Monitoring the Evolution of Relative Product Populations at Early Times during a Photochemical Reaction
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UV-Induced Reaction Pathways in Bromoform Probed with Ultrafast Electron Diffraction
For many chemical reactions, it remains notoriously difficult to predict and experimentally determine the rates and branching ratios between different reaction channels. This is particularly the case for reactions involving short-lived intermediates, whose observation requires ultrafast methods. The UV photochemistry of bromoform (CHBr 3 ) is among the most intensely studied photoreactions. Yet, a detailed understanding of the chemical pathways leading to the production of atomic Br and molecular Br 2 fragments has proven challenging. In particular, the role of isomerization and/or roaming and their competition with direct C–Br bond scission has been a matter of continued debate. Here, in this work, gas-phase ultrafast megaelectronvolt electron diffraction (MeV-UED) is used to directly study structural dynamics in bromoform after single 267 nm photon excitation with femtosecond temporal resolution. The results show unambiguously that isomerization contributes significantly to the early stages of the UV photochemistry of bromoform. In addition to direct C–Br bond breaking within <200 fs, formation of iso-CHBr 3 (Br-CH-Br-Br) is observed on the same time scale and with an isomer lifetime of >1.1 ps. The branching ratio between direct dissociation and isomerization is determined to be 0.4 ± 0.2:0.6 ± 0.2, i.e., approximately 60% of molecules undergo isomerization within the first few hundred femtoseconds after UV excitation. The structure and time of formation of iso-CHBr 3 compare favorably with the results of an ab initio molecular dynamics simulation. The lifetime and interatomic distances of the isomer are consistent with the involvement of a roaming reaction mechanism.
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Cascade CO 2 Insertion in Carbanion Ionic Liquids Driven by Structure Rearrangement
The CO 2 chemisorption in state-of-the-art sorbents based on oxide/hydroxide/amine moieties is driven by strong chemical bonding formation in the carbonate/bicarbonate/carbamate products, which in turn leads to high energy input in sorbent regeneration. In addition, the CO 2 uptake capacity was limited by the active sites’ utilization efficiency, with each active site incorporating one CO 2 molecule or less. In this work, a new concept and generation of sorbent was developed to achieve cascade insertion of multiple CO 2 molecules by leveraging structure rearrangement as the driving force, leading to in situ generation of extra CO 2 -binding sites and significantly reduced energy input for CO 2 release. The designed ionic liquids (ILs) containing carbanions with conjugated and asymmetric structure, deprotonated (methylsulfonyl)acetonitrile ([MSA]) anion, allowed the cascade insertion of two CO 2 molecules via consecutive C–C and O–C bond formations. The proton transfer and structure rearrangement of the carboxylic acid intermediates played critical roles in stabilizing the first integrated CO 2 and generating extra electron-rich oxygen sites for the insertion of the second CO 2 . The structure variation and reaction pathway were confirmed by operando spectroscopy, magnetic resonance spectroscopy (NMR), mass spectroscopy, and computational chemistry. The energy input in sorbent regeneration could be further reduced by harnessing the phase-changing behavior of the carbanion salts in ether solutions upon reacting with CO 2 , avoiding the energy consumption in heating the solvent. In conclusion, the fundamental insights obtained herein provide a promising approach to greatly improve the CO 2 sorption performance via sophisticated molecular-scale structural engineering of the sorbents.
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Three rate-determining protein roles in photosynthetic O 2 -evolution addressed by time-resolved experiments on genetically modified photosystems
Light-driven water splitting by plants, algae and cyanobacteria is pivotal for global bioenergetics and biomass formation. A manganese cluster bound to the photosystem II proteins catalyzes the complex reaction at high rate, but the rate-determining factors are insufficiently understood. Here we trace the oxygen-evolution transition by time-resolved polarography and infrared spectroscopy for cyanobacterial photosystems genetically modified at two strategic sites, complemented by computational chemistry. Our results highlight three rate-determining roles of the protein environment of the metal cluster: acceleration of proton-coupled electron transfer, acceleration of substrate-water insertion after O 2 -formation, and balancing of rate-determining enthalpic and entropic contributions. Whereas in general the substrate-water insertion step may be unresolvable in time-resolved experiments, here it likely becomes traceable because of deceleration by genetic modification. Our results may stimulate new time-resolved experiments on substrate-water insertion in photosynthesis, clarification of enthalpy-entropy compensation in enzyme catalysis, and knowledge-guided development of inorganic catalyst materials.
Bioenergetics↗
Accurate Dehydrogenation Enthalpies Dataset for Liquid Organic Hydrogen Carriers
This contribution presents a comprehensive extension of the QM9 dataset (originally at 133 K molecules) with the calculation of G4MP2 enthalpies for 9,841 molecules, featuring up to nine heavy atoms. We present QM9-LOHC, a (de)hydrogenation dataset of 10,373 reactions, including a minimum of 5.5% weight hydrogen storage capacity in line with the Department of Energy standards for Liquid Organic Hydrogen Carriers (LOHC). By utilizing the accurate quantum chemical method G4MP2 we expand the QM9 database and explore new avenues for the exploration of hydrogen storage technologies (electrochemical LOHCs, alkali metal-LOHCs, and mixtures of LOHCs). The QM9-LOHC dataset, with its focus on reactions that vary only by hydrogen saturation levels, provides a needed data resource for advancing the design and optimization of both conventional and innovative LOHC systems, and high-fidelity data for molecular discovery.
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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.
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Pressure induced structural and electronic band transition in CsPbBr3
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AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs
Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.
Chemistry↗
Real-space Kohn–Sham density functional theory for complex energy applications
Real-space Kohn-Sham density functional theory (real-space KS-DFT) enables large-scale electronic structure simulations that is particularly well-suited for the modern high-performance computing (HPC) architectures. This feature article reviews its theoretical foundations, highlights the algorithmic advances and recent developments, and showcases applications in complex nano systems. We aim to provide a perspective on the trajectory of real-space KS-DFT as an emerging tool for computational chemistry and materials science in the exascale era.
Zhang, Zeyi↗
Unsupervised atomic data mining via multi-kernel graph autoencoders for machine learning force fields
Constructing a chemically diverse dataset while avoiding sampling bias is critical to training efficient and generalizable force fields. However, in computational chemistry and materials science, many common dataset generation techniques are prone to oversampling regions of the potential energy surface. Furthermore, these regions can be difficult to identify and isolate from each other or may not align well with human intuition, making it challenging to systematically remove bias in the dataset. While traditional clustering and pruning (down-sampling) approaches can be useful for this, they can often lead to information loss or a failure to properly identify distinct regions of the potential energy surface due to difficulties associated with the high dimensionality of atomic descriptors. In this work, we introduce the Multi-kernel Edge Attention-based Graph Autoencoder (MEAGraph) model, an unsupervised approach for analyzing atomic datasets. MEAGraph combines multiple linear kernel transformations with attention-based message passing to capture geometric sensitivity and enable effective dataset pruning without relying on labels or extensive training. Demonstrated applications on niobium, tantalum, and iron datasets show that MEAGraph efficiently groups similar atomic environments, allowing for the use of basic pruning techniques for removing sampling bias. This approach provides an effective method for representation learning and clustering that can be used for data analysis, outlier detection, and dataset optimization.
Materials science↗
Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials
Machine-learned interatomic potentials (ML-IAPs) continue to gain popularity as accurate, computationally efficient replacements for traditional, physics-based interatomic potentials and expensive ab initio methods. Uncertainty quantification (UQ) of ML-IAPs is a growing area of research as UQ is critical in many applications of IAPs, such as developing curated datasets, active learning-based data augmentation, self-improving models, and estimating the uncertainty of molecular dynamics simulations. In this paper, we construct and benchmark a series of different neural network potentials (NNPs) with varying network architectures to determine the performance of these models with respect to both the mean and uncertainty calibration error. Each NNP method is specifically designed to predict either epistemic or aleatoric uncertainty with particular focus on the differences in behavior between the epistemic and aleatoric uncertainty estimates. We benchmark these methods using multiple datasets common in the ML-IAP literature. The results show that the aleatoric uncertainty from single-shot model architectures is a competitive alternative to ensemble-based epistemic uncertainty predictions in regions of sufficient data-density. However, in regions where the representative data is sparse, aleatoric uncertainty models tend to overpredict and epistemic methods tend to underpredict the actual model error. We conclude that the type of UQ is crucial when discussing performance of probabilistic model results as different methods have different performance characteristics depending on the regime in which they are evaluated. Therefore, the type of UQ method should be carefully evaluated against both the data characteristics and requirements for the intended application.
97 MATHEMATICS AND COMPUTING↗
An analysis of communications blackout for Pioneer Venus entry probes
Estimates of the maximum duration of communications blackout are presented for a proposed Pioneer Venus mission involving a multiprobe entry targeted to individual impact sites. Calculations of the inviscid flow about the probes have been made using both finite-rate chemistry and equilibrium chemistry computer codes. Equilibrium boundary-layer calculations including ablation are also presented both with and without the presence of alkali metal contaminants from the heat shield. Methods are described for estimating maximum electron density in the inviscid wake and the recirculation region at the base of the probes. The results presented provide a conservative basis for planning for storage and subsequent telemetry of data during the entry phase of the mission.
Grose, W. L.↗
NASA's hypersonic fluid and thermal physics program (Aerothermodynamics)
This survey paper gives an overview of NASA's hypersonic fluid and thermal physics program (recently renamed aerothermodynamics). The purpose is to present the elements of, example results from, and rationale and projection for this program. The program is based on improving the fundamental understanding of aerodynamic and aerothermodynamic flow phenomena over hypersonic vehicles in the continuum, transitional, and rarefied flow regimes. Vehicle design capabilities, computational fluid dynamics, computational chemistry, turbulence modeling, aerothermal loads, orbiter flight data analysis, orbiter experiments, laser photodiagnostics, and facilities are discussed.
Graves, R. A.↗
CELSS science needs
Questions and areas of study that need to be persued in order to develope a Controlled Ecological Life Support System are posed. Research topics needing attention are grouped under various leadings: ecology, genetics, plant pathology, cybernetics, chemistry, computer science, fluid dynamics, optics, and solid-state physics.
Rummel, J. D.↗
Hypersonic aerothermodynamics status report
This survey paper gives an overview of NASA's Aerothermodynamics program. The purpose is to present the elements of, example results from, and projected technology needs for hypersonic fluid and thermal physics. The NASA program is focused on improving the fundamental understanding of aerodynamic and aerothermodynamic flow phenomena over hypersonic vehicles operating in the continuum, transitional, and free molecule flow regime. Vehicle design capabilities, computational fluid dynamics, computational chemistry, turbulence modeling, aerothermal loading, Shuttle Orbiter flight data analysis, and aerothermal facilities are discussed.
Graves, R. A., Jr.↗
Civil propulsion technology for the next twenty-five years
The next twenty-five years will see major advances in civil propulsion technology that will result in completely new aircraft systems for domestic, international, commuter and high-speed transports. These aircraft will include advanced aerodynamic, structural, and avionic technologies resulting in major new system capabilities and economic improvements. Propulsion technologies will include high-speed turboprops in the near term, very high bypass ratio turbofans, high efficiency small engines and advanced cycles utilizing high temperature materials for high-speed propulsion. Key fundamental enabling technologies include increased temperature capability and advanced design methods. Increased temperature capability will be based on improved composite materials such as metal matrix, intermetallics, ceramics, and carbon/carbon as well as advanced heat transfer techniques. Advanced design methods will make use of advances in internal computational fluid mechanics, reacting flow computation, computational structural mechanics and computational chemistry. The combination of advanced enabling technologies, new propulsion concepts and advanced control approaches will provide major improvements in civil aircraft.
Rosen, Robert↗
Strategies for vectorizing the sparse matrix vector product on the CRAY XMP, CRAY 2, and CYBER 205
Large, randomly sparse matrix vector products are important in a number of applications in computational chemistry, such as matrix diagonalization and the solution of simultaneous equations. Vectorization of this process is considered for the CRAY XMP, CRAY 2, and CYBER 205, using a matrix of dimension of 20,000 with from 1 percent to 6 percent nonzeros. Efficient scatter/gather capabilities add coding flexibility and yield significant improvements in performance. For the CYBER 205, it is shown that minor changes in the IO can reduce the CPU time by a factor of 50. Similar changes in the CRAY codes make a far smaller improvement.
Bauschlicher, Charles W., Jr.↗
Recent advances at NASA in calculating the electronic spectra of diatomic molecules
Advanced entry vehicles, such as the proposed Aero-assisted Orbital Transfer Vehicle, provide new and challenging problems for spectroscopy. Large portions of the flow field about such vehicles will be characterized by chemical and thermal nonequilibrium. Only by considering the actual overlap of the atomic and rotational lines emitted by the species present can the impact of radiative transport within the flow field be assessed correctly. To help make such an assessment, a new computer program is described that can generate high-resolution, line-by-line spectra for any spin-allowed transitions in diatomic molecules. The program includes the matrix elements for the rotational energy and distortion to the fourth order; the spin-orbit, spin-spin, and spin-rotation interactions to first order; and the lambda splitting by a perturbation calculation. An overview of the Computational Chemistry Branch at Ames Research Center is also presented.
Whiting, Ellis E.↗