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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Interactive Quantum Chemistry Enabled by Machine Learning, Graphical Processing Units, and Cloud Computing

Modern quantum chemistry algorithms are increasingly able to accurately predict molecular properties that are useful for chemists in research and education. Despite this progress, performing such calculations is currently unattainable to the wider chemistry community, as they often require domain expertise, computer programming skills, and powerful computer hardware. In this review, we outline methods to eliminate these barriers using cutting-edge technologies. We discuss the ingredients needed to create accessible platforms that can compute quantum chemistry properties in real time, including graphical processing units–accelerated quantum chemistry in the cloud, artificial intelligence–driven natural molecule input methods, and extended reality visualization. We end by highlighting a series of exciting applications that assemble these components to create uniquely interactive platforms for computing and visualizing spectra, 3D structures, molecular orbitals, and many other chemical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Predicting permeation of compounds across the outer membrane of P. aeruginosa using molecular descriptors

The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds’ intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.

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

Competing Singlet Fission and Excimer Formation in Solid Fluorinated 1,3-Diphenylisobenzofurans

Singlet fission (kSF) and excimer formation (kEXC) rate constants along with other photophysical properties of thin solid layers of 1,3-diphenylisobenzofuran and 11 of its fluorinated derivatives have been determined. The molecular properties of these compounds are similar, but their crystal packing varies widely. Most of them undergo singlet fission whereas excitation in others is trapped in excimers. The trend in rate constants kSF agrees qualitatively with results of calculations by a simplified version of the frontier orbital model for a molecular pair. The main shortcoming of the model is discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs

Molecular representation is a critical element in our understanding of the physical world and the foundation for modern molecular machine learning. Previous molecular machine learning models have used strings, fingerprints, global features and simple molecular graphs that are inherently information-sparse representations. However, as the complexity of prediction tasks increases, the molecular representation needs to encode higher fidelity information. This work introduces a new approach to infusing quantum-chemical-rich information into molecular graphs via stereoelectronic effects, enhancing expressivity and interpretability. Learning to predict the stereoelectronics-infused representation with a tailored double graph neural network workflow enables its application to any downstream molecular machine learning task without expensive quantum-chemical calculations. We show that the explicit addition of stereoelectronic information substantially improves the performance of message-passing two-dimensional machine learning models for molecular property prediction. We show that the learned representations trained on small molecules can accurately extrapolate to much larger molecular structures, yielding chemical insight into orbital interactions for previously intractable systems, such as entire proteins, opening new avenues of molecular design. Finally, we have developed a web application (simg.cheme.cmu.edu) where users can rapidly explore stereoelectronic information for their own molecular systems.

Boiko, Daniil A↗
DOE OSTI2020

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

Abstract Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data diversity, we visualize it with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc. We provide this data to the community to aid research and development of ML models for chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1975

Vibration-translation energy transfer in anharmonic diatomic molecules. 2: The vibrational quantum number dependence

A semiclassical model of the inelastic collision between a vibrationally excited anharmonic oscillator and a structureless atom was used to predict the variation of thermally averaged vibration-translation rate coefficients with temperature and initial-state quantum number. Multiple oscillator states were included in a numerical solution for collinear encounters. The results are compared with CO-He experimental values for both ground and excited initial states using several simplified forms of the interaction potential. The numerical model was also used as a basis for evaluating several less complete but analytic models. Two computationally simple analytic approximations were found that successfully reproduced the numerical rate coefficients for a wide range of molecular properties and collision partners. Their limitations were also identified. The relative rates of multiple-quantum transitions from excited states were evaluated for several molecular types.

Mckenzie, R. L.↗
NASA NTRS1976

Vibration-translation energy transfer in anharmonic diatomic molecules. II - The vibrational quantum-number dependence

A semiclassical model of the inelastic collision between a vibrationally excited anharmonic oscillator and a structureless atom is used to predict the variation of thermally averaged vibrational-translational rate coefficients with temperature and initial-state quantum number. Multiple oscillator states are included in a numerical solution for collinear encounters. The results are compared with CO-He experimental values for both ground and excited initial states using several simplified forms of the interaction potential. The numerical model is also used as a basis for evaluating several less complete, but analytic, models. Two computationally simple analytic approximations are found that successfully reproduce the numerical rate coefficients for a wide range of molecular properties and collision partners. Their limitations are identified, and the relative rates of multiple-quantum transitions from excited states are evaluated for several molecular types.

Mckenzie, R. L.↗
DOE OSTI2026

Active learning enables generation of molecules that advance the known Pareto front

Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up to 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model.

Chemistry↗
NASA NTRS2026

A Study of Space Environment Effect on Highly Thermally Conductive Hybrid Carbon Fiber Polymer Composites

A set of novel highly thermally conductive hybrid carbon fiber (CF) polymer composites has been developed for lightweight thermal radiator applications in space missions. This study investigates the effects of the space environment on these materials following exposure in low-Earth orbit (LEO) during the Materials International Space Station Experiment (MISSE)-17 flight mission, where samples experienced 159 days of combined atomic oxygen (AO), ultraviolet (UV) radiation, high vacuum, space radiation and thermal cycling. Post-flight characterization included weight loss, surface morphology, thermo-optical properties, molecular structures, glass transition temperature, thermal degradation and thermal conductivity analyses. The pyrolytic graphite sheet (PGS) samples exhibited negligible weight loss, stable thermo-optical property, and only minor oxidation signature on the exposed surface. Although AO erosion of the epoxy polymer matrix was evident, carbon nanotube (CNT)-infused PGS/CF epoxy composites retained high thermal emissivity and preserved their high thermal conductivities. These results demonstrate that the novel highly thermally conductive hybrid CF composites possess strong environmental resilience and are promising candidates for lightweight thermal radiators and thermal management components in future space exploration missions.

Space Environment↗
NASA NTRS1992

Monitoring Polymer Curing Via Electromagnetic Impedance

New nondestructive in-situ electromagnetic-impedance measurement technique senses cure-processing properties of high-temperature, high-performance thermostat and thermoplastic resins. Continuous frequency-dependent measurement and analysis performed during curing cycle. Monitors and measures molecular properties of polymeric resin in liquid and solid states. Applications include nondestructive means for evaluation of materials, determination of "window" boundaries of curing cycles of thermoplastics and thermoset resins, and for online, closed-loop control of curing cycles.

Freeman, William T.↗
DOE OSTI2022

Diffusion Monte Carlo approaches for studying nuclear quantum effects in fluxional molecules

Abstract Diffusion quantum Monte Carlo (DMC) provides a powerful approach for obtaining the ground state energy and wave function of molecules, ions, and molecular clusters. The approach is uniquely well suited for studies of fluxional molecules, which undergo large amplitude vibrational motions even in their ground state. In contrast to the electronic structure problem, where the wave function must be antisymmetric with respect to exchange of any pair of electrons, the wave function for the ground vibrational state is nodeless. This greatly simplifies the application of DMC for vibrational problems. Because there is not a single potential function that can be used to describe the intramolecular and intermolecular interactions in all molecular systems, most methods that are used to describe nuclear quantum effects rely on a carefully chosen zero‐order description of the molecular vibrations. In contrast, DMC calculations can be performed in Cartesian coordinates, making the DMC algorithm easily transferable between different chemical systems. In this contribution, the theory that underlies DMC will be discussed along with important considerations for performing DMC calculations. Extensions for evaluating vibrationally excited states and molecular properties are also discussed. Insights that can be obtained from DMC calculations are illustrated in the context of the protonated water clusters. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Theoretical and Physical Chemistry > Spectroscopy

Chemistry↗
NASA NTRS1986

Characterization of the relationship of the cure cycle chemistry to cure cycle processing properties

Dynamic Dielectric measurements made over a wide range of frequency provide a sensitive and convenient means for monitoring the cure process in thermosets and thermoplastics. The measurement of dielectric relaxation is one of only a few instrumental techniques available for studying molecular properties in both the liquid and solid states. Furthermore, it is probably the only convenient experimental technique for studying the polymerization process of going from a monomeric liquid of varying viscosity to a crosslinked, insoluble, high temperature solid. The objective of the research is to develop on-line dielectric instrumentation for quantitative nondestructive material evaluation and closed loop smart cure cycle control. The key is to relate the chemistry of the cure cycle process to the dielectric properties of the polymer system by correlating the time, temperature, and frequency dependent dielectric measurements with chemical characterization measurements. Measurement of the wide variation in magnitude of the complex permittivity with both frequency and state of cure, coupled with chemical characterization work, have been shown in the laboratory to have the potential to determine: resin quality, composition and age; cure cycle window boundaries; onset of flow and point of maximum flow; extent of and completion of reaction; evolution of volatiles; T sub g; and, crosslinking and molecular weight buildup.

Kranbuehl, D. E.↗
DOE OSTI2026

Expanding the Domain of Applicability of Machine Learning Models with Limited Data for Drug Property Prediction

Accurate machine learning models for predicting small molecule interactions with biological targets are essential for therapeutic discovery, biothreat response, and computational drug design, but their performance is often limited for understudied targets with sparse experimental data. To address this challenge, we developed and evaluated methods to improve molecular property prediction under low-data conditions, using the NimA-related kinase (NEK) family as a proof-of-concept. This work focused on two complementary goals within the ATOM Modeling PipeLine (AMPL) and the Generative Molecular Design (GMD) loop: expanding model applicability through transfer learning, representation learning, feature scaling, sampling strategies, and active-learning-inspired compound selection; and enabling efficient virtual screening to prioritize compounds that balance predicted activity, design objectives, and synthetic accessibility.

organic↗
NASA NTRS1977

MCSCF + Cl wavefunctions and properties of the X2-Pi and A2-Pi states of ClO

A multiconfiguration, self-consistent field plus configuration-interaction calculation has been performed on the X2-Pi and A2-Pi electronic states of ClO. The values of certain molecular properties computed from the wavefunctions agree well with those from experiment. The sum of the squares of the computed electronic transition moments between the X2-Pi and A2-Pi states at 3.07 bohr is 1.75 a.u., which corresponds to the experimental result of 1.58 plus or minus 0.20 a.u. The computed ground state dipole moment at the experimental equilibrium separation is 1.232 D, which compares well with two experimental results of 1.18 plus or minus 0.12 and 1.239 plus or minus 0.010 D. The value of the computed dissociation energy is 2.75 eV, which agrees well with the experimental value of 2.803 plus or minus 0.001 eV.

Arnold, J. O.↗
NASA NTRS1998

The Anatomy of the Perseus Spiral Arm: 12 CO and IRAS Imaging Observations of the W3-W4-W5 Cloud Complex

Panoramic images of 12CO J = 1-0 and thermal dust emissions from the W3-W4-W5 region of the outer Galaxy are presented. These data and recently published H I 21 cm line emission images provide an approximate 1' resolution perspective to the dynamics and thermal energy content of the interstellar gas and dust components contained within a 9 deg. arc of the Perseus spiral arm. We tabulate the molecular properties of 1560 clouds identified as closed surfaces within the l-b-v CO data cube at a threshold of 0.9 K T* (sub R). Relative surface densities of the molecular (28:1) and atomic (2.5:1) gas components determined within the arm and interarm velocity intervals demonstrate that the gas component that enters the spiral arm is predominantly atomic. Molecular clouds must necessarily condense from the compressed atomic material that enters the spiral arm and are likely short lived within the interarm regions. From the distribution of centroid velocities of clouds, we determine a random cloud-to-cloud velocity dispersion of 4 km s (exp. -1) over the width of the spiral arm but find no clear evidence within the molecular gas for streaming motions induced by the spiral potential. The far-infrared images are analyzed with the CO J = 1-0 and H I 21 cm line emission. The enhanced UV (Ultraviolet) radiation field from members of the Cas OB6 association and embedded newborn stars provide a significant source of heating to the extended dust component within the Perseus arm relative to the quiescent cirrus regions. Much of the measured far-infrared flux (69% at 60 micrometers and 47% at 100 micrometers) originates from regions associated with star formation rather than the extended, infrared cirrus component.

Heyer, Mark H.↗
NASA NTRS1998

The Anatomy of the Perseus Spiral ARM: (sup 12)CO and IRAS Imaging Observations of the W3-W4-W5 Cloud Complex

Panoramic images of (sup l2)CO J = 1-0 and thermal dust emissions from the W3-W4-W5 region of the outer Galaxy are presented. These data and recently published H (sub I) 21 cm line emission images provide an approx. 1 min resolution perspective to the dynamics and thermal energy content of the interstellar gas and dust components contained within a 9 deg arc of the Perseus spiral arm. We tabulate the molecular properties of 1560 clouds identified as closed surfaces within the l-b-v CO data cube at a threshold of 0.9 K T(sup *)(sub R). Relative surface densities of the molecular (28:1) and atomic (2.5: 1) gas components determined within the arm and interarm velocity intervals demonstrate that the gas component that enters the spiral arm is predominantly atomic. Molecular clouds must necessarily condense from the compressed atomic material that enters the spiral arm and are likely short lived within the interarm regions. From the distribution of centroid velocities of clouds, we determine a random cloud-to-cloud velocity dispersion of 4 km/s over the width of the spiral arm but find no clear evidence within the molecular gas for streaming motions induced by the spiral potential. The far-infrared images are analyzed with the CO J = 1-0 and H (sub I) 21 cm line emission. The enhanced UV radiation field from members of the Cas OB6 association and embedded newborn stars provide a significant source of heating to the extended dust component within the Perseus arm relative to the quiescent cirrus regions. Much of the measured far-infrared flux (69% at 60 microns and 47% at 100 microns) originates from regions associated with star formation rather than the extended, infrared cirrus component.

Heyer, Mark H.↗
DOE OSTI2022

SOMAS: a platform for data-driven material discovery in redox flow battery development

Abstract Aqueous organic redox flow batteries offer an environmentally benign, tunable, and safe route to large-scale energy storage. The energy density is one of the key performance parameters of organic redox flow batteries, which critically depends on the solubility of the redox-active molecule in water. Prediction of aqueous solubility remains a challenge in chemistry. Recently, machine learning models have been developed for molecular properties prediction in chemistry and material science. The fidelity of a machine learning model critically depends on the diversity, accuracy, and abundancy of the training datasets. We build a comprehensive open access organic molecular database “Solubility of Organic Molecules in Aqueous Solution” (SOMAS) containing about 12,000 molecules that covers wider chemical and solubility regimes suitable for aqueous organic redox flow battery development efforts. In addition to experimental solubility, we also provide eight distinctive quantum descriptors including optimized geometry derived from high-throughput density functional theory calculations along with six molecular descriptors for each molecule. SOMAS builds a critical foundation for future efforts in artificial intelligence-based solubility prediction models.

25 ENERGY STORAGE↗
DOE OSTI2025

CSGL: chemical synthesis graph learning for molecule representation

Abstract Motivation Molecule representation learning (MRL) translates molecules into a real vector space, serving as input to downstream tasks in biology, chemistry, and computer science. This article introduces a chemical synthesis graph learning (CSGL) framework, which enhances MRL by considering both the atomic structures of molecules and their roles in chemical reactions through a hierarchical graph representation. Specifically, molecules are first modeled based on their molecular graphs, which capture atomic-level structural information. They are then further refined using a chemical synthesis graph, where nodes represent reactant and product molecule sets, and edges encode chemical transformations between reactants and products (e.g. changes in molecular structures). CSGL optimizes molecular embeddings of reactant and product nodes in a fashion that ensures the embeddings conform to a chemical balance constraint. Results Experimental results show that our method CSGL achieves strong performance on a variety of tasks, including product prediction, reaction classification, and molecular property prediction. Availability and implementation https://github.com/li-2023/CSGL.

Biochemistry & Molecular Biology↗