Application of Strain Functionals for Physics Informed Machine Learning
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This project aims to understand the critical factors that determine transduction performance of topological phonons across an oxide perovskite/tungsten diselenide heterojunction. We will investigate mechanisms of their propagation and interfacial coupling using modeling and machine learning approaches. Methods include density functional theory, molecular dynamics, numerical transport simulations, and active learning for building up a training dataset for force field development.
In the first paper of this series, the authors derived an expression for the interaction energy between two reagents in terms of the chemical reactivity indicators that can be derived from density functional perturbation theory. While negative interaction energies can explain reactivity, reactivity is often more simply explained using the “|dμ| big is good” rule or the maximum hardness principle. Expressions for the change in chemical potential (μ) and hardness when two reagents interact are derived. A partial justification for the maximum hardness principle is that the terms that appear in the interaction energy expression often reappear in the expression for the interaction hardness, but with opposite sign.
Three clinically relevant ebolaviruses – Ebola (EBOV), Bundibugyo (BDBV), and Sudan (SUDV) viruses, are responsible for severe disease and occasional deadly outbreaks in Africa. The largest Ebola virus disease (EVD) epidemic to date in 2013-2016 in West Africa highlighted the urgent need for countermeasures, leading to the development and FDA approval of the Ebola virus vaccine rVSV-ZEBOV (Ervebo ® ) in 2020 and two monoclonal antibody (mAb)-based therapeutics (Inmazeb ® [atoltivimab, maftivimab, and odesivimab-ebgn] and Ebanga ® (ansuvimab-zykl) in 2020. The humoral response plays an indispensable role in ebolavirus immunity, based on studies of mAbs isolated from the antibody genes in peripheral blood circulating ebolavirus-specific human memory B cells. However, antibodies in the body are not secreted by circulating memory B cells in the blood but rather principally by plasma cells in the bone marrow. Little is known about the protective polyclonal antibody responses in convalescent plasma. Here we exploited both single-cell antibody gene sequencing and proteomic sequencing approaches to assess the composition of the ebolavirus glycoprotein (GP)-reactive antibody repertoire in the plasma of an EVD survivor. We first identified 1,512 GP-specific mAb variable gene sequences from single cells in the memory B cell compartment. Using mass spectrometric analysis of the corresponding GP-specific plasma IgG, we found that only a portion of the large B cell antibody repertoire was represented in the plasma. Molecular and functional analysis of proteomics-identified mAbs revealed recognition of epitopes in three major antigenic sites - the GP head domain, the glycan cap, and the base region, with a high prevalence of neutralizing and protective mAb specificities that targeted the base and glycan cap regions on the GP. Polyclonal plasma antibodies from the survivor reacted broadly to EBOV, BDBV, and SUDV GP, while reactivity of the potently neutralizing mAbs we identified was limited mostly to the homologous EBOV GP. Together these results reveal a restricted diversity of neutralizing humoral response in which mAbs targeting two antigenic sites on GP – glycan cap and base – play a principal role in plasma-antibody-mediated protective immunity against EVD.
Fentanyl is one of the most common opioid analgesics administered to patients undergoing surgery or for chronic pain management. While the side effects of chronic fentanyl abuse are recognized (e.g., addiction, tolerance, impairment of cognitive functions, and inhibit nociception, arousal, and respiration), it remains poorly understood what and how changes in brain activity from chronic fentanyl use influences the respective behavioral outcome. Here, we examined the functional and molecular changes to cortical neural network activity following sub-chronic exposure to two fentanyl concentrations, a low (0.01 μM) and high (10 μM) dose. Primary rat co-cultures, containing cortical neurons, astrocytes, and oligodendrocyte precursor cells, were seeded in wells on either a 6-well multi-electrode array (MEA, for electrophysiology) or a 96-well tissue culture plate (for serial endpoint bulk RNA sequencing analysis). Once networks matured (at 28 days in vitro ), co-cultures were treated with 0.01 or 10 μM of fentanyl for 4 days and monitored daily. Only high dose exposure to fentanyl resulted in a decline in features of spiking and bursting activity as early as 30 min post-exposure and sustained for 4 days in cultures. Transcriptomic analysis of the complex cultures after 4 days of fentanyl exposure revealed that both the low and high dose induced gene expression changes involved in synaptic transmission, inflammation, and organization of the extracellular matrix. Collectively, the findings of this in vitro study suggest that while neuroadaptive changes to neural network activity at a systems level was detected only at the high dose of fentanyl, transcriptomic changes were also detected at the low dose conditions, suggesting that fentanyl rapidly elicits changes in plasticity.
3D photoacoustic computed tomography (3D-PACT) has made great advances in volumetric imaging of biological tissues, with high spatial-temporal resolutions and large penetration depth. The development of 3D-PACT requires high-performance acoustic sensors with a small size, large detection bandwidth, and high sensitivity. In this work, we present a new high-frequency 3D-PACT system that uses a microring resonator (MRR) as the acoustic sensor. The MRR sensor has a size of 80 μ m in diameter and was fabricated using the nanoimprint lithography technology. Using the MRR sensor, we have developed a transmission-mode 3D-PACT system that has achieved a detection bandwidth of ~23 MHz, an imaging depth of ~8 mm, a lateral resolution of 114 μ m, and an axial resolution of 57 μ m. We have demonstrated the 3D PACT’s performance on in vitro phantoms, ex vivo mouse brain, and in vivo mouse ear and tadpole. The MRR-based 3D-PACT system can be a promising tool for structural, functional, and molecular imaging of biological tissues at depths.
Eukaryotes possess eight highly conserved Lsm (like Sm) proteins that assemble into circular, heteroheptameric complexes, bind RNA, and direct a diverse range of biological processes. Among the many essential functions of Lsm proteins, the cytoplasmic Lsm1–7 complex initiates mRNA decay, while the nuclear Lsm2–8 complex acts as a chaperone for U6 spliceosomal RNA. It has been unclear how these complexes perform their distinct functions while differing by only one out of seven subunits. Here, we elucidate the molecular basis for Lsm-RNA recognition and present four high-resolution structures of Lsm complexes bound to RNAs. The structures of Lsm2–8 bound to RNA identify the unique 2',3' cyclic phosphate end of U6 as a prime determinant of specificity. In contrast, the Lsm1–7 complex strongly discriminates against cyclic phosphates and tightly binds to oligouridylate tracts with terminal purines. Lsm5 uniquely recognizes purine bases, explaining its divergent sequence relative to other Lsm subunits. Lsm1–7 loads onto RNA from the 3' end and removal of the Lsm1 carboxy-terminal region allows Lsm1–7 to scan along RNA, suggesting a gated mechanism for accessing internal binding sites. These data reveal the molecular basis for RNA binding by Lsm proteins, a fundamental step in the formation of molecular assemblies that are central to eukaryotic mRNA metabolism.
Articular cartilage is a connective tissue lining the surfaces of synovial joints. When the cartilage severely wears down, it leads to osteoarthritis (OA), a debilitating disease that affects millions of people globally. The articular cartilage is composed of a dense extracellular matrix (ECM) with a sparse distribution of chondrocytes with varying morphology and potentially different functions. Elucidating the molecular and functional profiles of various chondrocyte subtypes and understanding the interplay between these chondrocyte subtypes and other cell types in the joint will greatly expand our understanding of joint biology and OA pathology. Although recent advances in high-throughput OMICS technologies have enabled molecular-level characterization of tissues and organs at an unprecedented resolution, thorough molecular profiling of articular chondrocytes has not yet been undertaken, which may be in part due to the technical difficulties in isolating chondrocytes from dense cartilage ECM. In this study, we profiled articular cartilage from healthy and injured mouse knee joints at a single-cell resolution and identified nine chondrocyte subtypes with distinct molecular profiles and injury-induced early molecular changes in these chondrocytes. We also compared mouse chondrocyte subpopulations to human chondrocytes and evaluated the extent of molecular similarity between mice and humans. This work expands our view of chondrocyte heterogeneity and rapid molecular changes in chondrocyte populations in response to joint trauma and highlights potential mechanisms that trigger cartilage degeneration.
The implementation of fast relativistic methods based on density functional theory, in conjunction with localized molecular orbital (LMO) based analysis, allows straightforward interpretations of NMR parameters in terms of contributions from core shells, lone pairs, and bonds, for compounds containing elements from across the periodic table. We present a conceptual review of a frequently used LMO analysis of NMR parameters calculated in the presence of spin-orbit interactions and other relativistic effects. An accompanying example focuses on the 15 N shielding in a heavy metal complex.
We have developed a density functional tight-binding model for hydrocarbon excited state dynamics by referencing to high-level electronic structure theory and incorporating a many-body repulsive energy. We then validate our model against n-octane geometry optimizations, bond dissociation scans, and vibrational frequencies. Our model is approximately 10 3 times more efficient than hybrid time-dependent density functional theory calculations with comparable accuracy. In conclusion, our efforts enable longer timescale excited state simulations of photochemistry and scattering of incident radiation.
Many eukaryotic transcription factors (TF) form homodimer or heterodimer complexes to regulate gene expression. Dimerization of BASIC LEUCINE ZIPPER (bZIP) TFs are critical for their functions, but the molecular mechanism underlying the DNA binding and functional specificity of homo- versus heterodimers remains elusive. To address this gap, we present the double DNA Affinity Purification-sequencing (dDAP-seq) technique that maps heterodimer binding sites on endogenous genomic DNA. Using dDAP-seq we profile twenty pairs of C/S1 bZIP heterodimers and S1 homodimers in Arabidopsis and show that heterodimerization significantly expands the DNA binding preferences of these TFs. Analysis of dDAP-seq binding sites reveals the function of bZIP9 in abscisic acid response and the role of bZIP53 heterodimer-specific binding in seed maturation. The C/S1 heterodimers show distinct preferences for the ACGT elements recognized by plant bZIPs and motifs resembling the yeast GCN4 cis -elements. This study demonstrates the potential of dDAP-seq in deciphering the DNA binding specificities of interacting TFs that are key for combinatorial gene regulation.
Molecular genetic understanding of flowering time regulation is crucial for sorghum development. GRAIN NUMBER, PLANT HEIGHT AND HEADING DATE 7 (SbGhd7) is one of the six classical loci conferring photoperiod sensitivity of sorghum flowering. However, its functions remain poorly studied. The molecular functions of SbGhd7 were characterized. The gene regulatory network controlled by SbGhd7 was constructed and validated. The biological roles of SbGhd7 and its major targets were studied. SbGhd7overexpression (OE) completely prevented sorghum flowering. Additionally, we show thatSbGhd7is a major negative regulator of flowering, binding to the promoter motif TGAATG(A/T)(A/T/C) and repressing transcription of the major florigen FLOWERING LOCUS T 10(SbFT10) and floral activators EARLY HEADING DATE(SbEhd1),FLAVIN-BINDING,KELCH REPEAT,F-BOX1(SbFKF1) and EARLY FLOWERING 3(SbELF3). Reinforcing the direct effect ofSbGhd7,SbEhd1OE activated the promoters of three functional florigens (SbFT1,SbFT8andSbFT10), dramatically accelerating flowering. Here our studies demonstrate that SbGhd7 is a major repressor of sorghum flowering by directly and indirectly targeting genes for flowering activation. The mechanism appears ancient. Our study extends the current model of floral transition regulation in sorghum and provides a framework for a comprehensive understanding of sorghum photoperiod response.
Nonadiabatic excited-state molecular dynamics underpin many photophysical and photochemical phenomena, such as exciton dynamics, charge separation and transport. In this work, we present an efficient nonadiabatic molecular dynamic (NAMD) simulation method based on time-dependent density functional tight-binding (TDDFTB) theory. Specifically, the adiabatic electronic structure, an essential NAMD input, is described at the TDDFTB level. Here, the nonadiabatic effects originating from the coupled motions of electrons and nuclei are treated by the trajectory surface hopping algorithm. To improve the computational efficiency, nonadiabatic couplings between excited states within the TDDFTB method are derived and implemented using an analytical approach. Further, the time-dependent nonadiabatic coupling scalars are calculated based on the overlap between molecular orbitals rather than the Slater determinants to speed up the simulations. In addition, the electronic decoherence scheme and a state reassigned unavoided crossings algorithm, which has been implemented in the NEXMD software, are used to improve the accuracy of the simulated dynamics and handle trivial unavoided crossings. Finally, the photoinduced nonadiabatic dynamics of a benzene molecule are simulated to demonstrate our implementation. The results for excited state NAMD simulations of benzene molecule based on TDDFTB method compare well that obtained with numerically expensive time-dependent density functional theory. The proposed methodology provides an attractive theoretical simulation tool for predicting the photophysical and photochemical properties of complex materials.
We report extended Lagrangian Born–Oppenheimer molecular dynamics (XL-BOMD) is formulated for orbital-free Hohenberg–Kohn density-functional theory and for charge equilibration and polarizable force-field models that can be derived from the same orbital-free framework. The purpose is to introduce the most recent features of orbital-based XL-BOMD to molecular dynamics simulations based on charge equilibration and polarizable force-field models. These features include a metric tensor generalization of the extended harmonic potential, preconditioners, and the ability to use only a single Coulomb summation to determine the fully equilibrated charges and the interatomic forces in each time step for the shadow Born–Oppenheimer potential energy surface. The orbital-free formulation has a charge-dependent, short-range energy term that is separate from long-range Coulomb interactions. This enables local parameterizations of the short-range energy term, while the long-range electrostatic interactions can be treated separately. The theory is illustrated for molecular dynamics simulations of an atomistic system described by a charge equilibration model with periodic boundary conditions. The system of linear equations that determines the equilibrated charges and the forces is diagonal, and only a single Ewald summation is needed in each time step. The simulations exhibit the same features in accuracy, convergence, and stability as are expected from orbital-based XL-BOMD.
The velocity autocorrelation function (VACF) encapsulates extensive information about a fluid’s molecular-structural and hydrodynamic properties. We address the following fundamental question: How well can a purely hydrodynamic description recover the molecular features of a fluid as exhibited by the VACF? To this end, we formulate a bona fide hydrodynamic theory of the tagged-particle VACF for simple fluids. Our approach is distinguished from previous efforts in two key ways: collective hydrodynamic modes and tagged-particle self-motion are modeled by linear hydrodynamic equations; the fluid’s spatial velocity power spectrum is identified as a necessary initial condition for the momentum current correlation. This formulation leads to a natural physical interpretation of the VACF as a superposition of products of quasinormal hydrodynamic modes weighted commensurately with the spatial velocity power spectrum, the latter of which appears to physically bridge continuum hydrodynamical behavior and discrete-particle kinetics. The methodology yields VACF calculations quantitatively on par with existing approaches for liquid noble gases and alkali metals. Furthermore, we obtain a new, hydrodynamic form of the self-intermediate scattering function whose description has been extended to low densities where the Schmidt number is of order unity; various calculations are performed for gaseous and supercritical argon to support the general validity of the theory. Excellent quantitative agreement is obtained with recent MD calculations for a dense supercritical Lennard-Jones fluid.
This review spotlights the role of atomic-level modeling in research on metal-organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster-based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high-throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research.
We present a systematic investigation of particle-number fluctuations in the crossover region near the critical end point of a first-order phase transition using molecular dynamics simulations of the classical Lennard-Jones fluid. We extend our prior studies to third- and fourth-order cumulants in both coordinate- and momentum-space acceptances and integrated correlation functions (factorial cumulants). We find that, even near the critical point, non-Gaussian cumulants equilibrate on timescales comparable to those of the second-order cumulants, but show stronger finite-size effects. The presence of interactions and of the critical point leads to strong deviations of the cumulants from the ideal-gas baseline in coordinate space; these deviations are expected to persist in momentum space in the presence of collective expansion. In particular, the kurtosis becomes strongly negative, κσ 2 ≪ − 1 , on the crossover side of the critical point. However, this signal is significantly diluted once an efficiency cut used to distinguish protons from baryons is applied, leading to |κσ 2 | ≲ 1 even in the presence of the critical point. We discuss our results in the context of ongoing measurements of proton-number cumulants in heavy-ion collisions in RHIC-BES-II.
Eleven 2,2'-bipyridine (bpy) ligands functionalized with attachment groups for covalent immobilization on silicon surfaces were prepared. Five of the ligands feature silatrane functional groups for attachment to metal oxide coatings on the silicon surfaces, while six contain either alkene or alkyne functional groups for attachment to hydrogen-terminated silicon surfaces. The bpy ligands were coordinated to Re(CO) 5 Cl to form complexes of the type Re(bpy)(CO) 3 Cl, which are related to known catalysts for CO 2 reduction. Six of the new complexes were characterized using X-ray crystallography. As proof of principle, four molecular Re complexes were immobilized on either a thin layer of TiO 2 on silicon or hydrogen-terminated silicon. The surface-immobilized complexes were characterized using X-ray photoelectron spectroscopy, IR spectroscopy, and cyclic voltammetry (CV) in the dark and for one representative example in the light. The CO stretching frequencies of the attached complexes were similar to those of the pure molecular complexes, but the CVs were less analogous. For two of the complexes, comparison of the electrocatalytic CO 2 reduction performance showed lower CO Faradaic efficiencies for the immobilized complexes than the same complex in solution under similar conditions. In particular, a complex containing a silatrane linked to bpy with an amide linker showed poor catalytic performance and control experiments suggest that amide linkers in conjugation with a redox-active ligand are not stable under highly reducing conditions and alkyl linkers are more stable. So a conclusion of this work is that understanding the behavior of molecular Re catalysts attached to semiconducting silicon is more complicated than related complexes, which have previously been immobilized on metallic electrodes.