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Data from quantum mechanical calculations were generated to train a NN model for predicting phosphorescence energies. Model uses localization layer, which learns to resemble the triplet spin density without explicit training. As part of this allocation, the HIPPYNN software package was tested and openly released though the Feynman Center for Innovation.
Data from quantum mechanical calculations were generated to train a NN model for predicting phosphorescence energies. Model uses localization layer, which learns to resemble the triplet spin density without explicit training.
Forty minute invited presentation at the Cold Molecules and Cold Chemistry workshop.
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Neural Ordinary Differential Equations (ODEs) are a promising approach to learn dynamical models from time-series data in science and engineering applications. This work aims at learning neural ODEs for stiff systems, which are usually raised from chemical kinetic modeling in chemical and biological systems. We first show the challenges of learning neural ODEs in the classical stiff ODE systems of Robertson’s problem and propose techniques to mitigate the challenges associated with scale separations in stiff systems. We then present successful demonstrations in stiff systems of Robertson’s problem and an air pollution problem. The demonstrations show that the usage of deep networks with rectified activations, proper scaling of the network outputs as well as loss functions, and stabilized gradient calculations are the key techniques enabling the learning of stiff neural ODEs. The success of learning stiff neural ODEs opens up possibilities of using neural ODEs in applications with widely varying time-scales, such as chemical dynamics in energy conversion, environmental engineering, and life sciences.
Dynamic point impact loading is a primary cause of fracture of glass screens on mobile devices. An improved understanding of crack initiation and evolution under high-speed indentation could contribute to the development of materials with better performance, but experimental observations are challenging due to the short timescales and limited depth-of-field of optical microscopy. To address this need, we have observed fracture of a chemically-strengthened glass during dynamic indentation using in situ x-ray phase-contrast imaging (XPCI). Median cracks initiate below the surface of the glass, at a depth approximately corresponding to the depth at which the surface residual compressive stress diminishes to zero. These cracks initially propagate at ~10ms –1 , rapidly accelerating to > 100ms –1 . We also observe some evidence for rate-dependent behavior, in that indentation at the lowest rates studied here (below about ~0.15ms –1 ) fails to initiate cracks regardless of the depth of the indentation (up to 18μm), while indentation at higher rates produces median cracks that either arrest or cause complete fracture, depending on the depth of indentation.
We report the surface of an electrocatalyst undergoes dynamic chemical and structural transformations under electro-chemical operating conditions. There is a dynamic exchange of metal cations between the electrocatalyst and electrolyte. Understanding how iron in the electrolyte gets incorporated in the nickel hydroxide electrocatalyst is critical for pinpointing the roles of Fe during water oxidation. Here, we report that iron incorporation and oxygen evolution reaction (OER) are highly coupled, especially at high working potentials. The iron incorporation rate is much higher at OER potentials than that at the OER dormant state (low potentials). At OER potentials, iron incorporation favors electrochemically more reactive edge sites, as visualized by synchrotron X-ray fluorescence microscopy. Using X-ray absorption spectroscopy and density functional theory calculations, we show that Fe incorporation can suppress the oxidation of Ni and enhance the Ni reducibility, leading to improved OER catalytic activity. Our findings provide a holistic approach to understanding and tailoring Fe incorporation dynamics across the electrocatalyst-electrolyte interface, thus controlling catalytic processes.
Phillip L. Geissler made important contributions to the statistical mechanics of biological polymers, heterogeneous materials, and chemical dynamics in aqueous environments. He devised analytical and computational methods that revealed the underlying organization of complex systems at the frontiers of biology, chemistry, and materials science. In this retrospective we celebrate his work at these frontiers.
Potential energy surfaces for high-energy collisions between an oxygen molecule and a nitrogen atom are useful for modeling chemical dynamics in shock waves. In the present work, we present doublet, quartet, and sextet potential energy surfaces that are suitable for studying collisions of O 2 ( 3 Σ$^{–}_{g}$) with N( 4 S) in the electronically adiabatic approximation. Two sets of surfaces are developed, one using neural networks (NNs) with permutationally invariant polynomials (PIPs) and one with the least-squares many-body (MB) method, where a two-body part is an accurate diatomic potential and the three-body part is expressed with connected PIPs in mixed-exponential-Gaussian bond order variables (MEGs). We find, using the same dataset for both fits, that the fitting performance of the PIP-NN method is significantly better than that of the MB-PIP-MEG method, even though the MB-PIP-MEG fit uses a higher-order PIP than those used in previous MB-PIP-MEG fits of related systems (such as N 4 and N 2 O 2 ). However, the evaluation of the PIP-NN fit in trajectory calculations requires about 5 times more computer time than is required for the MB-PIP-MEG fit.
Aerosol effects on climate, weather, and air quality depend on characteristics of individual particles, which are tremendously diverse and change in time. Particle-resolved models are the only models able to capture this diversity in particle physiochemical properties, and these models are computationally expensive. As a strategy for accelerating particle-resolved microphysics models, we introduce Graph-based Learning of Aerosol Dynamics (GLAD) and use this model to train a surrogate of the particle-resolved model PartMC-MOSAIC. GLAD implements a Graph Network-based Simulator (GNS), a machine learning framework that has been used to simulate particle-based fluid dynamics models. In GLAD, each particle is represented as a node in a graph, and the evolution of the particle population over time is simulated through learned message passing. Here, we demonstrate our GNS approach on a simple aerosol system that includes condensation of sulfuric acid onto particles composed of sulfate, black carbon, organic carbon, and water. A graph with particles as nodes is constructed, and a graph neural network (GNN) is then trained using the model output from PartMC-MOSAIC. The trained GNN can then be used for simulating and predicting aerosol dynamics over time. Results demonstrate the framework's ability to accurately learn chemical dynamics and generalize across different scenarios, achieving efficient training and prediction times. We evaluate the performance across four scenarios, highlighting the framework's robustness and adaptability in modeling aerosol microphysics and chemistry.
We have studied the chemical transformations of semi-volatile organic iodide species (RI) in the condensed phase as well as adsorbed onto the surfaces of environmentally relevant particles, e.g., soot (carbon) and dust (silica) under real-world conditions. The combination of chosen experimental methods provide a molecular-level insight into the chemical dynamics of organic iodides. Throughout this project, we have developed new experimental, quantum chemistry, and data analysis capabilities. All experimental work was performed with 127 I.
The coupled-trajectory mixed quantum–classical method (CTMQC), derived from the exact factorization approach, has successfully predicted photo-chemical dynamics in a number of interesting molecules, capturing population transfer and decoherence from first principles. Furthermore, due to the approximations made, CTMQC does not guarantee energy conservation. We propose a modified algorithm, CTMQC-E, which redefines the integrated force in the coupled-trajectory term so to restore energy conservation, and demonstrate its accuracy on scattering in Tully’s extended coupling region model and photoisomerization in a retinal chromophore model.
Abstract The dynamics of photodissociation and recombination in heme proteins represent an archetypical photochemical reaction widely used to understand the interplay between chemical dynamics and reaction environment. We report a study of the photodissociation mechanism for the Fe(II)-S bond between the heme iron and methionine sulfur of ferrous cytochrome c . This bond dissociation is an essential step in the conversion of cytochrome c from an electron transfer protein to a peroxidase enzyme. We use ultrafast X-ray solution scattering to follow the dynamics of Fe(II)-S bond dissociation and 1 s 3 p (Kβ) X-ray emission spectroscopy to follow the dynamics of the iron charge and spin multiplicity during bond dissociation. From these measurements, we conclude that the formation of a triplet metal-centered excited state with anti-bonding Fe(II)-S interactions triggers the bond dissociation and precedes the formation of the metastable Fe high-spin quintet state.
The DMC meeting, since 1965, has a great history of providing a unique platform and focal point for the gathering of experimentalists and theoreticians in the field of chemical dynamics. At this biannual conference which spans five days, the latest developments in bimolecular reactions, unimolecular reactions, reactions in clusters and solutions, reactions at interfaces, reactions in combustion, atmospheres, and interstellar media, as well as cold chemistry, new spectroscopic techniques and non-adiabatic reactions will be discussed by experts in these fields. The 2021 meeting was postponed due to Covid but is back in 2023 including the traditional awarding of two Herschbach Medals, honoring important contributions to the fields of theoretical and experimental dynamics respectively. The 2023 meeting will be organized by Richard Dawes (Chair) and Amy Mullin (Vice Chair). The DMC format encourages participation of young scientists through invited and contributed talks as well as posters. This proposal seeks support for students, post-docs and invited scientists to attend the meeting.
To improve lithium-ion battery technology, it is essential to probe and comprehend the microscopic dynamic processes that occur in a real-world composite electrode under operating conditions. The primary and secondary particles are the structural building blocks of battery cathode electrodes. Their dynamic inconsistency has profound but not well-understood impacts. In this research, we combine operando coherent multi-crystal diffraction and optical microscopy to examine the chemical dynamics in local domains of layered oxide cathode. Our results not only pinpoint the asynchronicity of the lithium (de)intercalation at the sub-particle level, but also reveal sophisticated diffusion kinetics and reaction patterns, involving various localized processes, e.g., chemical onset, reaction front propagation, domains equilibration, particle deformation and motion. These observations shed new lights onto the activation and degradation mechanisms of state-of-the-art battery cathode materials.
Experimental investigations of chemical fractionation/phase partition and aerosol dynamics in high-temperature environment were reviewed and discussed. Particle composition and size distribution are the key data for aerosol dynamics predictive model and simulation. However, there were no particle size distribution available from the previous works. Nucleation, condensation, and coagulation are three key physical processes driving the fractionation or phase partition thereby the aerosol dynamics. A new approach was proposed to experimentally observe fractionation/phase partition and provide data for validating the predictive model for aerosol dynamics in the simulation of bomb debris formation.
For turbulent reacting flow systems, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the computational cost of device-scale simulations. Principal component analysis (PCA), and its variants, are a widely employed class of methods. Recently, an alternative technique that focuses on higher-order statistical interactions, co-kurtosis PCA (CoK-PCA), has been shown to effectively provide a low-dimensional representation by capturing the stiff chemical dynamics associated with spatiotemporally localized reaction zones. While its effectiveness has only been demonstrated based on a priori analyses with linear reconstruction, in this work, we employ nonlinear techniques to reconstruct the full thermo-chemical state and evaluate the efficacy of CoK-PCA compared to PCA. Specifically, we combine a CoK-PCA-/PCA-based dimensionality reduction (encoding) with an artificial neural network (ANN) based reconstruction (decoding) and examine, a priori, the reconstruction errors of the thermo-chemical state. In addition, we evaluate the errors in species production rates and heat release rates, which are nonlinear functions of the reconstructed state, as a measure of the overall accuracy of the dimensionality reduction technique. We employ four datasets to assess CoK-PCA/PCA coupled with ANN-based reconstruction: zero-dimensional (homogeneous) reactor for autoignition of an ethylene/air mixture that has conventional single-stage ignition kinetics, a dimethyl ether (DME)/air mixture which has two-stage (low and high temperature) ignition kinetics, a one-dimensional freely propagating premixed ethylene/air laminar flame, and a two-dimensional dataset representing turbulent autoignition of ethanol in a homogeneous charge compression ignition (HCCI) engine. Finally, results from the analyses demonstrate the robustness of the CoK-PCA based low-dimensional manifold with ANN reconstruction in accurately capturing the data, specifically from the reaction zones.
Disclosed herein are systems, methods, systems and devices for measurement and visualization of chemical dynamics in living cells or tissues for diagnostic pathology. Devices can be open- or closed-channel microfluidic membrane devices for long-term IR spectroscopy of live adherent cells and ultimately for rapidly identifying time-dependent spectral features indicative of chemical abnormality in individual cells.