arXiv subjects

Michael E. Wall

Publications and source records attributed to Michael E. Wall.

18 recordsLinked to original sources

arXiv2026

Modeling Reactions on the Solid-Liquid Interface With Next Generation Extended Lagrangian Quantum-Based Molecular Dynamics

We present a series of simulations of the oxygen reduction reaction (ORR) using a novel framework for atomistic simulations of surface catalysis under electrochemical bias. The framework makes use of quantum-mechanical extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) simulations, which provide the speed and accuracy required for explicit atomistic treatment of both electrode and electrolyte. Simulations of solvated O$_2$ near nitrogen-doped graphene (NG) were performed to gain insight into the ORR, and different mechanisms were observed, depending on the applied bias. Under higher bias the ORR occurred by an outer-sphere mechanism, without adsorption of O$_2$ to NG. In this mechanism, electron transfer between the catalyst and the O$_2$ was mediated by the solvent. Under lower bias the ORR occurred by an inner-sphere mechanism involving adsorption of O$_2$ to NG, leading to direct electron transfer. Our extensive, all-atom quantum-mechanical molecular dynamics simulations also show clear differences between the kinetics of the ORR on this ideally polarizable electrode and commonly used kinetic theories, leading to new insights regarding mechanistic changes with varied overpotentials. Combining quantum accuracy with explicit solvation and electrostatic potential bias, XL-BOMD opens a route to predictive, atomistic insight into electrocatalytic processes, as demonstrated with the ORR.

physics.chem-ph↗

arXiv2026

SEDACS: A Scalable Framework for Complex Chemistry Simulations

Graph-based linear-scaling electronic-structure theory provides a scalable framework for parallel quantum-mechanical molecular dynamics (QMD) simulations by exploiting the nearsightedness of the non-local electronic connectivity in non-metallic systems. When combined with recent shadow molecular dynamics in an extended-Lagrangian formulation, it enables stable long-time simulations of large, chemically active systems. This article introduces the Scalable Ecosystem, Driver, and Analyzer for Complex Chemistry Simulations (SEDACS), which integrates all these advances within a modular, Python-based software package for large-scale QMD simulations driven by external electronic-structure codes. SEDACS provides a tunable, adaptive graph construction in which edges encode the non-local electronic overlap between atoms. This graph is then decomposed into a set of smaller, overlapping subgraphs, where the electronic structure of each of these subgraphs is solved for independently and in parallel using an external electronic-structure code. SEDACS can be coupled to a variety of external electronic-structure solvers with minimal modifications to their software, enabling rapid adoption of the graph-based QMD approach. In this way, SEDACS can greatly extend the capability of existing electronic-structure packages by enabling stable QMD simulations of systems that were previously computationally inaccessible. We demonstrate highly efficient and stable QMD simulations for chemically active systems with tens of thousands of atoms by interfacing SEDACS with an external Fortran-based electronic-structure code based on self-consistent-charge density functional tight-binding theory.

physics.chem-ph↗

arXiv2025

Shadow Molecular Dynamics for Flexible Multipole Models

Shadow molecular dynamics provide an efficient and stable atomistic simulation framework for flexible charge models with long-range electrostatic interactions. While previous implementations have been limited to atomic monopole charge distributions, we extend this approach to flexible multipole models. We derive detailed expressions for the shadow energy functions, potentials, and force terms, explicitly incorporating monopole-monopole, dipole-monopole, and dipole-dipole interactions. In our formulation, both atomic monopoles and atomic dipoles are treated as extended dynamical variables alongside the propagation of the nuclear degrees of freedom. We demonstrate that introducing the additional dipole degrees of freedom preserves the stability and accuracy previously seen in monopole-only shadow molecular dynamics simulations. Additionally, we present a shadow molecular dynamics scheme where the monopole charges are held fixed while the dipoles remain flexible. Our extended shadow dynamics provide a framework for stable, computationally efficient, and versatile molecular dynamics simulations involving long-range interactions between flexible multipoles. This is of particular interest in combination with modern artificial intelligence and machine learning techniques, which are increasingly used to develop physics-informed and data-driven foundation models for atomistic simulations. These models aim to provide transferable, high-accuracy representations of atomic interactions that are applicable across diverse sets of molecular systems, which requires accurate treatment of long-range charge interactions.

physics.chem-ph↗

arXiv2025

Structure-Based Experimental Datasets for Benchmarking Protein Simulation Force Fields

This review article provides an overview of structurally oriented experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear magnetic resonance (NMR) spectroscopy and room temperature (RT) protein crystallography. We discuss what the observables are, what they tell us about structure and dynamics, what makes them useful for assessing force field accuracy, and how they can be connected to molecular dynamics simulations carried out using the force field one wishes to benchmark. We also touch on statistical issues that arise when comparing simulations with experiment. We hope this article will be particularly useful to computational researchers and trainees who develop, benchmark, or use protein force fields for molecular simulations.

q-bio.BM↗

arXiv2024

Hybrid programming-model strategies for GPU offloading of electronic structure calculation kernels

To address the challenge of performance portability, and facilitate the implementation of electronic structure solvers, we developed the Basic Matrix Library (BML) and Parallel, Rapid O(N) and Graph-based Recursive Electronic Structure Solver (PROGRESS) libraries. BML implements linear algebra operations necessary for electronic structure kernels using a unified user interface for various matrix formats (dense, sparse) and architectures (CPUs, GPUs). Focusing on Density Functional Theory (DFT) and Tight-Binding (TB) models, PROGRESS implements several solvers for computing the single-particle density matrix and relies on BML. In this paper, we describe the general strategies used for these implementations on various computer architectures, using OpenMP target functionalities on GPUs, in conjunction with third-party libraries to handle performance critical numerical kernels. We demonstrate the portability of this approach and its performance on benchmark problems.

physics.comp-ph↗

arXiv2022

Graph-based Quantum Response Theory and Shadow Born-Oppenheimer Molecular Dynamics

Graph-based linear scaling electronic structure theory for quantum-mechanical molecular dynamics simulations is adapted to the most recent shadow potential formulations of extended Lagrangian Born-Oppenheimer molecular dynamics, including fractional molecular-orbital occupation numbers, which enables stable simulations of sensitive complex chemical systems with unsteady charge solutions. The proposed formulation includes a preconditioned Krylov subspace approximation for the integration of the extended electronic degrees of freedom, which requires quantum response calculations for electronic states with fractional occupation numbers. For the response calculations we introduce a graph-based canonical quantum perturbation theory that can be performed with the same natural parallelism and linear scaling complexity as the graph-based electronic structure calculations for the unperturbed ground state. The proposed techniques are particularly well-suited for semi-empirical electronic structure theory and the methods are demonstrated using self-consistent charge density-functional tight-binding (SCC-DFTB) theory, both for the acceleration of self-consistent field calculations and for quantum molecular dynamics simulations. The graph-based techniques combined with the semi-empirical theory enable stable simulations of large, complex chemical systems, including tens-of-thousands of atoms.

physics.chem-ph↗

arXiv2022

Accelerating X-Ray Tracing for Exascale Systems using Kokkos

The upcoming exascale computing systems Frontier and Aurora will draw much of their computing power from GPU accelerators. The hardware for these systems will be provided by AMD and Intel, respectively, each supporting their own GPU programming model. The challenge for applications that harness one of these exascale systems will be to avoid lock-in and to preserve performance portability. We report here on our results of using Kokkos to accelerate a real-world application on NERSC's Perlmutter Phase 1 (using NVIDIA A100 accelerators) and the testbed system for OLCF's Frontier (using AMD MI250X). By porting to Kokkos, we were able to successfully run the same X-ray tracing code on both systems and achieved speed-ups between 13% and 66% compared to the original CUDA code. These results are a highly encouraging demonstration of using Kokkos to accelerate production science code.

cs.DC↗

arXiv2021

Performance Optimizations of Recursive Electronic Structure Solvers targeting Multi-Core Architectures (LA-UR-20-26665)

As we rapidly approach the frontiers of ultra large computing resources, software optimization is becoming of paramount interest to scientific application developers interested in efficiently leveraging all available on-Node computing capabilities and thereby improving a requisite science per watt metric. The scientific application of interest here is the Basic Math Library (BML) that provides a singular interface for linear algebra operation frequently used in the Quantum Molecular Dynamics (QMD) community. The provisioning of a singular interface indicates the presence of an abstraction layer which in-turn suggests commonalities in the code-base and therefore any optimization or tuning introduced in the core of code-base has the ability to positively affect the performance of the aforementioned library as a whole. With that in mind, we proceed with this investigation by performing a survey of the entirety of the BML code-base, and extract, in form of micro-kernels, common snippets of code. We introduce several optimization strategies into these micro-kernels including 1.) Strength Reduction 2.) Memory Alignment for large arrays 3.) Non Uniform Memory Access (NUMA) aware allocations to enforce data locality and 4.) appropriate thread affinity and bindings to enhance the overall multi-threaded performance. After introducing these optimizations, we benchmark the micro-kernels and compare the run-time before and after optimization for several target architectures. Finally we use the results as a guide to propagating the optimization strategies into the BML code-base. As a demonstration, herein, we test the efficacy of these optimization strategies by comparing the benchmark and optimized versions of the code.

cs.PF↗

arXiv2016

Quantum crystallographic charge density of urea

Standard X-ray crystallography methods use free-atom models to calculate mean unit cell charge densities. Real molecules, however, have shared charge that is not captured accurately using free-atom models. To address this limitation, a charge density model of crystalline urea was calculated using high-level quantum theory and was refined against publicly available ultra high-resolution experimental Bragg data, including the effects of atomic displacement parameters. The resulting quantum crystallographic model was compared to models obtained using spherical atom or multipole methods. Despite using only the same number of free parameters as the spherical atom model, the agreement of the quantum model with the data is comparable to the multipole model. The static, theoretical crystalline charge density of the quantum model is distinct from the multipole model, indicating the quantum model provides substantially new information. Hydrogen thermal ellipsoids in the quantum model were very similar to those obtained using neutron crystallography, indicating that quantum crystallography can increase the accuracy of the X-ray crystallographic atomic displacement parameters. The results demonstrate the feasibility and benefits of integrating fully periodic quantum charge density calculations into ultra high-resolution X-ray crystallographic model building and refinement.

cond-mat.mtrl-sci↗

arXiv2016

Graph-based linear scaling electronic structure theory

We show how graph theory can be combined with quantum theory to calculate the electronic structure of large complex systems. The graph formalism is general and applicable to a broad range of electronic structure methods and materials, including challenging systems such as biomolecules. The methodology combines well-controlled accuracy, low computational cost, and natural low-communication parallelism. This combination addresses substantial shortcomings of linear scaling electronic structure theory, in particular with respect to quantum-based molecular dynamics simulations.

physics.comp-ph↗

arXiv2015

Of fishes and birthdays: Efficient estimation of polymer configurational entropies

We present an algorithm to estimate the configurational entropy $S$ of a polymer. The algorithm uses the statistics of coincidences among random samples of configurations and is related to the catch-tag-release method for estimation of population sizes, and to the classic "birthday paradox". Bias in the entropy estimation is decreased by grouping configurations in nearly equiprobable partitions based on their energies, and estimating entropies separately within each partition. Whereas most entropy estimation algorithms require $N\sim 2^{S}$ samples to achieve small bias, our approach typically needs only $N\sim \sqrt{2^{S}}$. Thus the algorithm can be applied to estimate protein free energies with increased accuracy and decreased computational cost.

q-bio.BM↗

arXiv2009

Model of Transcriptional Activation by MarA in Escherichia coli

We have developed a mathematical model of transcriptional activation by MarA in Escherichia coli, and used the model to analyze measurements of MarA-dependent activity of the marRAB, sodA, and micF promoters in mar-rob- cells. The model rationalizes an unexpected poor correlation between the mid-point of in vivo promoter activity profiles and in vitro equilibrium constants for MarA binding to promoter sequences. Analysis of the promoter activity data using the model yielded the following predictions regarding activation mechanisms: (1) MarA activation of the marRAB, sodA, and micF promoters involves a net acceleration of the kinetics of transitions after RNA polymerase binding, up to and including promoter escape and message elongation; (2) RNA polymerase binds to these promoters with nearly unit occupancy in the absence of MarA, making recruitment of polymerase an insignificant factor in activation of these promoters; and (3) instead of recruitment, activation of the micF promoter might involve a repulsion of polymerase combined with a large acceleration of the kinetics of polymerase activity. These predictions are consistent with published chromatin immunoprecipitation assays of interactions between polymerase and the E. coli chromosome. A lack of recruitment in transcriptional activation represents an exception to the textbook description of activation of bacterial sigma-70 promoters. However, use of accelerated polymerase kinetics instead of recruitment might confer a competitive advantage to E. coli by decreasing latency in gene regulation.

q-bio.MN↗

arXiv2008

Determinants of bistability in induction of the Escherichia coli lac operon

We have developed a mathematical model of regulation of expression of the Escherichia coli lac operon, and have investigated bistability in its steady-state induction behavior in the absence of external glucose. Numerical analysis of equations describing regulation by artificial inducers revealed two natural bistability parameters that can be used to control the range of inducer concentrations over which the model exhibits bistability. By tuning these bistability parameters, we found a family of biophysically reasonable systems that are consistent with an experimentally determined bistable region for induction by thio-methylgalactoside (Ozbudak et al. Nature 427:737, 2004). The model predicts that bistability can be abolished when passive transport or permease export becomes sufficiently large; the former case is especially relevant to induction by isopropyl-beta, D-thiogalactopyranoside. To model regulation by lactose, we developed similar equations in which allolactose, a metabolic intermediate in lactose metabolism and a natural inducer of lac, is the inducer. For biophysically reasonable parameter values, these equations yield no bistability in response to induction by lactose; however, systems with an unphysically small permease-dependent export effect can exhibit small amounts of bistability for limited ranges of parameter values. These results cast doubt on the relevance of bistability in the lac operon within the natural context of E. coli, and help shed light on the controversy among existing theoretical studies that address this issue. The results also suggest an experimental approach to address the relevance of bistability in the lac operon within the natural context of E. coli.

q-bio.CB↗

arXiv2007

Hidden Structure in Protein Energy Landscapes

Inherent structure theory is used to discover strong connections between simple characteristics of protein structure and the energy landscape of a Go model. The potential energies and vibrational free energies of inherent structures are highly correlated, and both reflect simple measures of networks of native contacts. These connections have important consequences for models of protein dynamics and thermodynamics.

q-bio.BM↗

arXiv2007

Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism

We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For this, we generate synthetic metabolic profiles for benchmarking purposes based on a well-established model for red blood cell metabolism. A variety of data sets is generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We apply ARACNE, a mainstream transcriptional networks reverse engineering algorithm, to these data sets and observe performance comparable to that obtained in the transcriptional domain, for which the algorithm was originally designed.

q-bio.MN↗

arXiv2006

Ligand Binding, Protein Fluctuations, and Allosteric Free Energy

Although the importance of protein dynamics in protein function is generally recognized, the role of protein fluctuations in allosteric effects scarcely has been considered. To address this gap, the Kullback-Leibler divergence (Dx) between protein conformational distributions before and after ligand binding was proposed as a means of quantifying allosteric effects in proteins. Here, previous applications of Dx to methods for analysis and simulation of proteins are first reviewed, and their implications for understanding aspects of protein function and protein evolution are discussed. Next, equations for Dx suggest that k_{B}TDx should be interpreted as an allosteric free energy -- the free energy associated with changing the ligand-free protein conformational distribution to the ligand-bound conformational distribution. This interpretation leads to a thermodynamic model of allosteric transitions that unifies existing perspectives on the relation between ligand binding and changes in protein conformational distributions. The definition of Dx is used to explore some interesting mathematical relations among commonly recognized thermodynamic and biophysical quantities, such as the total free energy change upon ligand binding, and ligand-binding affinities for individual protein conformations. These results represent the beginnings of a theoretical framework for considering the full protein conformational distribution in modeling allosteric transitions. Early applications of the framework have produced results with implications both for methods for coarsed-grained modeling of proteins, and for understanding the relation between ligand binding and protein dynamics.

q-bio.BM↗

arXiv2005

Robustness in Gene Circuits: Clustering of Functional Responses

In contrast to engineering applications, in which the structure of control laws are designed to satisfy prescribed function requirements, in biology it is often necessary to infer gene-circuit function from incomplete data on gene-circuit structure. By using the feed-forward loop as a model system, this paper introduces a technique for classifying gene-circuit function given gene-circuit structure. In simulations performed on a comprehensive set of models that span a broad range of parameter space, some designs are robust, producing one unique type of functional response regardless of parameter selection. Other designs may exhibit a variety of functional responses, depending upon parameter values. We conclude that, although some feed-forward loop models have designs that lend themselves to unique function inference, others have designs for which the function type may be uncertain.

q-bio.MN↗

arXiv2003

Singular Value Decomposition and Principal Component Analysis

This chapter describes gene expression analysis by Singular Value Decomposition (SVD), emphasizing initial characterization of the data. We describe SVD methods for visualization of gene expression data, representation of the data using a smaller number of variables, and detection of patterns in noisy gene expression data. In addition, we describe the precise relation between SVD analysis and Principal Component Analysis (PCA) when PCA is calculated using the covariance matrix, enabling our descriptions to apply equally well to either method. Our aim is to provide definitions, interpretations, examples, and references that will serve as resources for understanding and extending the application of SVD and PCA to gene expression analysis.

physics.bio-ph↗