DLAP2019 Poster title and abstract
Nagai, Yuki (Japan Atomic Energy Agency)
Self-learning Hybrid Monte Carlo for first-principles molecular simulations
We propose a novel approach called Self-Learning Hybrid Monte Carlo(SLHMC)[1] which is a general method to make use of machine learningpotentials

toacceleratethestatisticalsamplingoffirst-principles

density-functional-theory (DFT) simulations. The trajectories are generated on
an approximate machine learning (ML) potential energy surface. The trajectories
are then accepted or rejected by the Metropolis algorithm based on DFT
energies. In this way the statistical ensemble is sampled exactly at the DFT level
for a given thermodynamic condition. Meanwhile the ML potential is improved on
the fly by training to enhance the sampling, whereby the training data set, whichis sampled from the exact ensemble, is created automatically.
[1]Yuki Nagai, Masahiko Okumura, Keita Kobayashi, and Motoyuki Shiga,
arXiv:1909.02255

Shirai, Tatsuhiko (Green Computing Systems Research Organization, Waseda
University)
Generalized quantum annealing inspired by thermal effectWith the progress of quantum technology, it is becoming essential for exploring
the potential of Ising machines in the field of machine learning. Ising machinesprovide heuristic solvers for a combinatorial optimization problem, which is
ubiquitous in the whole procedure of machine learning. Toward the realization of
more efficient solvers, in this poster, we propose a generalized quantum
annealing method inspired by a thermal effect. We present an influence of thethermal effect on the performance and evaluate the fair sampling of thedegenerate ground states.

Tamura, Kensuke (Department of Physics, Graduate School of Science, The
University of Tokyo)
Performance comparison of integer encoding methods in Ising machines
Combinatorial optimization problems have wide applications in various fields
including machine learning. These problems can be mapped to finding the
ground states of the corresponding classical Ising model, which can be solved
efficiently by Ising machines. Recently, potential applications of Ising machines
to machine learning have been explored. Toward the realization ofhigh-performance machine learning by using Ising machines, we need torephrase optimization procedures in machine learning as combinatorial
optimization problems, which have to be described by binary variables, i.e., {0,1}.
To this end, it is convenient to use integer encodings which converts any integer
into a binary string. There are four typical integer encodings: one-hot, log, unary
and order encoding and it is important to clarify which of these methods
performs better. In this poster presentation, we compare these four methods for
0-1 quadratic knapsack problems using an Ising machine.

Seryo, Naoki (Department of Chemical Engineering, Kyoto University)
Learning constitutive relations from micro-scale dynamics with memory effects
in non-Newtonian fluids
Polymeric materials, which are widely used in industry, are usually created frompolymer melts. These melts are an example of a complex fluid, where thebehavior is determined by the microscopic dynamics of the entangled polymer
chains. For this reason, the mechanical properties and the flow state of thepolymer melt are extremely complicated. Therefore, to reliably control the natureof the flow and the degree of entanglement, detailed simulations are required.
However, performing microscopic simulations is out of the questions, given thelarge separation of length- and time-scales between the microscopic andmacroscopic degrees of freedom. Therefore, to deal with the relationshipbetween micro-scale and macro-scale in detail, Multi-Scale Simulations (MSS)of polymeric fluids have become the standard approach. Unfortunately, this stillrequires considerable computational resources. This study aims to develop adata-driven multi-scale simulation method to derive the constitutive relation of
non-Newtonian fluids exhibiting memory effects. For this, we introduce a
Gaussian Process Regression model to derive the constitutive relation from themicroscopic simulation. This allows us to drastically reduce the calculation cost,compared to full MSS. To test our method, we consider the case of simple shear
flow with a Hookean-dumbbell model for the polymer chains. Our results inexcellent agreement with the MSS and offer the possibility of deriving theconstitutive relations for more complicated microscopic polymer models.

Mototake, Yoh-ichi (The Institute of Statistical Mathematics)
Conservation Law Estimation by Extracting the Symmetry of a Dynamical
System Using a DNN
From the viewpoint that Deep Neural Networks (DNN) have the ability to modelthe distribution of dataset as a low-dimensional manifold, we propose a method
to extract the coordinate transformation making a dataset distribution invariant
by sampling DNN using Replica Exchange Monte-Carlo method. In addition, weshow that the canonical transformation that makes the Hamiltonian invariant,which is necessary condition for Noether’s theorem, and symmetry of the
manifold structure of the time series data of the dynamical system are related.
By integrating this knowledge with the method described above, we propose amethod to estimate conservation law from time-series data.
We verified the efficiency of the proposed methods in some primitive cases.

Berns, Lukas (Tokyo Institute of Technology)
A new CNN architecture for sub-manifolds embedded into higher dimensionalspace
We study methods to overcome practical challenges in applying CNNs to water
Cherenkov detectors like Super-Kamiokande. Here a huge volume of water issurrounded by light detectors, which record the hit position and timing of photons
emitted from relativistic particles inside the tank. The task is particle ID andreconstruction of track parameters like energy or interaction vertex. We propose
a way to apply a CNN to the 2D sub-manifold formed by the light detectors, that
correctly takes into account the S_2 topology, as well as the reparametrization
invariance of the sub-manifold coordinates. In our formulation the integration
kernels have a geometrical interpretation, which can be utilized to enlarge theintrinsic translation symmetry in 3D space by rotational symmetry. Thesefeatures make the network robust against overtraining, suggesting the possibility
for data-driven training at the small sample sizes of neutrino events.

Matsumoto, Ryo (Tokyo University of Science)
Classification of Super-Kamiokande atmospheric neutrino events by usingneural network
Neutrino mass hierarchy (MH) is not yet determined while it was revealed fromobservation of neutrino oscillation that the extremely small neutrino mass exists.
Current precision of MH in Super-Kamionade is limited by the statistics in a few
GeV region where resonance oscillation occurs due to Earth&#039;s matter
effects. In this study, we investigated a classification of multi-GeV multi-ring
events by neural network. We will present the evaluation of the method using theatmospheric neutrino MC simulations.

Yoshioka, Nobuyuki (Department of Physics, University of Tokyo)
Constructing neural stationary states for open quantum many-body systems
We propose a new variational scheme based on the neural-network quantum
states to simulate the stationary states of open quantum many-body systems.
Using the high expressive power of the variational ansatz described by the
restricted Boltzmann machines, which we dub as the neural stationary stateansatz, we compute the stationary states of quantum dynamics obeying the
Lindblad master equations. The mapping of the stationary-state search problem
into finding a zero-energy ground state of an appropriate Hermitian operator
allows us to apply the conventional variational Monte Carlo method for the
optimization. Our method is shown to simulate various spin systems efficiently,
i.e., the transverse-field Ising models in both one and two dimensions and the
XYZ model in one dimension.

Sumimoto, Takayuki (Osaka University)
Meson Spectrum in Holographic QCD and Deep Learning
In my talk, I will report that 5d theory on curved spacetime describing the meson
spectrum was found by using deep learning. Based on holographic principle, we
employed deep learning as a method obtaining bulk geometry dual to a certain
QFT. Our input data was the spectrum of meson, QCD observables. Thenexperimental data of spectrum gave the metric of bulk spacetime as learning
weight of neural network. I will explain about how the bulk spacetime emergedon neural network and discuss its consistency as bulk spacetime dual to QCD.

Lim, Sung Hak (Theory Center, HIGH ENERGY ACCELERATOR RESEARCHORGANIZATION, KEK)
Interpretable deep learning for two-prong jet classification with jet spectra
Classification of jets with deep learning has gained significant attention in recent
times. However, the performance of deep neural networks is often achieved at
the cost of interpretability. Here we propose an interpretable network trained on
the jet spectrum S2(R) which is a two-point correlation function of the jet
constituents. The spectrum can be derived from a functional Taylor series of anarbitrary jet classifier function of energy flows. An interpretable network can beobtained by truncating the series. The intermediate feature of the network is an
infrared and collinear safe C-correlator which allows us to estimate theimportance of an S2(R) deposit at an angular scale R in the classification. The
performance of the architecture is comparable to that of a convolutional neuralnetwork (CNN) trained on jet images, although the number of inputs andcomplexity of the architecture is significantly simpler than the CNN classifier. We
consider two examples: one is the classification of two-prong jets which differ incolor charge of the mother particle, and the other is a comparison between
Pythia 8 and Herwig 7 generated jets.

Shiina, Kenta (Department of Physics, Tokyo Metropolitan University)
Solving Schrodinger equation by Machine Learning with support of Perturbation
theoryWe tried to make Neural network (NN) learn the mapping between a potential
energy and its wave function for time-independent system. Unlike previous
studies, we don’t prepare any exact solutions (ground truth) provided by othernumerical methods. Instead of that, we use first perturbation of wave function as
supportive information in order to help NN learn the mapping. We found thattrained NN can produce an approximated wave function for new input ofpotential energy which is not included in training data. The advantages of our
method are that it doesn’t require ground truth as training data set, and it can beapplied to any quantum state if the first perturbation of target state can becalculated.

Kuriki, Ryosuke (Tokyo University of Science)
Deep learning method to solve an inverse problem clarifying the properties ofthermoelectric materials from observables
Thermoelectric effects are typical phenomena in material physics. They are
attracting much attention from the viewpoint of energy harvesting. Recently,
Yamamoto and Fukuyama clarified their theoretical structures using Kubo
formula. In their frameworks, electrical conductivity and thermoelectricalconductivity are given by fermi-distribution and spectrum conductivity. This
spectrum conductivity depends on the physics of target materials, that is, weneed to know its behavior to find the origin of thermoelectric effects of thematerials, However, spectrum conductivity is not observable while we canmeasure the electrical conductivity are thermoelectric conductivity. The relation
between electrical conductivity (or thermoelectric conductivity) and spectrum
conductivity is shown in Fredholm integral equation(FIE). The problem to solve
FIE is a kind of inverse problem. Thus, in the present study, we tried to solve thisFIE using Deep learning. In our presentation, we show that the spectrum
conductivity can be estimated by electrical conductivity and thermoelectric
conductivity in some typical cases. Furthermore, we discuss how to apply thepresent method to real measurements.

Ota, Toshihiro (Osaka University)
Robust and Transferable Adversarial Examples from Deep Image Prior
The accuracy of image recognition using machine learning is already beyond
human-level. However, it has been known that a trained neural net is easily
fooled by a tiny perturbation, which is called an adversarial example. To study
the vulnerability of neural nets, we focus on VGG16, a trained convolutional
neural net, and explore how we can deceive the network by making a
perturbation through another neural net, deep image prior. In our setup, unlike
the normal method, we train deep image prior to cheat VGG16 more efficiently,
and it turns out that our noise images show transferability. From our result, weconclude that adversarial example may be extracting a universal concept rather
than attacking.

Mano, Tomohiro (Department of Physics, Sophia University)
Application of convolutional neural network to quantum percolations in
topological systems

Quantum material phases such Anderson insulator, diffusive metal, Weyl/Diracsemimetal as well as topological insulators show specific wave functions both inreal and Fourier spaces. These features are well captured by convolutional
neural networks, and the phase diagrams have been obtained, where standard
methods are not applicable. One of the examples are the cases of random
lattices such as quantum percolation. Here we study the topological insulatorswith random vacancies, namely the quantum percolation in topological insulators,
by analyzing the wave functions via convolutional neural network. The vacancies
in topological insulators are especially interesting since peculiar bound states
are formed around the vacancies.

We show that only a few percent ofvacancies derives topological phase transition. The results are confirmed bydensity of states and wave packet dynamics.

Robles, Sandra (The University of Melbourne)
A deep learning approach to halo merger tree construction
Semi-analytic models for galaxy formation are best suited to compare theories
with observations. These models rely heavily on halo merger trees which
encapsulate the growth and merger history of dark matter (DM) halos. Despite
being computationally intensive, the most popular method for merger tree
construction is based on high-resolution DM only N-body simulations. With theaim to provide a new framework for halo merger tree construction with a modest
computational cost, we propose a Generative Adversarial Network (GAN) thatlearns to generate realistic halo merger trees taking advantage of the results oflarge volume simulations. We train our GAN with merger trees from the EAGLE
simulation suite, and show the quality of our results.

Hayase, Tomohiro (Fujitsu Laboratories LTD)
Free Probability Theory for Jacobian Spectrum of Neural Networks

Motivated by the hypothesis that well-conditioned Jacobian spectrum can speed
up learning of deep neural networks, Pennington, Sonenholtz, and Ganguli
introduced a spectral analysis of the Jacobian with mean-field approximation.
Based on Free probability theory, it is revealed that how the asymptotic spectral
distribution of the Jacobian of deep networks depend on initialization of weights
and activation functions.
This poster introduces how to guarantee the analysis of the Jacobianmathematically.

Kudo, Kazue (Department of Computer Science, Ochanomizu University)
Constrained quantum annealing of graph coloring
We investigate an efficient quantum annealing approach based on real-timequantum dynamics for graph coloring. The total Hamiltonian consists of driving
and problem Hamiltonians. In the constrained quantum annealing, a driving
Hamiltonian is chosen so that constraints are naturally satisfied without penalty
terms and the dimension of the Hilbert space is considerably reduced. Real-timequantum simulations in a small system provide some insight into quantum
annealing.

Nomura, Yusuke (RIKEN Center for Emergent Matter Science)
Machine learning reveals nodal spin liquid in S=1/2 J1-J2 Heisenberg model onsquare lattice
We study the phase diagram of S=1/2 J1-J2 Heisenberg model on square latticeby using machine learning variational methods. We show that the combination of
restricted Boltzmann machine and pair-product wave function gives highly
accurate description of the quantum states. We find that there exist nodal spin
liquid ground state in the S=1/2 J1-J2 Heisenberg model.

Kitazawa, Masakiyo (Physics, Osaka University)
Deep learning topological sector in lattice gauge theory
We apply a machine learning technique for identifying the topological charge ofquantum gauge configurations in four-dimensional SU(3) Yang-Mills theory. The
topological charge density measured on the original and smoothed gauge
configurations with and without dimensional reduction is used for inputs of theneural networks (NN) with and without convolutional layers.

Huebsch , Marie-Therese (Graduate School of Frontier Sciences, University ofTokyo)
Towards a Feature Space for Magnetic Structures
Many calculations in solid state physics become dramatically more efficient byusing an orthonormal basis adapted to the symmetry of the material. The cluster
multipole (CMP) expansion for magnetic structures [1] provides an orthonormal
basis set of magnetic configurations based on the crystallographic point group.
Recent discussions in terms of CMPs established a novel perspective ontransverse response properties [2] and spin dynamics in non-collinear
antiferromagnets [3]. Our analysis of the experimental data found on MagnData
provided by Bilbao Crystallographic Server [4] suggests that only a few terms
are finite in the CMP expansion of the most stable configurations in nature. This
sparseness can be exploited to build a larger learning database, however that isprovided that a reliable prediction of the most stable configuration can be
made?possibly by means of ab-initio calculations.
[1] M.-T. Suzuki et al., Phys. Rev. B 99, 174407 (2019).
[2] M. Ikhlas et al., Nat. Physics volume 13, pages 1085-1090 (2017); T. Higo etal., Nat. Photonics 12, 73?78 (2018).
[3] T. Nomoto and R. Arita, arXiv:1903.02259.
[4] S. V. Gallego et al., J. Appl. Cryst. 49, 1941-1956 (2016).

Tanabe, Takehiko (National Metrology Institute of Japan, National Institute ofAdvanced Industrial Science and Technology)
Potential for improving the local realization of coordinated universal time with aconvolutional neural network
The time difference between coordinated universal time (UTC) and a hydrogenmaser, which is a master oscillator for the local realization of UTC at the National
Metrology Institute of Japan (UTC(NMIJ)), has been predicted by using a
one-dimensional convolutional neural network (1D-CNN). We have found thatthe present 1D-CNN shows better performance as a predictor compared to thatbased on the Kalman filter, which suggests that the new computational approach
may provide a new and useful method for improving the synchronous accuracyof UTC(NMIJ) relative to UTC.

Hayashi, Yusuke (Japan Digital Design Inc.)
Neural Demon: Maxwell's demon as a meta-learner
In recent years, deep learning has achieved remarkable success in supervised
and reinforcement learning problems, such as image classification, speech
recognition, and game playing. However, these models are specialized for the
single task they are trained for. Meta-learning or few-shot learning offers apotential solution to this problem: by learning to learn across data from multiple
previous tasks, few-shot meta-learning algorithms can discover the structure
among tasks to enable fast learning of new tasks. In this presentation, we
consider a hierarchical Bayesian model with a global latent variable θ andtask-specific latent variables φ={φ^(t)}. First, we show that when the distributionof latent variables in the decoder p(θ, φ) is equal to the marginal distribution of
the encoder q(θ, φ), the thermodynamic costs of the meta-learning processprovide an upper bound on the amount of information that the model is able tolearn from its teacher. This allows us to introduce the second law of information
thermodynamics on meta-learning. Next, we propose the application of thismodel to multi-task image classification.

Konno, Tomohiko (National Institute of Information and Communications
Technologies)
Deep Learning Model for Finding New Superconductors
Superconductivity has been extensively studied since its discovery in 1911.
However, the feasibility of room-temperature superconductivity is unknown.
There is no theory of high-temperature superconductors and there are nocomputational

methods

high-temperatureforstrongly

superconductivity

correlated
emerges.

systems,

in

which

Exploration

of

new

superconductors still relies on the experience and intuition of experts and islargely a process of experimental trial and error. In one study, only 3% of thecandidatematerials

showedsuperconductivity.

Here

we

reportaninterdisciplinary attempt for finding new superconductors based on deeplearning. We represented the periodic table in a way that allows a deep learningmodel to learn it. Although we used only the chemical composition of materials
as information, we obtained an R-squared value of 0.92 for predicting thesuperconducting transition temperature, Tc, for materials in a database of
superconductors. We obtained three remarkable results. The deep learning
method can predict superconductivity for a material with a precision of 62%,
which shows the usefulness of the model; it found the recently discovered
superconductor CaBi2, which is not in the superconductor database; and it
found Fe-based high-temperature superconductors (discovered in 2008) fromthe training data before 2008. These results open the way for the discovery of
new high-temperature superconductor families.

Neupane, Chetanath (Tribhuvan University, Nepal)
Machine learning based study and visualization of molecular dynamics ofoxymyoglobin protein using PyMOL and UCSF Chimera

PyMOL and UCSF Chimera are molecular visualization tools, which have beendeveloped to provide clear image in molecular dynamic simulation of proteins
and other similar complex biostructures. Using these tools we can visualize
three-dimensional structures of bio-molecules graphically to understand thecomplete structural sketch of biological phenomena elegantly.
In this work, we study necessary numerical techniques and algorithms for the
analysis to numerically integrate Newton’s equation of motions needed formolecular dynamics simulations. We have taken oxymyoglobin protein for which
the required protein file is PDB ID 1MBO.pdb as to provide its brief structuraldetails using PyMOL and UCSF Chimera tools. Through the visualization
options in both tools, we have found that the iron core of the heme (HEM) groupin oxymyoglobin bounds oxygen. Also, using the structure analysis feature of
UCSF Chimera, we have estimated the distance between iron core of HEM to
the molecular oxygen and the distance between iron core to the histidine
93(H93) and found to be 1.827 A (OXY-iron core) and 2.065A (histidine 93-iron)respectively.

Inubushi, Masanobu (Graduate School of Engineering Science, Osaka
University)
Inference of the energy dissipation rate of turbulence by reservoir computing
The dissipation rate of the kinetic energy of fluid motion is the central quantity inthe statistical theories and modelings of turbulence. A type of recurrent neuralnetwork, called reservoir computing, enables us to accurately infer the energy
dissipation rate based only on the kinetic energy. Furthermore, we show that&#039;reusing&#039; the reservoir computer learned with data at a low
Reynolds number is effective for inference of the dissipation rate at much higher
Reynolds numbers.

Sakamoto, Kotaro (Center for Computational Sciences, University of Tsukuba)
Towards a geometrical understanding of physical phenomena via extraction ofdata manifolds using a GAN
Deep generative models learn a mapping from low-dimensional latent spaces tohigh-dimensional data spaces. Such mapping functions parameterise an
immersed manifold in the data space. We investigate GANs for modelling
physical data to extract data manifold structure of time series data and report
some results.
(Collaboration with Yuichiro Mori and Yoh-ichiro Mototake)