed indicated maternal heterodisomy for chromosome 9 (UPhD(9)mat) in case 1 and paternal isodisomy for chromosome 21 (UPiD(21)pat) in case 2. The originating mechanism suggested was monosomy complementation in UPiD(21)pat and trisomy rescue in UPhD(9)mat. In the case of UPhD(9)mat purulent chorioamnionitis was noted and a distinctly growth retarded embryo of 3 cm crown-rump length showing no gross external malformations. Histological analysis in the case of UPiD(21)pat suggested a primary anlage defect. Our results indicate that less than 3% of genetically unexplained pregnancy wastage is associated with total chromosome UPD. UPD may contribute to anlage defects of human conception. Chromosome aneuploidy correction can occur in very early cleavage stages. More research, however, ought to be performed into placental mosaicism to further clarify timing and mechanisms involved in foetal UPD