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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enhancing Biopreparedness through a Model System to Understand the Molecular Mechanisms that Lead to Pathogenesis and Disease Transmission: NW-BRaVE

The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. Our vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen-host relationships and host shifts. We will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions. To make sure the system is applicable to real-world problems, we will develop it in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to other viral-host interactions. Our overall objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts. These objectives address Focus Area 2 Reveal Molecular Interactions Across Biological Scales for Design of Targeted Interventions. Our powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration (Figure 1). We will build an integrated navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The project will develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens, successfully impacting the research community by revolutionizing abilities to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. We will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Method of information entropy for convergence assessment of molecular dynamics simulations

The lack of a reliable method to evaluate the convergence of molecular dynamics simulations has contributed to discrepancies in different areas of molecular dynamics. Here, the method of information entropy is introduced to molecular dynamics for stationarity assessment. The Shannon information entropy formalism is used to monitor the convergence of the atom motion to a steady state in a continuous spatial domain and is also used to assess the stationarity of calculated multidimensional fields such as the temperature field in a discrete spatial domain. It is demonstrated in this work that monitoring the information entropy of the atom position matrix provides a clear indicator of reaching steady state in radiation damage simulations, non-equilibrium molecular dynamics thermal conductivity computations, and simulations of Poiseuille and Couette flow in nanochannels. A main advantage of the present technique is that it is non-local and relies on fundamental quantities available in all molecular dynamics simulations. Unlike monitoring average temperature, the technique is applicable to simulations that conserve total energy such as reverse non-equilibrium molecular dynamics thermal conductivity computations and to simulations where energy dissipates through a boundary as in radiation damage simulations. The method is applied to simulations of iron using the Tersoff/ZBL splined potential, silicon using the Stillinger–Weber potential, and to Lennard–Jones fluid. Its applicability to both solids and fluids shows that the technique has potential for generalization to other areas in molecular dynamics.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Retrieval of the molecular orientation distribution from atom-pair angular distributions

Imaging laser-induced rotational dynamics is an important and active field due to its applications in capturing reactions in the molecular frame and in molecular imaging. Experimental measurement of the molecular orientation distribution, as a function of the Euler angles, has only been demonstrated for special cases when the detectable signal is generated along the molecular symmetry axis. Here we developed the general theory that maps the probability density distribution of the molecular orientation to the atom-pair angular distributions for nonlinear molecules. With the theory, the molecular orientation distribution can be retrieved from the measured atom-pair angular distribution, which we demonstrate experimentally using ultrafast electron diffractive imaging of impulsively aligned trifluoro-iodomethane molecules. Here, the retrieved molecular orientation distribution is in good agreement with direct numerical simulations of the time-dependent Schrodinger equation using the experimental conditions. Unlike the existing retrieval methods, the retrieval method does not require solving Schrodinger equation, works for any alignment method, and is in principle applicable to asymmetric top molecules.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Machine Learning Models for Binary Molecular Classification using VUV Absorption Spectra

Machine learning methods were combined with differential absorption spectroscopy measurements in the vacuum-ultraviolet region (5.167 – 9.920 eV) in order to develop predictive capabilities for inferring molecular structure from the spectra. Several types of species were analyzed and, for modeling purposes, were defined using a single classification: (1) alkane, (2) conjugation with oxygen (e.g. diacetyl, ethyl vinyl ether), (3) non-conjugated alkene (e.g. 1-butene, 1,4-cyclohexadiene), (4) oxygen-containing (e.g. 1-butanol, tetrahydrofuran), or (5) cyclic (e.g. cyclopentane, cyclohexanone). The latter molecular classification excluded cyclic ethers. Several modeling methods were employed in the analysis of 102 absorption spectra, 24 of which were measured for the first time. The primary objective was to identify suitable methods that enable accurate predictions of molecular structure classifications with minimized statistical uncertainties. Rather than identifying a single, unifying method to reliably predict molecular structure contributions to VUV absorption spectra, coordination is required among a particular method, the type of molecular structure detail (e.g. conjugation), and absorption region of interest. The latter is accomplished using a binning approach, wherein absorption regions of ~0.5 eV were utilized rather than the entire ~4.8 eV range. Photon energy binning enabled analysis of region-specific predictions of accuracy, precision, and recall. The outcome from the binning approach is that, rather than utilizing the entire spectrum, optimal determination of molecular structure using machine learning methods depends on the absorption region. Furthermore, the present work provides separate machine learning models for each molecular classification, which enables the identification of multi-functional species relevant to atmospheric chemistry and combustion chemistry, where isomer-resolved speciation is critical to understanding complex reaction networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Impact of ferromagnetic electrode length and thickness on Magnetic Tunnel Junction-Based Molecular Spintronic Devices (MTJMSD)

A knowledge gap exists about the impact of variation in length and thickness of ferromagnetic(FM) electrodes on molecular spintronics devices’ ability to manifest molecular coupling impact. Magnetic Tunnel Junction-Based Molecular Spintronic Devices (MTJMSDs) are potential candidates for inventing highly correlated materials and devices and investigating the fundamental science of organic spintronics. This paper reports our experi- mental observations providing the dramatic impact of variation in ferromagnetic electrode length and thickness on paramagnetic molecule-based MTJMSD. Room temperature transport studies were performed to investigate the effect of FM electrode thickness. On the other hand, magnetic force microscopy measurements were con- ducted to understand the effect of FM electrode length extending beyond the molecular junction area, i.e., the site where paramagnetic molecules bridged between two FM. In the strong molecular coupling regime, transport study suggested thickness variation caused ~1000 to million-fold differences in junction conductivity. MFM study revealed near-zero magnetic contrast for pillar-shaped MTJMSD without any extended FM electrode. However, MFM images showed a multitude of microscopic magnetic phases on cross junction shaped MTJMSD where FM electrodes extended beyond the junction area. To understand the intriguing experimental results, we conducted an in-depth theoretical study using Monte Carlo Simulation (MCS) approach. MCS study utilized a Heisenberg atomic model of cross junction shaped MTJMSD to gain insights about room temperature transport and MFM experimental observations of microscopic MTJMSD. To make this study applicable for a wide variety of MTJMSDs, we systematically studied the effect of variation in molecular coupling strength between magnetic molecules and ferromagnetic (FM) electrodes of various dimensions. Here, our theoretical results suggest 25 atoms thick FM electrode show very weak molecular coupling impact and of the same order of experimentally studied FM electrode dimensions showing weak effect. Also, MCS results agree with experimentally observed multiple magnetic phases on lengthy FM electrodes of an MTJMSD.

42 ENGINEERING↗
DOE OSTI2023

Shadow Molecular Dynamics and Atomic Cluster Expansions for Flexible Charge Models

Here, a shadow molecular dynamics scheme for flexible charge models is presented where the shadow Born–Oppenheimer potential is derived from a coarse-grained approximation of range-separated density functional theory. The interatomic potential, including the atomic electronegativities and the charge-independent short-range part of the potential and force terms, is modeled by the linear atomic cluster expansion (ACE), which provides a computationally efficient alternative to many machine learning methods. The shadow molecular dynamics scheme is based on extended Lagrangian (XL) Born–Oppenheimer molecular dynamics (BOMD). XL-BOMD provides stable dynamics while avoiding the costly computational overhead associated with solving an all-to-all system of equations, which normally is required to determine the relaxed electronic ground state prior to each force evaluation. To demonstrate the proposed shadow molecular dynamics scheme for flexible charge models using atomic cluster expansion, we emulate the dynamics generated from self-consistent charge density functional tight-binding (SCC-DFTB) theory using a second-order charge equilibration (QEq) model. The charge-independent potentials and electronegativities of the QEq model are trained for a supercell of uranium oxide (UO 2 ) and a molecular system of liquid water. The combined ACE+XL-QEq molecular dynamics simulations are stable over a wide range of temperatures both for the oxide and for the molecular systems and provide a precise sampling of the Born–Oppenheimer potential energy surfaces. Accurate ground Coulomb energies are produced by the ACE-based electronegativity model during an NVE simulation of UO 2 , predicted to be within 1 meV of those from SCC-DFTB on average during comparable simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Impact of direct exchange coupling via the insulator on the magnetic tunnel junction based molecular spintronics devices with competing molecule induced inter-electrode coupling

The magnetic tunnel junction (MTJ) based molecular spintronics device (MTJMSD) approach is suitable for mass production. This approach provides solutions to fabrication difficulties related to reliably connecting molecular device elements to the ferromagnets (FMs). To producing MTJMSD, the molecular channels are bridged across the insulator of an MTJ testbed with exposed side edges. In an MTJMSD, two FMs are simultaneously connected by an insulator film and the molecular channels along the exposed sides. In our prior experimental studies, we observed that molecules could produce strong coupling between ferromagnets in the presence of the competing coupling via an insulator. In this paper, our Monte Carlo Simulation (MCS) was used to study the impact of coupling variation via insulator (a.k.a. Ji) on the magnetic properties of an MTJMSD. We studied the effect of Ji while varying the molecule induced antiferromagnetic exchange coupling. The ferromagnetic or antiferromagnetic nature and magnitude of Ji determined the resultant effect. Antiferromagnetic Ji enhanced the pre-existing antiferromagnetic molecular coupling effect. Ferromagnetic Ji competed with the opposite nature of antiferromagnetic molecular coupling. Our MCS may help to understand the impact of insulator thickness and defects on the molecular spintronics device performance and design process.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Decomposition decreases molecular diversity and ecosystem similarity of soil organic matter

Soil organic matter (SOM) is comprised of a diverse array of reactive carbon molecules, including hydrophilic and hydrophobic compounds, that impact rates of SOM formation and persistence. Despite clear importance to ecosystem science, little is known about broad-scale controls on SOM diversity and variability in soil. Here, we show that microbial decomposition drives significant variability in the molecular richness and diversity of SOM between soil horizons and across a continental-scale gradient in climate and ecosystem type (arid shrubs, coniferous, deciduous, and mixed forests, grasslands, and tundra sedges). The molecular dissimilarity of SOM was strongly influenced by ecosystem type (hydrophilic compounds: 17%, P < 0.001; hydrophobic compounds: 10% P < 0.001) and soil horizon (hydrophilic compounds: 17%, P < 0.001; hydrophobic compounds: 21%, P < 0.001), as assessed using metabolomic analysis of hydrophilic and hydrophobic metabolites. While the proportion of shared molecular features was significantly higher in the litter layer than subsoil C horizons across ecosystems (12 times and 4 times higher for hydrophilic and hydrophobic compounds, respectively), the proportion of site-specific molecular features nearly doubled from the litter layer to the subsoil horizon, suggesting greater differentiation of compounds after microbial decomposition within each ecosystem. Together, these results suggest that microbial decomposition of plant litter leads to a decrease in SOM α-molecular diversity, yet an increase in β-molecular diversity across ecosystems. The degree of microbial degradation, determined by the position in the soil profile, exerts a greater control on SOM molecular diversity than environmental factors, such as soil texture, moisture, and ecosystem type.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

One thousand soils for molecular understanding of belowground carbon cycling

While significant progress has been made in understanding global carbon (C) cycling, the mechanisms regulating belowground C fluxes and storage are still uncertain. New molecular technologies have the power to elucidate these processes, yet we have no widespread standardized implementation of molecular techniques. To address this gap, we introduce the Molecular Observation Network (MONet), a decadal vision from the Environmental Molecular Sciences Laboratory (EMSL), to develop a national network for understanding the molecular composition, physical structure, and hydraulic and biological properties of soil and water. These data are essential for advancing the next generation of multiscale Earth systems models. In this paper, we discuss the 1000 Soils Pilot for MONet, including a description of standardized sampling materials and protocols and a use case to highlight the utility of molecular-level and microstructural measurements for assessing the impacts of wildfire on soil. While the 1000 Soils Pilot generated a plethora of data, we focus on assessments of soil organic matter (SOM) chemistry via Fourier-transform ion cyclotron resonance-mass spectrometry and microstructural properties via X-ray computed tomography to highlight the effects of recent fire history in forested ecosystems on belowground C cycling. We observed decreases in soil respiration, microbial biomass, and potential enzyme activity in soils with high frequency burns. Additionally, the nominal oxidation state of carbon in SOM increased with burn frequency in surface soils. This results in a quantifiable shift in the molecular signature of SOM and shows that wildfire may result in oxidation of SOM and structural changes to soil pore networks that persist into deeper soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Charge Transport Characteristics of Molecular Electronic Junctions Studied by Transition Voltage Spectroscopy

The field of molecular electronics is prompted by tremendous opportunities for using a single-molecule and molecular monolayers as active components in integrated circuits. Until now, a wide range of molecular devices exhibiting characteristic functions, such as diodes, transistors, switches, and memory, have been demonstrated. However, a full understanding of the crucial factors that affect charge transport through molecular electronic junctions should yet be accomplished. Remarkably, recent advances in transition voltage spectroscopy (TVS) elucidate that it can provide key quantities for probing the transport characteristics of the junctions, including, for example, the position of the frontier molecular orbital energy relative to the electrode Fermi level and the strength of the molecule–electrode interactions. These parameters are known to be highly associated with charge transport behaviors in molecular systems and can then be used in the design of molecule-based devices with rationally tuned electronic properties. This article highlights the fundamental principle of TVS and then demonstrates its major applications to study the charge transport properties of molecular electronic junctions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-resolution CARMA Observation of Molecular Gas in the North America and Pelican Nebulae

In this paper, we present the first results from a CARMA high-resolution {sup 12}CO(1-0), {sup 13}CO(1-0), and C{sup 18}O(1-0) molecular line survey of the North America and Pelican (NAP) Nebulae. CARMA observations have been combined with single-dish data from the Purple Mountain 13.7 m telescope, to add short spacings and to produce high-dynamic-range images. We find that the molecular gas is predominantly shaped by the W80 H ii bubble, driven by an O star. Several bright rims noted in the observation are probably remnant molecular clouds, heated and stripped by the massive star. Matching these rims in molecular lines and optical images, we construct a model of the three-dimensional structure of the NAP complex. Two groups of molecular clumps/filaments are on the near side of the bubble: one is being pushed toward us, whereas the other is moving toward the bubble. Another group is on the far side of the bubble, and moving away. The young stellar objects in the Gulf region reside in three different clusters, each hosted by a cloud from one of the three molecular clump groups. Although all gas content in the NAP is impacted by feedback from the central O star, some regions show no signs of star formation, while other areas clearly exhibit star formation activity. Additional molecular gas being carved by feedback includes cometary structures in the Pelican Head region, and the boomerang features at the boundary of the Gulf region. The results show that the NAP complex is an ideal place for the study of feedback effects on star formation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

The State of the Molecular Gas in Post-starburst Galaxies

The molecular gas in galaxies traces both the fuel for star formation and the processes that can enhance or suppress star formation. Observations of the molecular gas state can thus point to when and why galaxies stop forming stars. In this study, we present Atacama Large Millimeter/submillimeter Array observations of the molecular gas in galaxies evolving through the post-starburst phase. These galaxies have low current star formation rates (SFRs), regardless of the SFR tracer used, with recent starbursts ending within the last 600 Myr. We present CO (3–2) observations for three post-starburst galaxies, and dense gas HCN/HCO+/HNC (1–0) observations for six (four new) post-starburst galaxies. The post-starbursts have low excitation traced by the CO spectral-line energy distribution up to CO (3–2), more similar to early-type than starburst galaxies. The low excitation indicates that lower density rather than high temperatures may suppress star formation during the post-starburst phase. One galaxy displays a blueshifted outflow traced by CO (3–2). MaNGA observations show that the ionized gas velocity is disturbed relative to the stellar velocity field, with a blueshifted component aligned with the molecular gas outflow, suggestive of a multiphase outflow. Low ratios of HCO + /CO, indicating low fractions of dense molecular gas relative to the total molecular gas, are seen throughout post-starburst phase, except for the youngest post-starburst galaxy considered here. These observations indicate that the impact of any feedback or quenching processes may be limited to low excitation and weak outflows in the cold molecular gas during the post-starburst phase.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Molecular Weight Distribution Analysis of Pyrolysis Oils by Gel Permeation Chromatography

Comprehensive analysis of the molecular weight distribution of raw and catalytic fast pyrolysis oils derived from biomass remains a key technical hurdle to understanding oil quality as it relates to downstream use and multiple methods may be necessary to accurately represent all components present. Here, we report the molecular weight distribution metrics of fast pyrolysis (FP) and catalytic fast pyrolysis (CFP) oils as determined by gel permeation chromatography (GPC) combined with UV-diode array (UV), differential refractive index (RI), and multi-angle laser light scattering (MALS) detection. The measured molar mass distributions revealed that FP oil consisted of a higher proportion of larger products relative to the low molecular weight products contained in the CFP oil. GPC/RI and UV methods showed FP oil to have higher weight-average molecular weight (Mw) and number-average molecular weight (Mn) than CFP oil based on elution time. However, GPC/MALS, determined the two oils to have similar overall molecular weight distribution metrics (Mw and Mn) and yielded values significantly higher than those determined by RI and UV detectors relative to external standards. Overall, the use of a multiple detection GPC method could enable a more accurate comparison and determination of true molecular weight metrics of bio-oils.

BIOMASS FUELS↗
DOE OSTI2022

Dramatic effect of electrode type on tunnel junction based molecular spintronic devices

A new class of molecular spintronic devices can be fabricated by chemically bonding magnetic molecular channels to the electrodes of a prefabricated tunnel junction with exposed side edges. Prior experimental studies showed that the cyanide-bridged octametallic molecular cluster, [(pzTp)FeIII(CN)3]4[NiII(L)]4¬[O3SCF3]4 [(pzTp) = tetra(pyrazol-1-yl)borate; L = 1-S(acetyl)tris(pyrazolyl)decane] molecule impact depended on the type of metallic electrodes used in the tunnel junction testbed. Experimental magnetization and transport studies showed a dramatic difference in molecule response on tunnel junctions with different combinations of metallic electrodes. Transport via paramagnetic molecular channels on a tunnel junction involving paramagnetic and ferromagnetic metal electrodes was dramatically different than the suppressed current state observed on tunnel junctions involving two ferromagnetic electrodes. We conducted theoretical studies to understand the experimental data and explore a wide range of electrode materials on tunnel junction-based molecular spintronics devices (TJMSD). Here, we report a Monte Carlo simulation study that focuses on understanding the effect of electrodes on the magnetic and physical properties of TJMSD. A 3D Heisenberg model of cross-junction-shaped TJMSD was used for the simulation study. We studied the effects of ferromagnetic, paramagnetic, and antiferromagnetic electrode materials. This study provides insights for designing and understanding futuristic molecular spintronics devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Two-dimensional retrieval methods for ultrafast imaging of molecular structure using laser-induced electron diffraction

Molecular structural retrieval based on electron diffraction has been proposed to determine the atomic positions of molecules with sub-angstrom spatial and femtosecond temporal resolutions. Given its success on small molecular systems, in this work, we point out that the accuracy of structure retrieval is constrained by the availability of a wide range of experimental data in the momentum space in all molecular systems. To mitigate the limitations, for laser-induced electron diffraction, here we retrieve molecular structures using two-dimensional (energy and angle) electron momentum spectra in the laboratory frame for a number of small molecular systems, which have previously been studied with 1D methods. Compared to the conventional single-energy or single-angle analysis, our 2D methods effectively expand the momentum range of the measured data. Besides utilization of the 2D data, two complementary methods are developed for consistency check on the retrieved results. Further, the 2D nature of our methods also offers a way of estimating the error from retrieval, which has never been explored before. Comparing with results from prior experiments, our findings show evidence that our 2D methods outperform the conventional 1D methods. Paving the way to the retrieval of large molecular systems, in which their tunneling ionization rates are challenging to obtain, we estimate the error of using the isotropic model in place of including the orientation-dependent ionization rate.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Ultrafast Molecular Frame Quantum Tomography

Here, we develop and experimentally demonstrate a methodology for a full molecular frame quantum tomography (MFQT) of dynamical polyatomic systems. We exemplify this approach through the complete characterization of an electronically nonadiabatic wave packet in ammonia (NH 3 ). The method exploits both energy and time-domain spectroscopic data, and yields the lab frame density matrix (LFDM) for the system, the elements of which are populations and coherences. The LFDM fully characterizes electronic and nuclear dynamics in the molecular frame, yielding the time- and orientation-angle dependent expectation values of any relevant operator. For example, the time-dependent molecular frame electronic probability density may be constructed, yielding information on electronic dynamics in the molecular frame. In NH 3 , we observe that electronic coherences are induced by nuclear dynamics which nonadiabatically drive electronic motions (charge migration) in the molecular frame. Here, the nuclear dynamics are rotational and it is nonadiabatic Coriolis coupling which drives the coherences. Interestingly, the nuclear-driven electronic coherence is preserved over longer timescales. In general, MFQT can help quantify entanglement between electronic and nuclear degrees of freedom, and provide new routes to the study of ultrafast molecular dynamics, charge migration, quantum information processing, and optimal control schemes.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Diabatization with Electrostatic Embedding for Studying Photophysics in Organic Molecular Crystals

Highly emissive organic molecular crystals find applications in several areas, such as organic electronics, solar cells, and sensors. Understanding the excited-state mechanisms underlying these applications is essential for optimizing and controlling them effectively. Exciton models coupled with nonadiabatic dynamics, particularly quantum dynamics, provide crucial insights into photochemical and photophysical processes in molecular crystals. Nevertheless, there remains a lack of general tools and automated workflows to facilitate such simulations. In this paper, we present a computational strategy to investigate the photoactivated dynamics of organic molecular crystals, bridging methodologies traditionally used for molecular systems and materials science, with a particular focus on the interplay between local excitations and charge transfer (CT) processes. We have implemented an interface between the fromage and Overdia programs, enabling the construction of vibronic Hamiltonians for molecular crystals within an excited-state ONIOM(QM:QM′) framework, incorporating long-range electrostatics through a RESP-based Ewald summation. Fragment-based diabatization provides a route to quantum dynamics simulations in weak-to-intermediate coupling regimes. The method was applied to the photophysics of dibenzo[g,p]chrysene (DBC) crystals using time-dependent DFT. The fromage/ Overdia interface was employed to compute the couplings of local excitations and CT states for 18 unique DBC dimers in the crystal and to quantify the influence of electrostatic embedding, which was found to be modest (10−20%). Simulations on π-stacked dimers reproduced the small red shift observed experimentally from solution to crystal, attributed to electronic interactions among fixed monomers rather than crystal electrostatics. Quantum dynamics simulations revealed ultrafast population transfer from bright local excitations to CT states. This approach establishes a robust framework linking molecular and solid-state excited-state dynamics, with potential applications for studying excitations, defects, and impurities in molecular crystals.

Crystals↗
DOE OSTI2025

PySIDT: Subgraph Isomorphic Decision Trees for Molecular Property Prediction

Accurate molecular property prediction is important across all fields of chemistry. Deep neural networks (DNNs) have become increasingly popular due to their ability to train automatically, avoiding the incredibly tedious process of constructing and extending traditional property estimation schemes. However, DNNs require large amounts of training data, are challenging to interpret, require large amounts of memory to load even during inference, and have severe difficulties incorporating qualitative chemical knowledge, which are often desired for molecular property prediction tasks. Here, in this study, we present PySIDT (https://github.com/zadorlab/PySIDT), a software for training and running inference on Subgraph Isomorphic Decision Trees (SIDTs). SIDTs are graph-based decision trees made of nodes associated with molecular substructures. Inference is done by descending target molecular structures down the decision tree to nodes with matching subgraph isomorphic substructures and making predictions based on the final (most specific) nodes matched. SIDTs scale down well to dataset sizes much smaller than is feasible for DNNs. As trees of molecular substructures, SIDTs are inherently readable and easy to visualize, making them easy to analyze. They are also straightforward to extend and retrain, facilitate uncertainty estimation, and enable easy integration of expert knowledge. We demonstrate the SIDT approach discussing its application to a diverse range of molecular prediction tasks: rate coefficient estimation, diffusion coefficient estimation, thermochemistry estimation, transition state bond stretch prediction, p K a prediction, stability of molecular structures, stability of surface structures, and prediction of surface lateral interaction energetics. Additionally, we demonstrate the power of the SIDT algorithms in two direct learning curve vanilla comparisons with the popular DNN-based software Chemprop and the popular gradient boosted trees-based software XGBoost on enthalpy of formation and rate coefficient prediction tasks. In particular, in the enthalpy of formation case, vanilla PySIDT is able to outperform vanilla Chemprop and XGBoost across the full range of training/validation set sizes out to 11,560 data points.

Johnson, Matthew Sean [Sandia National Laboratorie↗