arXiv subjects

Bobby G. Sumpter

Publications and source records attributed to Bobby G. Sumpter.

At least 19 recordsLinked to original sources

arXiv2026

Predicting Physical and Physical-Chemical Properties of Molecular-Based Materials Using Computational Neural Networks

A computational scheme, which utilizes neural networks, was developed to predict properties of molecular-based materials from chemical structures. The method uses a set of simple algorithms to encode the structure and composition of organic molecules directly into numerical vectors, which is used as input for neural networks. Backpropagation type neural networks are then used to correlate these numeric inputs with a set of desired properties. Calculated results for a series of hydrocarbons, hydrofluorocarbons, and crown ethers demonstrate average accuracies of 0.2-8.1% with maximum deviations of 16-20% for a broad range of thermodynamic, physical, and physical-chemical characteristics (heat capacity, enthalpy, heat of evaporation, boiling point, density, refractive index, stability constants, etc.). In addition, a number of physical and mechanical properties were estimated for polymeric materials and compared with regression analysis. Based on the neural network capabilities of formulating accurate quantitative structure property relationships, a technique called computational synthesis is suggested for performing materials design.

cond-mat.mtrl-sci↗

arXiv2025

Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning

The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture multiscale physics. Here, we demonstrate such an approach using symbolic regression (SR) with equation learner networks and a reinforcement learning search engine to derive interpretable equations for interatomic interactions. Training data were generated through nested ensemble sampling with density functional theory (DFT) energetics, spanning crystalline to highly disordered states. The optimization of the learner network employed continuous-action Monte Carlo Tree Search (MCTS) combined with gradient descent, enabling efficient exploration of function space. For copper as a representative transition metal, an unconstrained search produced models that outperformed fixed-form Sutton-Chen EAM potentials. The SR-derived models (SR1 and SR2) reproduced key material properties - lattice constants, cohesive energies, equations of state, elastic constants, phonon dispersion, defect formation energies, surface/bulk energetics, and phase transformation with significantly improved accuracy. Furthermore, stringent melting simulations using two-phase solid-amorphous interfaces confirmed that SR models accurately capture the interplay of vibrational entropy, cohesive energy, and structural dynamics, surpassing SC-EAM in both qualitative and quantitative predictions. This highlights the potential of SR to deliver fast, accurate, flexible, and physically meaningful potentials, advancing predictive modeling across scales.

cond-mat.mtrl-sci↗

arXiv2025

Cross-scale Modeling of Polymer Topology Impact on Extrudability through Molecular Dynamics and Computational Fluid Dynamics

Understanding how polymer topology influences melt extrudability is critical for advancing material design in extrusion-based additive manufacturing. In this work, we develop a bottom-up, cross-scale modeling framework that integrates coarse-grained molecular dynamics (CGMD) and continuum-scale computational fluid dynamics (CFD) to quantitatively assess the effects of polymer architecture on extrudability A range of branched polydimethylsiloxane (PDMS) polymers are systematically designed by varying backbone length, sidechain length, grafting density, grafted block ratio, and periodicity of grafted-ungrafted segments. CGMD simulations are used to compute zero-shear viscosity and relaxation times, which are then incorporated into the Phan-Thien-Tanner (PTT) model within a computational fluid dynamics (CFD) model to predict pressure drop of PDMS during extrusion through printer nozzle. Qualitative analysis reveals that polymers with concentrated grafted blocks exhibit significantly higher zero-shear viscosity than stochastically branched analogs, while sidechain inertia drives longer relaxation time. However, for untangled and weakly entangled PDMS, relaxation time remains in the nanosecond range, making shear-thinning and elastic effects negligible. Consequently, zero-shear viscosity emerges as the primary determinant of extrudability. This cross-scale modeling strategy provides a predictive framework for guiding the rational design of extrudable polymer materials with tailored topologies.

cond-mat.soft↗

arXiv2025

Room-Temperature High-Purity Single Photon Emission from Carbon-Doped Boron Nitride Thin Films

Hexagonal boron nitride (hBN) has emerged as an excellent host material for generating room temperature single photons exhibiting high brightness and spin-photon entanglement. However, challenges in improving purity, stability, and scalability limit its use in quantum technologies. Here, we demonstrate highly pure and stable single photon emitters (SPEs) in hBN by directly growing carbon-doped, centimeter-scale hBN thin films using the pulsed laser deposition (PLD) method. These SPEs exhibit room temperature operation with polarized emission, achieving a g(2)(0) value of 0.015, which is among the lowest reported for room temperature SPEs and the lowest achieved for hBN SPEs. It also exhibits high brightness ( around 0.5 million counts per second), remarkable stability during continuous operation (> 15 minutes), and a Debye-Waller factor of 45%. First-principles calculations reveal unique carbon defects responsible for these properties, enabled by PLD's low-temperature synthesis and in-situ doping. Our results demonstrate an effective method for large-scale production of high-purity, stable SPEs in hBN, enabling robust quantum optical sources for various applications.

physics.app-ph↗

arXiv2025

Deciphering the Scattering of Mechanically Driven Polymers using Deep Learning

We present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compressed into a three-dimensional latent space using a Variational Autoencoder (VAE), and two converter networks establish a bidirectional mapping between the polymer parameters (bending modulus, stretching force, and steady shear) and the scattering functions. The training data are generated using off-lattice Monte Carlo simulations to avoid the orientational bias inherent in lattice models, ensuring robust sampling of polymer conformations. The feasibility of this bidirectional mapping is demonstrated by the organized distribution of polymer parameters in the latent space. By integrating the converter networks with the VAE, we obtain a generator that produces scattering functions from given polymer parameters and an inferrer that directly extracts polymer parameters from scattering data. While the generator can be utilized in a traditional least-squares fitting procedure, the inferrer produces comparable results in a single pass and operates three orders of magnitude faster. This approach offers a scalable, automated tool for polymer scattering analysis and provides a promising foundation for extending the method to other scattering models, experimental validation, and the study of time-dependent scattering data.

cond-mat.soft↗

arXiv2025

Unlocking Hidden Information in Sparse Small-Angle Neutron Scattering Measurement

Small-angle neutron scattering (SANS) is a powerful technique for probing the nanoscale structure of materials. However, the fundamental limitations of neutron flux pose significant challenges for rapid, high-fidelity data acquisition required in many experiments. To circumvent this difficulty, we introduce a Bayesian statistical framework based on Gaussian process regression (GPR) to infer high-quality SANS intensity profiles from measurements with suboptimal signal-to-noise ratios (SNR). Unlike machine learning approaches that depend on extensive training datasets, the proposed one-shot method leverages the intrinsic mathematical properties of the scattering function, smoothness and continuity, offering a generalizable solution beyond the constraints of data-intensive techniques. By examining existing SANS experimental data, we demonstrate that this approach can reduce measurement time by between one and two orders of magnitude while maintaining accuracy and adaptability across different SANS instruments. By improving both efficiency and reliability, this method extends the capabilities of SANS, enabling broader applications in time-sensitive and low-flux experimental conditions.

physics.app-ph↗

arXiv2025

Simulation of 24,000 Electrons Dynamics: Real-Time Time-Dependent Density Functional Theory (TDDFT) with the Real-Space Multigrids (RMG)

We present the theory, implementation, and benchmarking of a real-time time-dependent density functional theory (RT-TDDFT) module within the RMG code, designed to simulate the electronic response of molecular systems to external perturbations. Our method offers insights into non-equilibrium dynamics and excited states across a diverse range of systems, from small organic molecules to large metallic nanoparticles. Benchmarking results demonstrate excellent agreement with established TDDFT implementations and showcase the superior stability of our time-integration algorithm, enabling long-term simulations with minimal energy drift. The scalability and efficiency of RMG on massively parallel architectures allow for simulations of complex systems, such as plasmonic nanoparticles with thousands of atoms. Future extensions, including nuclear and spin dynamics, will broaden the applicability of this RT-TDDFT implementation, providing a powerful toolset for studies of photoactive materials, nanoscale devices, and other systems where real-time electronic dynamics is essential.

physics.comp-ph↗

arXiv2025

A Panoramic View of MXenes via a New Design Strategy

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 environments, potentially leading to a wide variety of 2D phases. Despite extensive research and significant advancements, a fundamental understanding of MXenes' phase diversity and its relationship with their hierarchical precursors, including intermediate MAX phases and parent bulk phases, remains limited. Using high-throughput modeling based on first-principles density functional theory, we unveil a wide range of MXenes and comprehensively evaluate their relative stabilities across a large chemical space. The key lies in considering both octahedral and trigonal prismatic coordination environments characteristic of various bulk phases. Through this comprehensive structural library of MXenes, we uncover a close alignment between the phase stability of MXenes and that of their hierarchical 3D counterparts. Building on this, we demonstrate a new design strategy where the atomic coordination environments in parent bulk phases can serve as reliable predictors for the design of MXenes, reducing reliance on intermediate MAX phases. Our study significantly expands the landscape of MXenes, at least doubling the number of possible structures.

cond-mat.mtrl-sci↗

arXiv2025

Elongated particles in flow: Commentary on small angle scattering investigations

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 experimentally. The diverse range of mathematical approaches investigated herein provides insights into the flow behavior of elongated particles from different perspectives and serves as powerful tools for elucidating the complex interplay between flow dynamics and the orientation distribution function.

cond-mat.soft↗

arXiv2024

Scattering-Based Structural Inversion of Soft Materials via Kolmogorov-Arnold Networks

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 learning framework based on the Kolmogorov-Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions -- two representative soft matter systems -- we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems.

cond-mat.soft↗

arXiv2024

Machine Learning Inversion from Scattering for Mechanically Driven Polymers

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 such as stretching and shear. We generate a data set consisting of random combinations of energy parameters, including bending modulus, stretching, and shear force, along with Monte Carlo-calculated scattering functions and conformation variables such as end-to-end distance, radius of gyration, and the off-diagonal component of the gyration tensor. The effects of the energy parameters on the polymer are captured by the scattering function, and principal component analysis ensures the feasibility of the Machine Learning inversion. Finally, we train a Gaussian Process Regressor using part of the data set as a training set and validate the trained regressor for inversion using the rest of the data. The regressor successfully extracts the feature parameters.

cond-mat.soft↗

arXiv2024

Off-Lattice Markov Chain Monte Carlo Simulations of Mechanically Driven Polymers

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 to a wide range of mechanical forces, which traditional on-lattice models cannot achieve. Our approach has shown excellent agreement with theoretical predictions of persistence length and end-to-end distance in quiescent states, as well as stretching distances under tension. Moreover, our model eliminates the orientational bias present in on-lattice models, which significantly impacts calculations such as the scattering function, a crucial technique for revealing polymer conformation.

cond-mat.soft↗

arXiv2024

AI for Manufacturing and Healthcare: a chemistry and engineering perspective

Artificial Intelligence (AI) approaches are increasingly being applied to more and more domains of Science, Engineering, Chemistry, and Industries to not only improve efficiencies and enhance productivity, but also enable new capabilities. The new opportunities range from automated molecule design and screening, properties prediction, gaining insights of chemical reactions, to computer-aided design, predictive maintenance of systems, robotics, and autonomous vehicles. This review focuses on the new applications of AI in manufacturing and healthcare. For the Manufacturing Industries, we focus on AI and algorithms for (1) Battery, (2) Flow Chemistry, (3) Additive Manufacturing, (4) Sensors, and (5) Machine Vision. For Healthcare applications, we focus on: (1) Medical Vision (2) Diagnosis, (3) Protein Design, and (4) Drug Discovery. In the end, related topics are discussed, including physics integrated machine learning, model explainability, security, and governance during model deployment.

cond-mat.mtrl-sci↗

arXiv2023

Accelerated Design of Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning

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 junction point. Various parameters of these macromolecules can be tuned to obtain desired surface properties, including the number of arms, composition of the arms, and the degree-of-polymerization of the blocks (or the length of the arm). This makes identification of the optimal s-BCP design highly non-trivial as the total number of plausible s-BCPs architectures is experimentally or computationally intractable. In this work, we use molecular dynamics (MD) simulations coupled with reinforcement learning based Monte Carlo tree search (MCTS) to identify s-BCPs designs that minimize the interfacial tension between polar and non-polar solvents. We first validate the MCTS approach for design of small- and medium-sized s-BCPs, and then use it to efficiently identify sequences of copolymer blocks for large-sized s-BCPs. The structural origins of interfacial tension in these systems are also identified using the configurations obtained from MD simulations. Chemical insights on the arrangement of copolymer blocks that promote lower interfacial tension were mined using machine learning (ML) techniques. Overall, this work provides an efficient approach to solve design problems via fusion of simulations and ML and provide important groundwork for future experimental investigation of s-BCPs sequences for various applications.

cond-mat.soft↗

arXiv2023

Nanoscale imaging of He-ion irradiation effects on amorphous TaO$_x$ toward electroforming-free neuromorphic functions

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 amorphous TaO$_x$ as a model material system. Specifically, we apply Scanning Microwave Impedance Microscopy (sMIM) and cathodoluminescence (CL) microscopy as direct probes of conductance and electronic structure, respectively. These methods provide direct evidence of the electronic state of TaO$_x$ despite its amorphous nature. For example CL identifies characteristic impurity levels in TaO$_x$, in agreement with first principles calculations. We applied these methods to investigate He-ion-beam irradiation as a path to activate conductivity of materials and enable electroforming-free control over resistive switching. However, we find that even though He-ions begin to modify the nature of bonds even at the lowest doses, the films conductive properties exhibit remarkable stability with large displacement damage and they are driven to metallic states only at the limit of structural decomposition. Finally, we show that electroforming in a nanoscale junction can be carried out with a dissipated power of < 20 nW, a much smaller value compared to earlier studies and one that minimizes irreversible structural modifications of the films. The multimodal approach described here provides a new framework toward the theory/experiment guided design and optimization of electroresistive materials.

cond-mat.mtrl-sci↗

arXiv2023

Scale-free switching of polarization in the layered ferroelectric material CuInP$_2$S$_6$

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. Previously, a coherent switching of polarization via a quadruple-well potential was proposed for these materials. In the super-cells we considered, we find multiple structures with similar energies but with different local polar order. Our results suggest that the individual dipoles are weakly coupled in-plane and under an electric field at very low temperatures these dipoles in CIPS should undergo incoherent disordered switching. The barrier for switching is determined by the single Cu-ion switching barrier. This in turn suggests a scale-free polarization with a switching barrier of $\sim$ 203.6-258.0 meV, a factor of five smaller than that of HfO$_2$ (1380 meV) a prototypical scale-free ferroelectric. The mechanism of polarization switching in CIPS is mediated by the switching of each weakly interacting dipole rather than the macroscopic polarization itself as previously hypothesized. These findings reconcile prior observations of a quadruple well with sloping hysteresis loops, large ionic conductivity even at 250~K well below the Curie temperature (315~K), and a significant wake-up effects where the macroscopic polarization is slow to order and set-in under an applied electric field. We also find that computed piezoelectric response and the polarization show a linear dependence on the local dipolar order. This is consistent with having scale-free polarization and other polarization-dependent properties and opens doors for engineering tunable metastability by-design in CIPS (and related family of materials) for neuromorphic applications.

cond-mat.mtrl-sci↗

arXiv2023

Bridging Theory with Experiment: Digital Twins and Deep Learning Segmentation of Defects in Monolayer MX2 Phases

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. However, effective identification and quantification of defects remains a challenge even as high-throughput scanning tunneling electron microscopy (STEM) methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. This digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings, and potentially chart paths forward for automated discovery of materials defect phases in experiments.

cond-mat.mtrl-sci↗

arXiv2022

A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials

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 are considerably faster than density functional theory (DFT) calculations with reasonable accuracy and show the potential that GNNs are able to capture a functional form for energy predictions. To test this strategy, we developed a DFT dataset of 508 $E_{vac}$ consisting of 3D elemental solids, alloys, oxides, nitrides, and 2D monolayer materials. We analyzed and discussed the applicability of such direct and fast predictions. We applied the model to predict 192494 $E_{vac}$ for 55723 materials in the JARVIS-DFT database.

cond-mat.mtrl-sci↗