arXiv subjects

Isaac Tamblyn

Publications and source records attributed to Isaac Tamblyn.

At least 55 records · Page 3Linked to original sources

arXiv2019

Extensive deep neural networks for transferring small scale learning to large scale systems

We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with O(N) scaling. We use a form of domain decomposition for training and inference, where each sub-domain (tile) is comprised of a non-overlapping focus region surrounded by an overlapping context region. The size of these regions is motivated by the physical interaction length scales of the problem. We demonstrate the application of EDNNs to three physical systems: the Ising model and two hexagonal/graphene-like datasets. In the latter, an EDNN was able to make total energy predictions of a 60 atoms system, with comparable accuracy to density functional theory (DFT), in 57 milliseconds. Additionally EDNNs are well suited for massively parallel evaluation, as no communication is necessary during neural network evaluation. We demonstrate that EDNNs can be used to make an energy prediction of a two-dimensional 35.2 million atom system, over 1 square micrometer of material, at an accuracy comparable to DFT, in under 25 minutes. Such a system exists on a length scale visible with optical microscopy and larger than some living organisms.

physics.comp-ph↗

arXiv2018

Convolutional neural networks for atomistic systems

We introduce a new method, called CNNAS (convolutional neural networks for atomistic systems), for calculating the total energy of atomic systems which rivals the computational cost of empirical potentials while maintaining the accuracy of \emph{ab initio} calculations. This method uses deep convolutional neural networks (CNNs), where the input to these networks are simple representations of the atomic structure. We use this approach to predict energies obtained using density functional theory (DFT) for 2D hexagonal lattices of various types. Using a dataset consisting of graphene, hexagonal boron nitride (hBN), and graphene-hBN heterostructures, with and without defects, we trained a deep CNN that is capable of predicting DFT energies to an extremely high accuracy, with a mean absolute error (MAE) of 0.198 meV / atom (maximum absolute error of 16.1 meV / atom). To explore our new methodology, we investigate the ability of a deep neural network (DNN) in predicting a Lennard-Jones energy and separation distance for a dataset of dimer molecules in both two and three dimensions. In addition, we systematically investigate the flexibility of the deep learning models by performing interpolation and extrapolation tests.

cond-mat.mtrl-sci↗

arXiv2018

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4x4 Ising model. Using its success at this task, we motivate the study of the larger 8x8 Ising model, showing that the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only seeing a vanishingly small fraction of configuration space. Additionally, we show that the neural network has learned both the energy and magnetization operators with sufficient accuracy to replicate the low-temperature Ising phase transition. We then demonstrate the ability of the neural network to learn other spin models, teaching the convolutional deep neural network to accurately predict the long-range interaction of a screened Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian, and a modified Potts model Hamiltonian. In the case of the long-range interaction, we demonstrate the ability of the neural network to recover the phase transition with equivalent accuracy to the numerically exact method. Furthermore, in the case of the long-range interaction, the benefits of the neural network become apparent; it is able to make predictions with a high degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact calculation. Additionally, we demonstrate how the neural network succeeds at these tasks by looking at the weights learned in a simplified demonstration.

cond-mat.mtrl-sci↗

arXiv2017

Deep learning and the Schrödinger equation

We have trained a deep (convolutional) neural network to predict the ground-state energy of an electron in four classes of confining two-dimensional electrostatic potentials. On randomly generated potentials, for which there is no analytic form for either the potential or the ground-state energy, the neural network model was able to predict the ground-state energy to within chemical accuracy, with a median absolute error of 1.49 mHa. We also investigate the performance of the model in predicting other quantities such as the kinetic energy and the first excited-state energy of random potentials.

cond-mat.mtrl-sci↗

arXiv2017

Phase space sampling and operator confidence with generative adversarial networks

We demonstrate that a generative adversarial network can be trained to produce Ising model configurations in distinct regions of phase space. In training a generative adversarial network, the discriminator neural network becomes very good a discerning examples from the training set and examples from the testing set. We demonstrate that this ability can be used as an anomaly detector, producing estimations of operator values along with a confidence in the prediction.

cond-mat.stat-mech↗

arXiv2017

Energy Level Alignment at Hybridized Organic-metal Interfaces: the Role of Many-electron Effects

Hybridized molecule/metal interfaces are ubiquitous in molecular and organic devices. The energy level alignment (ELA) of frontier molecular levels relative to the metal Fermi level (EF) is critical to the conductance and functionality of these devices. However, a clear understanding of the ELA that includes many-electron self-energy effects is lacking. Here, we investigate the many-electron effects on the ELA using state-of-the-art, benchmark GW calculations on prototypical chemisorbed molecules on Au(111), in eleven different geometries. The GW ELA is in good agreement with photoemission for monolayers of benzene-diamine on Au(111). We find that in addition to static image charge screening, the frontier levels in most of these geometries are renormalized by additional screening from substrate-mediated intermolecular Coulomb interactions. For weakly chemisorbed systems, such as amines and pyridines on Au, this additional level renormalization (~1.5 eV) comes solely from static screened exchange energy, allowing us to suggest computationally more tractable schemes to predict the ELA at such interfaces. However, for more strongly chemisorbed thiolate layers, dynamical effects are present. Our ab initio results constitute an important step towards the understanding and manipulation of functional molecular/organic systems for both fundamental studies and applications.

cond-mat.mtrl-sci↗

arXiv2017

Structural characterizations of water-metal interfaces

We analyze and compare the structural, dynamical, and electronic properties of liquid water next to prototypical metals including Pt, graphite, and graphene. Our results are built on Born-Oppenheimer molecular dynamics (BOMD) generated using density functional theory (DFT) which explicitly include van der Waals (vdW) interactions within a first principles approach. All calculations reported use large simulation cells, allowing for an accurate treatment of the water-electrode interfaces. We have included vdW interactions through the use of the optB86b-vdW exchange correlation functional. Comparisons with the Perdew-Burke-Ernzerhof (PBE) exchange correlation functional are also shown. We find an initial peak, due to chemisorption, in the density profile of the liquid water-Pt interface not seen in the liquid water-graphite interface, liquid water-graphene interface, nor interfaces studied previously. To further investigate this chemisorption peak, we also report differences in the electronic structure of single water molecules on both Pt and graphite surfaces. We find that a covalent bond forms between the single water molecule and the Pt surface, but not between the single water molecule and the graphite surface. We also discuss the effects that defects and dopants in the graphite and graphene surfaces have on the structure and dynamics of liquid water.

cond-mat.mtrl-sci↗

arXiv2016

Sampling algorithms for validation of supervised learning models for Ising-like systems

In this paper, we build and explore supervised learning models of ferromagnetic system behavior, using Monte-Carlo sampling of the spin configuration space generated by the 2D Ising model. Given the enormous size of the space of all possible Ising model realizations, the question arises as to how to choose a reasonable number of samples that will form physically meaningful and non-intersecting training and testing datasets. Here, we propose a sampling technique called ID-MH that uses the Metropolis-Hastings algorithm creating Markov process across energy levels within the predefined configuration subspace. We show that application of this method retains phase transitions in both training and testing datasets and serves the purpose of validation of a machine learning algorithm. For larger lattice dimensions, ID-MH is not feasible as it requires knowledge of the complete configuration space. As such, we develop a new "block-ID" sampling strategy: it decomposes the given structure into square blocks with lattice dimension no greater than 5 and uses ID-MH sampling of candidate blocks. Further comparison of the performance of commonly used machine learning methods such as random forests, decision trees, k nearest neighbors and artificial neural networks shows that the PCA-based Decision Tree regressor is the most accurate predictor of magnetizations of the Ising model. For energies, however, the accuracy of prediction is not satisfactory, highlighting the need to consider more algorithmically complex methods (e.g., deep learning).

cond-mat.stat-mech↗

arXiv2016

Hashkat: Large-scale simulations of online social networks

Hashkat (http://hashkat.org) is a free, open source, agent based simulation software package designed to simulate large-scale online social networks (e.g. Twitter, Facebook, LinkedIn, etc). It allows for dynamic agent generation, edge creation, and information propagation. The purpose of hashkat is to study the growth of online social networks and how information flows within them. Like real life online social networks, hashkat incorporates user relationships, information diffusion, and trending topics. Hashkat was implemented in C++, and was designed with extensibility in mind. The software includes Shell and Python scripts for easy installation and usability. In this report, we describe all of the algorithms and features integrated into hashkat before moving on to example use cases. In general, hashkat can be used to understand the underlying topology of social networks, validate sampling methods of such networks, develop business strategy for advertising on online social networks, and test new features of an online social network before going into production.

cs.SI↗

arXiv2014

Emergent rhombus tilings from molecular interactions with $M$-fold rotational symmetry

We show that model molecules with particular rotational symmetries can self-assemble into network structures equivalent to rhombus tilings. This assembly happens in an emergent way, in the sense that molecules spontaneously select irregular 4-fold local coordination from a larger set of possible local binding geometries. The existence of such networks can be rationalized by simple geometrical arguments, but the same arguments do not guarantee networks' spontaneous self-assembly. This class of structures must in certain regimes of parameter space be able to reconfigure into networks equivalent to triangular tilings.

cond-mat.stat-mech↗

arXiv2014

Electronic Structure of Liquid Water and a Platinum Surface

Many-body perturbation theory within the G$_0$W$_0$ approximation is used to determine molecular orbital level alignment at a liquid water/Pt(111) interface generated through $ab~ initio$ molecular dynamics. Molecular orbital energy levels are shown to depend both on the position of H$_2$O molecules within the liquid relative to the surface and the details of their local bonding environment. Standard density functional theory calculations disagree qualitatively with level alignment predicted by many-body perturbation theory.

cond-mat.mes-hall↗

arXiv2014

Common physical framework explains phase behavior and dynamics of atomic, molecular and polymeric network-formers

We show that the self-assembly of a diverse collection of building blocks can be understood within a common physical framework. These building blocks, which form periodic honeycomb networks and nonperiodic variants thereof, range in size from atoms to micron-scale polymers, and interact through mechanisms as different as hydrogen bonds and covalent forces. A combination of statistical mechanics and quantum mechanics shows that one can capture the physics that governs assembly of these networks by resolving only the geometry and strength of building block interactions. The resulting framework reproduces a broad range of phenomena seen experimentally, including periodic and nonperiodic networks in thermal equilibrium, and nonperiodic supercooled and glassy networks away from equilibrium. Our results show how simple `design criteria' control assembly of a wide variety of networks, and suggest that kinetic trapping can be a useful way of making functional assemblies.

cond-mat.stat-mech↗

arXiv2013

Competing thermodynamic and dynamic factors select molecular assemblies on a gold surface

Controlling the self-assembly of surface-adsorbed molecules into nanostructures requires understanding physical mechanisms that act across multiple length and time scales. By combining scanning tunneling microscopy with hierarchical ab initio and statistical mechanical modeling of 1,4-substituted benzenediamine (BDA) molecules adsorbed on a gold (111) surface, we demonstrate that apparently simple nanostructures are selected by a subtle competition of thermodynamics and dynamics. Of the collection of possible BDA nanostructures mechanically stabilized by hydrogen bonding, the interplay of intermolecular forces, surface modulation, and assembly dynamics select at low temperature a particular subset: low free energy oriented linear chains of monomers, and high free energy branched chains.

cond-mat.mes-hall↗

arXiv2012

Random and ordered phases of off-lattice rhombus tiles

We study the covering of the plane by non-overlapping rhombus tiles, a problem well-studied only in the limiting case of dimer coverings of regular lattices. We go beyond this limit by allowing tiles to take any position and orientation on the plane, to be of irregular shape, and to possess different types of attractive interactions. Using extensive numerical simulations we show that at large tile densities there is a phase transition from a fluid of rhombus tiles to a solid packing with broken rotational symmetry. We observe self-assembly of broken-symmetry phases, even at low densities, in the presence of attractive tile-tile interactions. Depending on tile shape and interactions the solid phase can be random, possessing critical orientational fluctuations, or crystalline. Our results suggest strategies for controlling tiling order in experiments involving `molecular rhombi'.

cond-mat.stat-mech↗

arXiv2012

Quantitative Molecular Orbital Energies within a $G_0W_0$ Approximation

Using many-body perturbation theory within the $G_0W_0$ approximation, we explore routes for computing the ionization potential (IP), electron affinity (EA), and fundamental gap of three gas-phase molecules -- benzene, thiophene, and (1,4) diamino-benzene -- and compare with experiments. We examine the dependence of the IP on the number of unoccupied states used to build the dielectric function and the self energy, as well as the dielectric function plane-wave cutoff. We find that with an effective completion strategy for approximating the unoccupied subspace, and a converged dielectric function kinetic energy cutoff, the computed IPs and EAs are in excellent quantitative agreement with available experiment (within 0.2 eV), indicating that a one-shot $G_0W_0$ approach can be very accurate for calculating addition/removal energies of small organic molecules. Our results indicate that a sufficient dielectric function kinetic energy cutoff may be the limiting step for a wide application of $G_0W_0$ to larger organic systems.

cond-mat.mtrl-sci↗

arXiv2012

Molecular Adsorption on Metal Surfaces with a van der Waals Density Functional

The adsorption of 1,4-benzenediamine (BDA) on the Au(111) surface and azobenzene on the Ag(111) surface is investigated using density functional theory (DFT) with a non-local density functional (vdW-DF) and a semi-local Perdew-Burke-Ernzerhof (PBE) functional. For BDA on Au(111), the inclusion of London dispersion interactions not only dramatically enhances the molecule-substrate binding, resulting in adsorption energies consistent with experimental results, but also significantly alters the BDA binding geometry. For azobenzene on Ag(111), the vdW-DF produces superior adsorption energies compared to those obtained with other dispersion corrected DFT approaches. These results provide evidence for the applicability of the vdW-DF method and serves as a practical benchmark for the investigation of molecules adsorbed on noble metal surfaces.

cond-mat.mtrl-sci↗

arXiv2011

Electronic energy level alignment at metal-molecule interfaces with a GW approach

Using density functional theory and many-body perturbation theory within a GW approximation, we calculate the electronic structure of a metal-molecule interface consisting of benzene diamine (BDA) adsorbed on Au(111). Through direct comparison with photoemission data, we show that a conventional G$_0$W$_0$ approach can underestimate the energy of the adsorbed molecular resonance relative to the Au Fermi level by up to 0.8 eV. The source of this discrepancy is twofold: a 0.7 eV underestimate of the gas phase ionization energy (IE), and a 0.2 eV overestimate of the Au work function. Refinements to self-energy calculations within the GW framework that account for deviations in both the Au work function and BDA gas-phase IE can result in an interfacial electronic level alignment in quantitative agreement with experiment.

cond-mat.mtrl-sci↗

arXiv2009

A note on the metallization of compressed liquid hydrogen

We examine the molecular-atomic transition in liquid hydrogen as it relates to metallization. Pair potentials are obtained from first principles molecular dynamics and compared with potentials derived from quadratic response. The results provide insight into the nature of covalent bonding under extreme conditions. Based on this analysis, we construct a schematic dissociation-metallization phase diagram and suggest experimental approaches that should significantly reduce the pressures necessary for the realization of the elusive metallic phase of hydrogen.

cond-mat.mtrl-sci↗