Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces

We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) approach [Chmiela et al., Nat. Commun. 9, 3887 (2018); Sci. Adv. 3, e1603015 (2017)]. This approach is able to accurately reconstruct complex high-dimensional potential-energy surfaces from just a few 100s of molecular conformations extracted from ab initio molecular dynamics trajectories. The data efficiency of the sGDML approach implies that atomic forces for these conformations can be computed with high-level wavefunction-based approaches, such as the "gold standard" CCSD(T) method. We demonstrate that the flexible nature of the sGDML model recovers local and non-local electronic interactions (e.g. H-bonding, proton transfer, lone pairs, changes in hybridization states, steric repulsion and $n\to\pi^*$ interactions) without imposing any restriction on the nature of interatomic potentials. The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy

Deep-Inelastic Final States in a Space-Time Description of Shower Development and Hadronization

We extend a quantum kinetic approach to the description of hadronic showers in space, time and momentum space to deep-inelastic $ep$ collisions, with particular reference to experiments at HERA. We follow the history of hard scattering events back to the initial hadronic state and forward to the formation of colour-singlet pre-hadronic clusters and their decays into hadrons. The time evolution of the space-like initial-state shower and the time-like secondary partons are treated similarly, and cluster formation is treated using a spatial criterion motivated by confinement and a non-perturbative model for hadronization. We calculate the time evolution of particle distributions in rapidity, transverse and longitudinal space. We also compare the transverse hadronic energy flow and the distribution of observed hadronic masses with experimental data from HERA, and find encouraging results. The techniques developed in this paper may be applied in the future to more complicated processes such as eA, pp, pA and AA collisions.Comment: 44 pages plus 14 postscript figure

Identifying Heavy-Flavor Jets Using Vectors of Locally Aggregated Descriptors

Jets of collimated particles serve a multitude of purposes in high energy collisions. Recently, studies of jet interaction with the quark-gluon plasma (QGP) created in high energy heavy ion collisions are of growing interest, particularly towards understanding partonic energy loss in the QGP medium and its related modifications of the jet shower and fragmentation. Since the QGP is a colored medium, the extent of jet quenching and consequently, the transport properties of the medium are expected to be sensitive to fundamental properties of the jets such as the flavor of the parton that initiates the jet. Identifying the jet flavor enables an extraction of the mass dependence in jet-QGP interactions. We present a novel approach to tagging heavy-flavor jets at collider experiments utilizing the information contained within jet constituents via the \texttt{JetVLAD} model architecture. We show the performance of this model in proton-proton collisions at center of mass energy $\sqrt{s} = 200$ GeV as characterized by common metrics and showcase its ability to extract high purity heavy-flavor jet sample at various jet momenta and realistic production cross-sections including a brief discussion on the impact of out-of-time pile-up. Such studies open new opportunities for future high purity heavy-flavor measurements at jet energies accessible at current and future collider experiments.Comment: 18 pages, 6 figures and 3 tables. Accepted by JINS

Hadrons in the Nuclear Medium

Quantum Chromodynamics, the microscopic theory of strong interactions, has not yet been applied to the calculation of nuclear wave functions. However, it certainly provokes a number of specific questions and suggests the existence of novel phenomena in nuclear physics which are not part of the the traditional framework of the meson-nucleon description of nuclei. Many of these phenomena are related to high nuclear densities and the role of color in nucleonic interactions. Quantum fluctuations in the spatial separation between nucleons may lead to local high density configurations of cold nuclear matter in nuclei, up to four times larger than typical nuclear densities. We argue here that experiments utilizing the higher energies available upon completion of the Jefferson Laboratory energy upgrade will be able to probe the quark-gluon structure of such high density configurations and therefore elucidate the fundamental nature of nuclear matter. We review three key experimental programs: quasi-elastic electro-disintegration of light nuclei, deep inelastic scattering from nuclei at $x>1$, and the measurement of tagged structure functions. These interrelated programs are all aimed at the exploration of the quark structure of high density nuclear configurations. The study of the QCD dynamics of elementary hard processes is another important research direction and nuclei provide a unique avenue to explore these dynamics. We argue that the use of nuclear targets and large values of momentum transfer at would allow us to determine whether the physics of the nucleon form factors is dominated by spatially small configurations of three quarks.Comment: 52 pages IOP style LaTex file and 20 eps figure

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie