Molecular characterization of old local grapevine varieties from South East European countries

South East European (SEE) viticulture partially relies on native grapevine varieties, previously scarcely described. In order to characterize old local grapevine varieties and assess the level of synonymy and genetic diversity from SEE countries, we described and genotyped 122 accessions from Albania, Federation of Bosnia and Herzegovina (B&H), Croatia, Macedonia, Moldova, Montenegro, Republika Srpska (Bosnia and Herzegovina) and Romania on nine most commonly used microsatellite loci. As a result of the study a total of 86 different genotypes were identified. All loci were very polymorphic and a total of 96 alleles were detected, ranging from 8 to 14 alleles per locus, with an average allele number of 10.67. Overall observed heterozygosity was 0.759 and slightly lower than expected (0.789) while gene diversity per locus varied between 0.600 (VVMD27) and 0.906 (VVMD28). Eleven cases of synonymy and three of homonymy have been recorded for samples harvested from different countries. Cultivars with identical genotypes were mostly detected between neighboring countries. No clear differentiation between countries was detected although several specific alleles were detected. The integration of the obtained genetic data with ampelographic ones is very important for accurate identification of the SEE cultivars and provides a significant tool in cultivar preservation and utilization.

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

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

Interatomic potentials: predicting phase transformations in zirconium Machine learning leads to a new interatomic potential for zirconium that can predict phase transformations. A team led by Hongxian Zong at Xi’an Jiaotong University, China, and Turab Lookman at Los Alamos National Laboratory, U.S.A, used a Gaussian-type machine learning approach to produce an interatomic potential that predicted phase transformations in zirconium. They expressed each atomic energy contribution via changes in the local atomic environment, such as bond length, shape, and volume. The resulting machine-learning potential successfully described pure zirconium’s physical properties. When used in molecular dynamics simulations, it predicted a zirconium phase diagram as a function of both temperature and pressure that agreed well with previous experiments and simulations. Developing learnt interatomic potentials in phase-transforming systems could help us better simulate complex systems

Understanding high pressure hydrogen with a hierarchical machine-learned potential

The hydrogen phase diagram has a number of unusual features which are generally well reproduced by density functional calculations. Unfortunately, these calculations fail to provide good physical insights into why those features occur. In this paper, we parameterize a model potential for molecular hydrogen which permits long and large simulations. The model shows excellent reproduction of the phase diagram, including the broken-symmetry Phase II, an efficiently-packed phase III and the maximum in the melt curve. It also gives an excellent reproduction of the vibrational frequencies, including the maximum in the vibrational frequency $\nu(P)$ and negative thermal expansion. By detailed study of lengthy molecular dynamics, we give intuitive explanations for observed and calculated properties. All solid structures approximate to hexagonal close packed, with symmetry broken by molecular orientation. At high pressure, Phase I shows significant short-ranged correlations between molecular orientations. The turnover in Raman frequency is due to increased coupling between neighboring molecules, rather than weakening of the bond. The liquid is denser than the close-packed solid because, at molecular separations below 2.3\AA, the favoured relative orientation switches from quadrupole-energy-minimising to steric-repulsion-minimising. The latter allows molecules to get closer together, without atoms getting closer but this cannot be achieved within the constraints of a close-packed layer

Machine Learning Force Fields: Construction, Validation, and Outlook

Force fields developed with machine learning methods in tandem with quantum mechanics are beginning to find merit, given their (i) low cost, (ii) accuracy, and (iii) versatility. Recently, we proposed one such approach, wherein, the vectorial force on an atom is computed directly from its environment. Here, we discuss the multistep workflow required for their construction, which begins with generating diverse reference atomic environments and force data, choosing a numerical representation for the atomic environments, down selecting a representative training set, and lastly the learning method itself, for the case of Al. The constructed force field is then validated by simulating complex materials phenomena such as surface melting and stress–strain behavior, that truly go beyond the realm of ab initio methods, both in length and time scales. To make such force fields truly versatile an attempt to estimate the uncertainty in force predictions is put forth, allowing one to identify areas of poor performance and paving the way for their continual improvement

SSR Fingerprinting Panel Verifies Identities of Clones in Backup Hazelnut Collection of USDA Genebank

7th International Congress on Hazelnut -- OCT 31, 2009 -- Viterbo, ITALYHummer, Kim/0000-0003-4110-7501; /0000-0002-6371-3433WOS: 000305695500009The US Department of Agriculture (USDA), Agricultural Research Service maintains a genebank representing world hazelnut (Corylus L.) diversity. More than 670 clones are preserved as self-rooted trees in a two-hectare field planting in Corvallis, Oregon, with a single tree per accession. In 1996 and 1997, prior to the spread of eastern filbert blight caused by Anisogramma anomala to within 75 kilometers of Corvallis, a backup collection was established in Parlier, California. A core collection of 184 genotypes representing the wide taxonomic, geographic and phenotypic diversity of Corylus was targeted for this second planting. Two trees of each 'core' genotype were grafted onto seedling rootstocks over a period of five years and an orchard was established in Parlier. The grafted trees in Parlier are at risk of identity problems due to suckers arising from below the graft union. In May 2007, young leaves were collected from 29 Parlier trees that exhibited uncharacteristic morphological phenotypes. A set of 12 simple sequence repeat (SSR) markers was used to fingerprint trees in the backup collection, and the results were compared to the fingerprints of the same accessions in the Corvallis collection. Based on the results, misidentified accessions will be eliminated and the fingerprinting set will be refined further.USDA-ARS CRISUnited States Department of Agriculture (USDA) [5358-21000-038D]The authors are thankful to Barbara Gilmore and April Nyberg for technical assistance. We would also like to thank PhD. candidate Kahraman Gurcan and Dr. Shawn Mehlenbacher for providing primer sequences, map locations, and ratings of ease of scoring for the seven new SSRs used in this study. This research was funded by the USDA-ARS CRIS 5358-21000-038D