Bobby Sumpter

Bobby Sumpter
Oak Ridge National Laboratory | ORNL · Computer Science & Mathematics and Center for Nanophase Materials Sciences

About

803
Publications
122,634
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
32,497
Citations

Publications

Publications (803)
Preprint
Full-text available
Two-dimensional (2D) transition metal carbides and nitrides, known as MXenes, possess unique physical and chemical properties, enabling diverse applications in fields ranging from energy storage to communication, catalysis, sensing, healthcare, and beyond. The transition metal and nonmetallic atoms in MXenes can exhibit distinct coordination enviro...
Preprint
This work thoroughly examines several analytical tools, each possessing a different level of mathematical intricacy, for the purpose of characterizing the orientation distribution function of elongated objects under flow. Our investigation places an emphasis on connecting the orientation distribution to the small angle scattering spectra measured e...
Preprint
Small-angle scattering (SAS) techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine lea...
Article
Full-text available
In this study, we present a novel orientation discretization approach based on the rhombic triacontahedron for Monte Carlo simulations of semiflexible polymer chains, aiming at enhancing structural analysis through rheo-small-angle scattering (rheo-SAS). Our approach provides a more accurate representation of the geometric features of semiflexible...
Article
Full-text available
With energy shortages and excessive CO2 emissions driving climate change, converting CO2 into high‐value‐added products offers a promising solution for carbon recycling. We investigate CO2 reduction reactions (CO2RR) catalyzed by 10 single‐atom catalysts (SACs), incorporating weak non‐covalent interactions, specifically lone pair‐π and H‐π interact...
Preprint
We develop a Machine Learning Inversion method for analyzing scattering functions of mechanically driven polymers and extracting the corresponding feature parameters, which include energy parameters and conformation variables. The polymer is modeled as a chain of fixed-length bonds constrained by bending energy, and it is subject to external forces...
Preprint
We develop off-lattice simulations of semiflexible polymer chains subjected to applied mechanical forces using Markov Chain Monte Carlo. Our approach models the polymer as a chain of fixed-length bonds, with configurations updated through adaptive non-local Monte Carlo moves. This proposed method enables precise calculation of a polymer's response...
Article
Full-text available
Solid‐supported amines having low molecular weight branched poly(ethylenimine) (PEI) physically impregnated into porous solid supports are promising adsorbents for CO2 capture. Co‐impregnating short‐chain poly(ethylene glycol) (PEG) together with PEI alters the performance of the adsorbent, delivering improved amine efficiency (AE, mol CO2 sorbed/m...
Article
An innovative strategy is presented that incorporates deep auto-encoder networks into a least-squares fitting framework to address the potential inversion problem in small-angle scattering. To evaluate the performance of the proposed approach, a detailed case study focusing on charged colloidal suspensions was carried out. The results clearly indic...
Preprint
Two-dimensional (2D) halide perovskites (HPs) are now an emerging materials system that exhibits intriguing optoelectronic functionalities. Conventionally, they have been synthesized with linear and/or planar molecular spacers, rendering nominal modifications in optoelectronic properties. In contrast, lower dimensional HPs (0D and 1D) have been acc...
Article
Full-text available
A method for characterizing the topological fluctuations in liquids is proposed. This approach exploits the concept of the weighted gyration tensor of a collection of particles and permits the definition of a local configurational unit (LCU). The first principal axis of the gyration tensor serves as the director of the LCU, which can be tracked and...
Article
Full-text available
Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. How...
Article
Full-text available
Induction of point defects in nanomaterials can bestow upon them entirely new physics or augment their pre-existing physical properties, thereby expanding their potential use in green energy technology. Predicting structure-property relationships for defects a priori is challenging, and developing methods for precise control of defect type, density...
Article
Full-text available
Recently, two-dimensional (2D) FeSe-like anti-MXenes (or XMenes), composed of late d-block transition metal M and p-block nonmetal X elements, have been both experimentally and theoretically investigated. Here, we select three...
Article
Inspired by biological neuromorphic computing, artificial neural networks based on crossbar arrays of bilayer tantalum oxide memristors have shown to be promising alternatives to conventional complementary metal‐oxide‐semiconductor (CMOS) architectures. In order to understand the driving mechanism in these oxide systems, tantalum oxide films are re...
Article
Full-text available
Using first-principles calculations we model the out-of-plane switching of local dipoles in CuInP2S6 (CIPS) that are largely induced by Cu off-centering. Previously, a coherent switching of polarization via a quadruple-well potential was proposed for these materials. In the supercells we considered, we find multiple structures with similar energies...
Article
Resistive switching in thin films has been widely studied in a broad range of materials. Yet, the mechanisms behind electroresistive switching have been persistently difficult to decipher and control, in part due to their non-equilibrium nature. Here, we demonstrate new experimental approaches that can probe resistive switching phenomena, utilizing...
Article
We present results from explicit-solvent coarse-grained molecular dynamics (MD) simulations of fully charged, salt-free, and unentangled polyelectrolytes in semidilute solutions. The inclusion of a polar solvent in the model allows for a more physical representation of these solutions at concentrations, where the assumptions of a continuum dielectr...
Article
The collective density–density and hydrostatic pressure–pressure correlations of glass-forming liquids are spatiotemporally mapped out using molecular dynamics simulations. It is shown that the sharp rise of structural relaxation time below the Arrhenius temperature coincides with the emergence of slow, nonhydrodynamic collective dynamics on mesosc...
Article
Full-text available
The presence of point defects, such as vacancies, plays an important role in materials design. Here, we explore the extrapolative power of a graph neural network (GNN) to predict vacancy formation energies. We show that a model trained only on perfect materials can also be used to predict vacancy formation energies (Evac) of defect structures witho...
Preprint
Full-text available
Star block copolymers (s-BCPs) have potential applications as novel surfactants or amphiphiles for emulsification, compatbilization, chemical transformations and separations. s-BCPs are star-shaped macromolecules comprised of linear chains of different chemical blocks (e.g., solvophilic and solvophobic blocks) that are covalently joined at one junc...
Article
Conversion of plastic wastes to fatty acids is an attractive means to supplement the sourcing of these high-value, high-volume chemicals. We report a method for transforming polyethylene (PE) and polypropylene (PP) at ~80% conversion to fatty acids with number-average molar masses of up to ~700 and 670 daltons, respectively. The process is applicab...
Article
Full-text available
Plastic represents an essential material in our society; however, a major imbalance between their high production and end-of-life management is leading to unrecovered energy, economic hardship, and a high carbon footprint. The adoption of plastic recycling has been limited, mainly due to the difficulty of recycling mixed plastics. Here, we report a...
Article
Moving toward a future of efficient, accessible, and less carbon-reliant energy devices has been at the forefront of energy research innovations for the past 30 years. Metal-halide perovskite (MHP) thin films have gained significant attention due to their flexibility of device applications and tunable capabilities for improving power conversion eff...
Preprint
Full-text available
Resistive switching in thin films has been widely studied in a broad range of materials. Yet the mechanisms behind electroresistive switching have been persistently difficult to decipher and control, in part due to their non-equilibrium nature. Here, we demonstrate new experimental approaches that can probe resistive switching phenomena, utilizing...
Article
Full-text available
Polyzwitterions (PZs) are considered as model synthetic analogues of intrinsically disordered proteins. Based on this analogy, PZs in dilute aqueous solutions are expected to attain either globular (i.e., molten, compact) or random coil conformations. Addition of salt is expected to open these conformations. To the best of our knowledge, these hypo...
Preprint
Full-text available
Using first-principles calculations we model the out-of-plane switching of local dipoles in CuInP$_2$S$_6$ (CIPS) that are largely induced by Cu off-centering, and where previously a qudrupule-well potential for polarization switching was claimed. In the supercells considered, we find multiple structures with similar energies but with different loc...
Article
Full-text available
We show that unsupervised machine learning can be used to learn chemical transformation pathways from observational Scanning Transmission Electron Microscopy (STEM) data. To enable this analysis, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and t...
Preprint
Full-text available
Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. How...
Article
Ferroelectric materials such as BaTiO3 show tremendous potential for emerging advances in memory devices, particular neuromorphic type devices. High density of memory can be obtained by stabilising polar domain walls at the nanoscale, regions of discontinuity between the well-defined polarization order parameter, but little is known about what cont...
Article
A machine learning solution for the potential inversion problem in elastic scattering is outlined. The inversion scheme consists of two major components, a generative network featuring a variational autoencoder which extracts the targeted static two-point correlation functions from experimentally measured scattering cross sections, and a Gaussian p...
Article
Here, we report synergistic nanostructured surfaces combining bactericidal and bacteria-releasing properties. A polystyrene-block-poly(methyl methacrylate) (PS-block-PMMA) diblock copolymer is used to fabricate vertically oriented cylindrical PS structures ("PS nanopillars") on silicon substrates. The results demonstrate that the PS nanopillars (wi...
Article
Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materi...
Article
With new instrumentation design, robotics, and in-operando hyphenated analytical tool automation, the intelligent discovery of synthesis pathways is becoming feasible. It can potentially bridge the gap for the scale-up of new materials. We review current progress and describe a new system that uses an autonomous continuous flow chemistry framework...
Article
To understand and resolve adsorption, reconfiguration, and equilibrium conformations of charged star copolymers, we carried out an integrated experimental and coarse-grained molecular dynamics simulation study of the assembly process at...
Article
Explicit time-dependent electronic structure theory methods are increasingly prevalent in the areas of condensed matter physics and quantum chemistry, with the broad-band optical absorptivity of molecular and small condensed-phase systems nowadays routinely studied with such approaches. In this paper, it is demonstrated that electronic dynamics sim...
Article
Carbon fiber (CF) reinforced polymers (CFRPs) have experienced widespread use in various industries. One of the most important parameters that controls the macroscopic property of CFRPs is the interface between a polymer matrix and CF. There is growing evidence to suggest the formation of a bound polymer layer (BPL), i.e., polymer chains that physi...
Article
We present a quantitative approach to the self-dynamics of polymers under steady flow by employing a set of complementary reference frames and extending the spherical harmonic expansion technique to dynamic density correlations. Application of this method to nonequilibrium molecular dynamics simulations of polymer melts reveals a number of universa...
Article
Full-text available
The novel coronavirus SARS-CoV-2 infects human cells using a mechanism that involves binding and structural rearrangement of its Spike protein. Understanding protein rearrangement and identifying specific amino acids where mutations affect protein rearrangement has attracted much attention for drug development. In this manuscript, we use a mathemat...
Article
The self-correlation function and corresponding self-intermediate scattering function in Fourier space are important quantities for describing the molecular motions of liquids. This work draws attention to a largely overlooked issue concerning the analysis of these space-time density-density correlation functions of polymers. We show that the inter...
Article
In the last decade, the atomically-focused electron beams utilized in scanning transmission electron microscopes (STEMs) have been shown to induce a broad set of local structural transformations in materials, opening pathways for directing material synthesis and modification atom-by-atom. The mechanisms underlying these transformations remain large...
Article
Supported amines are a promising class of CO2 sorbents offering large uptake capacities and fast uptake rates. Among supported amines, poly(ethyleneimine) (PEI) physically impregnated in the mesopores of SBA-15 silica is widely used. Within these composite materials, the chain dynamics and morphologies of PEI strongly influence the CO2 capture perf...
Article
Full-text available
Many computational models have been developed to predict the rates of atomic displacements in two-dimensional (2D) materials under electron beam irradiation. However, these models often drastically underestimate the displacement rates in 2D insulators, in which beam-induced electronic excitations can reduce the binding energies of the irradiated at...
Article
The electronic structure of a material, such as its density of states (DOS), provides key insights into its physical and functional properties and serves as a valuable source of high-quality features for many materials screening and discovery workflows. However, the computational cost of calculating the DOS, most commonly with density functional th...
Preprint
Full-text available
The presence of point defects such as vacancies plays an important role in material design. Here, we demonstrate that a graph neural network (GNN) model trained only on perfect materials can also be used to predict vacancy formation energies ($E_{vac}$) of defect structures without the need for additional training data. Such GNN-based predictions a...
Preprint
The rapid development of machine learning (ML) methods has fundamentally affected numerous applications ranging from computer vision, biology, and medicine to accounting and text analytics. Until now, it was the availability of large and often labeled data sets that enabled significant breakthroughs. However, the adoption of these methods in classi...
Article
Full-text available
We outline a machine learning strategy for quantitively determining the conformation of AB-type diblock copolymers with excluded volume effects using small angle scattering. Complemented by computer simulations, a correlation matrix connecting conformations of different copolymers according to their scattering features is established on the mathema...
Article
Full-text available
Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as oppos...
Preprint
Full-text available
Electron-beam (e-beam) manipulation of single dopant atoms in an aberration-corrected scanning transmission electron microscope is emerging as a method for directed atomic motion and atom-by-atom assembly. Until now, the dopant species have been limited to atoms closely matched to carbon in terms of ionic radius and capable of strong covalent bondi...
Article
Full-text available
Machine learning and artificial intelligence (AI/ML) methods are beginning to have significant impact in chemistry and condensed matter physics. For example, deep learning methods have demonstrated new capabilities for high-throughput virtual screening, and global optimization approaches for inverse design of materials. Recently, a relatively new b...
Article
Full-text available
Small angle scattering techniques have now been routinely used to quantitatively determine the potential of mean force in colloidal suspensions. However the numerical accuracy of data interpretation is often compounded by the approximations adopted by liquid state analytical theories. To circumvent this long standing issue, here we outline a machin...
Article
Full-text available
Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional search problems and learning domains that have c...
Article
Graphene is one of the most intriguing two-dimensional carbon materials. Its mechanical strength and failure are key concerns for materials engineering and applications. Despite the success of fracture mechanics, the mechanism of how pristine materials fail remains an elusive problem. While many theoretical studies based on molecular dynamics using...
Chapter
Graphene and its analogues offer a broad range of application opportunities for (opto)-electronics, sensing, catalysis, phase separation, energy conversion and storage, etc. Engineering graphene properties often relies on its controllable functionalization, defect formation and patterning, and reactive gas etching. In this chapter, we survey the dy...
Chapter
Microscopes utilizing convergent electron and ion beams are emerging as powerful tools for both imaging and manipulating two-dimensional materials with atomic resolution, allowing the ultimate limits of nanofabrication to be realized. In this chapter, we detail the use of time-dependent electronic structure theory to determine the excited state pro...
Article
Full-text available
Conjugated copolymers containing electron donor and acceptor units in their main chain have emerged as promising materials for organic electronic devices due to their tunable optoelectronic properties.
Preprint
Full-text available
Many computational models have been developed to predict the rates of atomic displacements in two-dimensional (2D) materials under electron beam irradiation. However, these models often drastically underestimate the displacement rates in 2D insulators, in which beam-induced electronic excitations can reduce the binding energies of the irradiated at...
Preprint
Full-text available
We put forth a simple and yet practical theoretical model generalized from Raoult’s law and Henry’s law and show that it can be reduced to these two laws under limiting conditions. The model entertains a hybrid parameter h_B with activity coefficient bundled into it, which smoothly bridges the p_B^* and K_B coefficients in Raoult’s law and Henry’s...