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At least 199 records · Page 11

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DOE OSTI2024

Uncertainty quantification for molecular property predictions with graph neural architecture search

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets, and generalizes well to out-of-distribution datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Polymorphous nature of cubic halide perovskites

Many common crystal structures can be described by a single (or very few) repeated structural motif (“monomorphous structures”) such as octahedron in cubic halide perovskites. Interestingly, recent accumulated evidence suggests that electronic structure calculations based on such macroscopically averaged monomorphous cubic ($\textit{Pm} – 3m$) halide perovskites obtained from x-ray diffraction, show intriguing deviations from experiment. These include systematically too small band gaps, dielectric constants dominated by the electronic, negative mixing enthalpy of alloys, and significant deviations from the measured pair distribution function. We show here that a minimization of the systems $\textit{T}$ = 0 internal energy via density functional theory reveals a distribution of different low-symmetry local motifs, including tilting, rotations, and B-atom displacements (“polymorphous networks”). This is found only if one allows for larger-than-minimal cell size that does not geometrically exclude low symmetry motifs. As the (super) cell size increases, the energy is lowered relative to the monomorphous cell, and stabilizes after ~32 formula units (≥160 atoms) are included. Being a result of nonthermal energy minimization in the internal energy without entropy, this correlated set of displacements must represent the intrinsic geometry preferred by the underlying chemical bonding (lone pair bonding), and as such has a different origin than the normal, dynamic thermal disorder modeled by molecular dynamics. Indeed, the polymorphous network, not the monomorphous ansatz, is the kernel structure from which high temperature thermal agitation develops. The emerging physical picture is that the polymorphous network has an average structure with high symmetry, yet the local structural motifs have low symmetries. In this work we find that, compared with monomorphous counterparts, the polymorphous networks have significantly lower predicted total energies, larger band gaps, and ionic dominated dielectric constants, and agree much more closely with the observed pair distribution functions. An analogous polymorphous situation is found in the paraelectric phase of a few cubic oxide perovskites where local polarization takes the role of local displacements in halide perovskites, and in the paramagnetic phases of a few 3 d oxides where the local spin configuration takes that role.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Development and transferability of neural-network models for plasma-surface interactions

Plasma-surface interactions are increasingly critical to modern technologies; yet, accurate molecular dynamics simulations remain limited by the capabilities of interatomic potentials. Deep Potentials (DPs) promise to revolutionize the field by providing a systematic method for producing accurate interatomic potentials. The primary challenge of DP development is selecting a dataset, which efficiently spans the set of atomic environments one expects to encounter in the subsequent molecular dynamics simulations. The computational cost of density functional theory calculations, which are the typical basis for DP development, makes it impossible to directly verify the quality of a given DP. To address this challenge, we explore the development of a deep-learned interatomic potential, “DeepREBO,” trained to reproduce the behavior of the REBO2 empirical potential, enabling direct validation of training methodology and transferability. Using an active learning framework, we begin with a minimal dataset and iteratively expand it to train a Deep Potential-Smooth Edition model that faithfully reproduces REBO2 results for 25 eV hydrogen bombardment of diamond (001), a particularly challenging case. We show that small, carefully curated datasets can outperform large, unguided ones, with effective models requiring fewer than 15 000 snapshots. Subsequent transferability tests demonstrate that while DeepREBO generalizes well to diamond (111) surfaces, performance degrades for amorphous carbon or higher-energy impacts, highlighting the need for use-case-specific training data. We also evaluate methods to improve short-range repulsion. This study outlines best practices for training robust deep potentials and underscores the importance of dataset design for predictive plasma simulations.

Ab-initio molecular dynamics↗

DOE OSTI2023

Deep Boosted Molecular Dynamics: Accelerating Molecular Simulations with Gaussian Boost Potentials Generated Using Probabilistic Bayesian Deep Neural Network

Not provided.

Chemistry↗

DOE OSTI2025

How Does the Rate of Chain Exchange Relate to Stress Relaxation in Triblock Copolymer Networks?

The relationship between macroscopic stress relaxation and molecular-level chain exchange in triblock copolymer micelles has been explored using rheology and time-resolved small-angle neutron scattering (TR-SANS), marking the first measurements of chain exchange in concentrated triblock networks. It has long been assumed in models of transient or thermoreversible networks that the time scales for these two processes are equal. Experimentally, we find that stress relaxation occurs many orders-of-magnitude faster than chain exchange. This difference is quantitatively explained by modest dispersity in the core block that results in a slight asymmetry within any given nominally symmetric triblock. For stress relaxation to occur, only the shorter chain must pull out, while chain exchange is slowed due to the requirement of the eventual pullout of the longer block. The pullout time is extremely sensitive to the length of the core block. This mechanism is supported by measurements with an intentionally asymmetric triblock copolymer, which displays an even larger difference between the stress relaxation and chain exchange rates. These results establish a quantitative molecular-level picture of the chain dynamics associated with stress relaxation in triblock copolymer networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Can Third-Body Stabilization of Bimolecular Collision Complexes in Cold Molecular Clouds Happen?

For more than half a century, networks of radiative association, dissociative recombination, and bimolecular reactions have been postulated to drive the low-temperature chemistry of cold molecular clouds. Third-body stabilizations of collision complexes have been assumed to be 'irrelevant' due to short lifetime of such complexes. Here, we conduct crossed molecular beam studies of ground state atomic silicon with diacetylene in combination with electronic structure calculations and microcanonical kinetics models operating under cold molecular cloud conditions. Our combined experimental, electronic structure, and microcanonical kinetics modelling investigations provide compelling evidence that three-body collisions of molecular hydrogen with long-lived reaction intermediates accessed through intersystem crossing are prevalent deep inside molecular clouds. This concept might be exportable to reactions involving polycyclic aromatic hydrocarbons thus affording a versatile machinery to complex organics via third-body stabilizations of bimolecular collision complexes deep inside cold molecular clouds.

astrochemistry↗

DOE OSTI2020

The JA‐responsive MYC2‐ BADH ‐ like transcriptional regulatory module in Poncirus trifoliata contributes to cold tolerance by modulation of glycine betaine biosynthesis

Summary Glycine betaine (GB) is known to accumulate in plants exposed to cold, but the underlying molecular mechanisms and associated regulatory network remain unclear. Here, we demonstrated that PtrMYC2 of Poncirus trifoliata integrates the jasmonic acid (JA) signal to modulate cold‐induced GB accumulation by directly regulating PtrBADH‐l , a betaine aldehyde dehydrogenase (BADH)‐like gene. PtrBADH‐l was identified based on transcriptome and expression analysis in P. trifoliata . Overexpression and VIGS (virus‐induced gene silencing)‐mediated knockdown showed that PtrBADH‐l plays a positive role in cold tolerance and GB synthesis. Yeast one‐hybrid library screening using PtrBADH‐l promoter as baits unraveled PtrMYC2 as an interacting candidate. PtrMYC2 was confirmed to directly bind to two G‐box cis ‐acting elements within PtrBADH‐l promoter and acts as a transcriptional activator. In addition, PtrMYC2 functions positively in cold tolerance through modulation of GB synthesis by regulating PtrBADH‐l expression. Interestingly, we found that GB accumulation under cold stress was JA‐dependent and that PtrMYC2 orchestrates JA‐mediated PtrBADH‐l upregulation and GB accumulation. This study sheds new light on the roles of MYC2 homolog in modulating GB synthesis. In particular, we propose a transcriptional regulatory module PtrMYC2‐PtrBADH‐l to advance the understanding of molecular mechanisms underlying the GB accumulation under cold stress.

Ming, Ruhong↗

DOE OSTI2025

Structure and Sulfur: Tuning the Viscoelastic and Surface Properties of Natural Keratin Fibers

Natural keratin fibers, such as wool, possess a complex hierarchical structure that governs their mechanical properties and surface energy. However, the extent to which these characteristics are influenced by combined contributions of structural variations (e.g., fiber diameter, intermediate filament (IF) packing) and chemical composition (e.g., disulfide bond density) remains poorly understood. In this study, we investigate wool fibers from five sheep breeds (Merino, Polwarth, Cheviot, Eider, and Devon) to elucidate how these factors influence viscoelasticity and surface interactions. Using a multimodal approach integrating interfacial and bulk characterization methods, including inverse gas chromatography (IGC), atomic force microscopy-infrared spectroscopy (AFM-IR), X-ray photoelectron spectroscopy (XPS), uniaxial tensile testing, and synchrotron small-angle X-ray scattering (SAXS), we show that the nanometer-thick 18-methyleicosanoic acid (18-MEA) layer is consistently present across all wool types and plays a key role in governing hydrophobicity and surface heterogeneity. A controlled isothermal treatment at 200 °C, designed to cleave disulfide bonds, results in a nearly 40% reduction in specific surface area across all fiber types, accompanied by a significant decrease in tensile strength and 80% reduction in elongation at break for Merino and Devon wool, but limited influence on the mechanical properties of Eider fibers. Furthermore, rate-dependent tensile testing within the elastic regime reveals distinct viscoelastic responses among the fiber types, suggesting that the sulfur-rich protein matrix surrounding IFs and its structure contribute actively to stress partitioning. Altogether, when combined with conclusions from SAXS measurements of IF spacing, our work offers compelling insights into the role of the keratin-associated protein (KAP) matrix in shaping wool fiber mechanics. Differences in mechanical behavior among wool types, despite similar IF spacing or sulfur content, highlight the importance of matrix composition and cross-linking density, suggesting that the molecular architecture of the KAP network may be a dominant factor in determining fiber performance.

X-ray scattering↗

DOE OSTI2020

CryoEM structure of the low-complexity domain of hnRNPA2 and its conversion to pathogenic amyloid

hnRNPA2 is a human ribonucleoprotein (RNP) involved in RNA metabolism. It forms fibrils both under cellular stress and in mutated form in neurodegenerative conditions. Previous work established that the C-terminal low-complexity domain (LCD) of hnRNPA2 fibrillizes under stress, and missense mutations in this domain are found in the disease multisystem proteinopathy (MSP). However, little is known at the atomic level about the hnRNPA2 LCD structure that is involved in those processes and how disease mutations cause structural change. Here we present the cryo-electron microscopy (cryoEM) structure of the hnRNPA2 LCD fibril core and demonstrate its capability to form a reversible hydrogel in vitro containing amyloid-like fibrils. Whereas these fibrils, like pathogenic amyloid, are formed from protein chains stacked into β-sheets by backbone hydrogen bonds, they display distinct structural differences: the chains are kinked, enabling non-covalent cross-linking of fibrils and disfavoring formation of pathogenic steric zippers. Both reversibility and energetic calculations suggest these fibrils are less stable than pathogenic amyloid. Moreover, the crystal structure of the disease-mutation-containing segment (D290V) of hnRNPA2 suggests that the replacement fundamentally alters the fibril structure to a more stable energetic state. These findings illuminate how molecular interactions promote protein fibril networks and how mutation can transform fibril structure from functional to a pathogenic form.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Vibrational relaxation dynamics in layered perovskite quantum wells

Significance Halide perovskites, especially layered perovskites, offer a number of advantages to creating bright and efficient light-emitting devices and other optoelectronic applications. The organic–inorganic hybrid layered perovskite features complex lattice dynamics due to the ionic character of the crystal and the softness arising from noncovalent bonds between molecular moieties and the inorganic network. Especially, the packing geometry of the organic barriers leads to a structural deformation of inorganic octahedral, which strongly affects the properties that are crucial for device applications. In this work, we use high-resolution resonant impulsive stimulated Raman spectroscopy of a variety of ligand substitutions in layered perovskites. We find the composition of organic ligands can substantially change the dephasing rate of optical phonons and their temperature dependence, due to varying degrees of anharmonicity in the lattice and dynamic structural disorder.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Dose-dependent consequences of sub-chronic fentanyl exposure on neuron and glial co-cultures

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.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Sulfur Conversion to Donor‐Acceptor Ladder Polymer Networks through Mechanochemical Nucleophilic Aromatic Substitution for Efficient CO 2 Photoreduction

The development of synthetic methods capable of converting elemental sulfur into conjugated porous sulfur‐rich polymers remains a great challenge, although direct utilization of this readily available feedstock can significantly enrich its uses and circumvent environmental problems during sulfur storage. Here, we report herein mechanochemical (MC) nucleophilic aromatic substitution (S N Ar) that enables sulfur conversion into thianthrene‐bridged porous ladder polymer networks with dense donor‐acceptor (D−A) molecular junctions. We demonstrate that the key lies in the generation of bent thianthrene units through a solid‐state ball‐milling condensation reaction between 1,2‐dihaloarenes and elemental sulfur. We also show that the assembling of D−A structural motifs into porous networks affords efficient visible‐light‐driven photocatalytic reduction of carbon dioxide (CO 2 ) with water (H 2 O) vapor, in the absence of any additional photosensitizer, sacrificial agents or cocatalysts. Exceptional photoinduced charge separation along with boosted exciton dissociation results in a high‐performance of carbon monoxide (CO) production rate of 306.1 μmol g −1 h −1 with near 100 % CO selectivity, which is accompanied by H 2 O oxidation to O 2 , as confirmed by both experimental and theoretical results. We anticipate this novel MC S N Ar approach will advance processing techniques for direct sulfur utilization and facilitate new possibilities for the synthesis of D−A ladder polymer networks with promising potential in photocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Computational optimal transport for molecular spectra: The fully discrete case

In this study, the use of computational optimal transport is investigated as a tool for comparing two molecular spectra. Unlike other techniques for comparing molecular spectra in a pattern-recognition framework, transport distances simultaneously encode information about line positions and intensities. In addition, it is shown that transport distances are a useful alternative to Euclidean distances as Euclidean distances are based on line-by-line comparisons, while transport distances reflect broader features of molecular spectra and adequately compare spectra with different resolutions. This paper includes a tutorial on the use of optimal transport and investigates several well-chosen examples to illustrate the utility of computational optimal transport for comparing molecular spectra.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Neural Phase Simulation

The Neural Phase Simulation (NPS) is a package of codes for simulating microstructure evolution and accelerated molecular dynamics with deep neural-networks based surrogate models. NPS is designed to offer quantitatively accurate and computationally efficient simulation capabilities by leveraging modern machine-learning techniques. The primary intended use cases of NPS are training neural network surrogate models, though performing simulations on a single node is also supported. The NPS surrogate models can be trained from ground truth simulation methods, which are supposed to be accurate but expensive, such as molecular dynamics, phase field methods, kinetic Monte Carlo and discrete dislocation dynamics.

Zhou, Fei↗

DOE OSTI2021

Self-Healing Ceramics and Mechanical Metamaterials

We performed large scale molecular dynamics (MD) simulations and reactive & quantum molecular dynamics simulations to investigate: (1) faceting, grain growth, and crack healing in ceramics; (2) stress-release mechanism and healing in fibers; and (3) mechanical properties of two-dimensional metamaterials. We also designed highly accurate interatomic potentials and forces using artificial neural networks (ANN) and first-principles molecular dynamics simulation data for use in our MD and reactive MD simulations. We demonstrated the efficacy of modeling quantum materials atomistic simulation data using unsupervised deep learning approaches on a quantum computer. These achievements are elaborated in the following sections.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Network Polymer Properties Engineered Through Polymer Backbone Dispersity and Structure

Abstract Dispersity ( Ð or M w /M n ) is an important parameter in material design and as such can significantly impact the properties of polymers. Here, polymer networks with independent control over the molecular weight and dispersity of the linear chains that form the material are developed. Using a RAFT polymerization approach, a library of polymers with dispersity ranging from 1.2—1.9 for backbone chain‐length (DP) 100, and 1.4—3.1 for backbone chain‐length 200 were developed and transformed to networks through post‐polymerization crosslinking to form disulfide linkers. The tensile, swelling, and adhesive properties were explored, finding that both at DP 100 and DP 200 the swelling ratio, tensile strength, and extensibility were superior at intermediate dispersity (1.3—1.5 for DP 100 and 1.6—2.1 for DP 200) compared to materials with either substantially higher or lower dispersity. Furthermore, adhesive properties for materials with chains of intermediate dispersity at DP 200 revealed enhanced performance compared to the very low or high dispersity chains.

Raji, Ibrahim O.↗

DOE OSTI2024

Network Polymer Properties Engineered Through Polymer Backbone Dispersity and Structure

36 MATERIALS SCIENCE↗

DOE OSTI2025

Multi-Omics Reveals Temporal Scales of Carbon Metabolism in Synechococcus Elongatus PCC 7942 Under Light Disturbance

Central carbon metabolism in model cyanobacteria involves multiple pathways to adapt to energy-light limitations across diel cycles. However, the success in mechanistic modeling for phenotypic prediction of the protein regulators in the metabolic state depends on capturing the vast possibilities emerging from multiple regulatory pathways in complex biological processes. Here, we developed a physics-informed machine learning approach based on energy-landscape concepts to predict regulatory proteins responding to cyclic circadian and unforeseen light perturbations in cyanobacterial metabolic networks. Our approach provides interpretable de novo models for inferring gene expression dynamics from Synechococcus elongatus over diel cycles and using redox proteome analysis to distinguish immediate light-responsive elements from circadian-regulated processes in carbon metabolism pathways. We identified distinct temporal signatures with the analysis of the redox proteome: there was an immediate shift in cysteine redox states accompanied by a limited change in protein abundance under constant illumination and after 2 hours of darkness. This discovery indicates that the generation of reductants coordinates photoinduced electron transport with redox metabolic pathways in two discernable molecular mechanisms: fast redox-based protein modifications occur immediately after the light disturbance, followed by slow transcriptional regulations across networks. This temporal regulation reveals how metabolic networks integrate rapid light responses with programmed circadian rhythms to maintain cellular homeostasis under the light-energy limitations over the diel cycle.

Biomolecular & subcellular processes↗