Electronic Structure Information Research Articles

Probing Noncovalent Interaction Strengths of Host-Guest Complexes Using Negative Ion Photoelectron Spectroscopy.

Noncovalent interactions (NCIs) are crucial for the formation and stability of host-guest complexes, which have wide-ranging implications across various fields, including biology, chemistry, materials science, pharmaceuticals, and environmental science. However, since NCIs are relatively weak and sensitive to bulk perturbation, direct and accurate measurement of their absolute strength has always been a significant challenge. This concept article aims to demonstrate the gas-phase electrospray ionization (ESI)-negative ion photoelectron spectroscopy (NIPES) as a direct and precise technique to measure the absolute interaction strength, probe nature of NCIs, and reveal the electronic structural information for host-guest complexes. Our recent studies in investigating various host-guest complexes that involve various types of NCIs such as anion-π, (di)hydrogen bonding, charge-separated ionic interactions, are overviewed. Finally, a summary and outlook are provided for this field.

The facile preparation of polydopamine-modified sepiolite and its adsorption properties and microscopic mechanism for cadmium ion

Natural sepiolite (SEP) has the characteristics of cation exchange, abundant reserves, low cost, and environmental friendliness, but its adsorption capacity for heavy metal pollutants is limited. Because polydopamine (PDA) molecules contain rich functional groups such as phenolic and amino groups, in the present work, dopamine and natural sepiolite were utilized as raw materials to prepare polydopamine-modified sepiolite composite (SEP/PDA) by simple co-precipitation method. According to Langmuir isotherm model, the maximum adsorption capacity of SEP/PDA for Cd2+ can reach 203.21 mg/g at 298 K. At the initial concentration of 100 mg/L, the adsorption capacity was 2.9 times that of natural sepiolite. The composite material has good reusability and anti-cationic interference performance and has 100 % removal ability of low concentration Cd2+ in actual electroplating wastewater. Density Functional Theory (DFT) calculations were performed to obtain the electronic structure information. To elucidate the microscopic mechanism, molecular surface analyses, Interaction Region Indicator (IRI), Atom In Molecules (AIM) theory, Bond Order Density (BOD), and Electron Localization Function (ELF) were conducted. These analyses revealed that the amino nitrogen and phenolic oxygen atoms in the dopamine molecules donate lone pair electrons to coordinate with cadmium ions. Polydopamine, which possesses multiple oxidation states, contains catechol, quinone, and indole or a lesser extend pyrrole groups that can complex with cadmium ions, resulting in the formation of a surface complex. This coordination significantly enhances the adsorption properties of the SEP/PDA composite.

Porphyrin photosensitizer molecules as effective medicine candidates for photodynamic therapy: electronic structure information aided design.

Traditional photosensitizers (PS) in photodynamic therapy (PDT) have restricted tissue penetrability of light and a lack of selectivity for tumor cells, which diminishes the efficiency of PDT. Our aim is to effectively screen porphyrin-based PS medication through computational simulations of large-scale design and screening of PDT candidates via a precise description of the state of the light-stimulated PS molecule. Perylene-diimide (PDI) shows an absorption band in the near-infrared region (NIR) and a great photostability. Meanwhile, the insertion of metal can enhance tumor targeting. Therefore, on the basis of the original porphyrin PS segments, a series of metalloporphyrin combined with PDI and additional allosteric Zn-porphyrin-PDI systems were designed and investigated. Geometrical structures, frontier molecular orbitals, ultraviolet-visible (UV-vis) absorption spectra, adiabatic electron affinities (AEA), especially the triplet excited states and spin-orbit coupling matrix elements (SOCME) of these expanded D-A porphyrin were studied in detail using the density functional theory (DFT) and time-dependent density functional theory (TDDFT) methods. PS candidates, conforming type I or II mechanism for PDT, have been researched carefully by molecular docking which targeted Factor-related apoptosis (Fas)/Fas ligand (Fasl) mediated signaling pathway. It was found that porphyrin-PDI, Fe2-porphyrin-PDI, Zn-porphyrin-PDI, Mg-porphyrin-PDI, Zn-porphyrin combined with PDI through single bond (compound 1), and two acetylenic bonds (compound 2) in this work would be proposed as potential PS candidates for PDT process. This study was expected to provide PS candidates for the development of novel medicines in PDT.

Density Functional Tight-Binding Models for Band Structures of Transition-Metal Alloys and Surfaces across the d-Block.

First-principles electronic structure simulations are an invaluable tool for understanding chemical bonding and reactions. While machine-learning models such as interatomic potentials significantly accelerate the exploration of potential energy surfaces, electronic structure information is generally lost. Particularly in the field of heterogeneous catalysis, simulated electron band structures provide fundamental insights into catalytic reactivity. This ab initio knowledge is preserved in semiempirical methods such as density functional tight binding (DFTB), which extend the accessible computational length and time scales beyond first-principles approaches. In this paper we present Shell-Optimized Atomic Confinement (SOAC) DFTB electronic-part-only parametrizations for bulk and surface band structures of all d-block transition metals that enable efficient predictions of electronic descriptors for large structures or high-throughput studies on complex systems outside the computational reach of density functional theory.

QuanDB: a quantum chemical property database towards enhancing 3D molecular representation learning

Previous studies have shown that the three-dimensional (3D) geometric and electronic structure of molecules play a crucial role in determining their key properties and intermolecular interactions. Therefore, it is necessary to establish a quantum chemical (QC) property database containing the most stable 3D geometric conformations and electronic structures of molecules. In this study, a high-quality QC property database, called QuanDB, was developed, which included structurally diverse molecular entities and featured a user-friendly interface. Currently, QuanDB contains 154,610 compounds sourced from public databases and scientific literature, with 10,125 scaffolds. The elemental composition comprises nine elements: H, C, O, N, P, S, F, Cl, and Br. For each molecule, QuanDB provides 53 global and 5 local QC properties and the most stable 3D conformation. These properties are divided into three categories: geometric structure, electronic structure, and thermodynamics. Geometric structure optimization and single point energy calculation at the theoretical level of B3LYP-D3(BJ)/6-311G(d)/SMD/water and B3LYP-D3(BJ)/def2-TZVP/SMD/water, respectively, were applied to ensure highly accurate calculations of QC properties, with the computational cost exceeding 107 core-hours. QuanDB provides high-value geometric and electronic structure information for use in molecular representation models, which are critical for machine-learning-based molecular design, thereby contributing to a comprehensive description of the chemical compound space. As a new high-quality dataset for QC properties, QuanDB is expected to become a benchmark tool for the training and optimization of machine learning models, thus further advancing the development of novel drugs and materials. QuanDB is freely available, without registration, at https://quandb.cmdrg.com/.

Synchrotron Radiation Based X-ray Absorption Spectroscopy: Fundamentals and Applications in Photocatalysis.

Photocatalysis is one of the most promising green technologies to utilize solar energy for clean energy achievement and environmental governance. There is a knotty problem to rational designing high-performance photocatalyst, which largely depends on an in-depth insight into their structure-activity relationships and complex photocatalytic reaction mechanisms. Synchrotron radiation based X-ray absorption spectroscopy (XAS) is an important characterization method for photocatlayst to offer the element-specific key geometric and electronic structural information at the atomic level, on this basis, time-resolved XAS technique has a huge impact on mechanistic understanding of photochemical reaction owing to their powerful ability to probe, in real-time, the electronic and geometric structures evolution within photocatalysis reactions. This review will focus on the fundamentals of XAS and their applications in photocatalysis. The detailed applications obtained from XAS is described through the following aspects: 1) identifying local structure of photocatalyst; 2) uncovering in situ structure and chemical state evolution during photocatalysis; 3) revealing the photoexcited process. We will provide an in depth understanding on how the XAS method can guide the rational design of highly efficient photocatalyst. Finally, a systematic summary of XAS and related significance is made and the research perspectives are suggested.

Research progress of coincidence Doppler broadening of positron annihilation measurement technology in materials

Positron annihilation technique is an atomic-scale characterization method used to analyze the defects and microstructure of materials, which is extremely sensitive to open volume defects. By examining the annihilation behaviour of positrons and electrons in open volume defects, local electron density and atomic structure information around the annihilation site can be obtained, such as the size and concentration of vacancies, and vacancy clusters. In recent years, positron annihilation spectroscopy has evolved into a superior tool for characterizing features of material compared with conventional methods. The coincident Doppler broadening technique provides unique advantages for examining the local electronic structure and chemical environment (elemental composition) information about defects due to its effectiveness describing high momentum electronic information. The low momentum portion of the quotient spectrum indicates the Doppler shift generated by the annihilation of valence electrons near the vacancy defect. Changes in the peak amplitudes and positions of the characteristic peaks in the high momentum region can reveal elemental information about the positron annihilation point. The physical mechanism of element segregation, the structural features of open volume defects and the interaction between interstitial atoms and vacancy defects are well investigated by using the coincidence Doppler broadening technology. In recent years, based on the development of Doppler broadening technology, the sensitivity of slow positron beam coincidence Doppler broadening technology with adjustable energy has been significantly enhanced at a certain depth. It is notable that slow positron beam techniques can offer surface, defect, and interface microstructural information as a function of material depth. It compensates for the fact that the traditional coincidence Doppler broadening technique can only determine the overall defect information. Positron annihilation technology has been applied to the fields of second phase evolution in irradiated materials, hydrogen/helium effect, and free volume in thin films, as a result of the continuous development of slow positron beam and the improvement of various experimental test methods based on slow positron beam. In this paper, the basic principles of the coincidence Doppler broadening technique are briefly discussed, and the application research progress of the coincidence Doppler broadening technique in various materials is reviewed by combining the reported developments: 1) the evolution behaviour of nanoscale precipitation in alloys; 2) the interaction between lattice vacancies and impurity atoms in semiconductors; 3) the changes of oxygen vacancy and metal cation concentration in oxide material. In addition, coincident Doppler broadening technology has been steadily used to estimate and quantify the sizes, quantities, and distributions of free volume holes in polymers.

Geometry and electronic structure of Yb(III)[CH(SiMe3)2]3 from EPR and solid-state NMR augmented by computations.

Characterization of paramagnetic compounds, in particular regarding the detailed conformation and electronic structure, remains a challenge, and - still today it often relies solely on the use of X-ray crystallography, thus limiting the access to electronic structure information. This is particularly true for lanthanide elements that are often associated with peculiar structural and electronic features in relation to their partially filled f-shell. Here, we develop a methodology based on the combined use of state-of-the-art magnetic resonance spectroscopies (EPR and solid-state NMR) and computational approaches as well as magnetic susceptibility measurements to determine the electronic structure and geometry of a paramagnetic Yb(III) alkyl complex, Yb(III)[CH(SiMe3)2]3, a prototypical example, which contains notable structural features according to X-ray crystallography. Each of these techniques revealed specific information about the geometry and electronic structure of the complex. Taken together, both EPR and NMR, augmented by quantum chemical calculations, provide a detailed and complementary understanding of such paramagnetic compounds. In particular, the EPR and NMR signatures point to the presence of three-centre-two-electron Yb-γ-Me-β-Si secondary metal-ligand interactions in this otherwise tri-coordinate metal complex, similarly to its diamagnetic Lu analogues. The electronic structure of Yb(III) can be described as a single 4f13 configuration, while an unusually large crystal-field splitting results in a thermally isolated ground Kramers doublet. Furthermore, the computational data indicate that the Yb-carbon bond contains some π-character, reminiscent of the so-called α-H agostic interaction.

Infrared-modulated photoluminescence spectroscopy: from wide-band coverage to micro-area and high-throughput scanning imaging

Photoluminescence (PL) spectroscopy has been widely used in the ultraviolet-near-infrared spectral range for over seventy years since the very early report in 1950’s, because it not only reveals the electronic structure information of, e.g., band gap and impurity energy levels of semiconductor materials, but also serves as an efficient tool for analyzing interfacial structures, carrier lifetime, and quantum efficiency. In the infrared band beyond about 4 μm, however, the study of PL spectroscopy had been limited for decades long due to strong thermal background interference, weak PL signal and low detection ability. In this review, a conventional PL method is introduced based on a Fourier transform infrared (FTIR) spectrometer, and a continuous-scan FTIR spectrometer-based double-modulation PL (csFTIR-DMPL) method is briefly described that was proposed in 1989 for breaking through the dilemma of the infrared band, and developed continuously in the later more than 20 years, with its limitations emphasized. Then, a step-scan FTIR spectrometer-based infrared modulated PL (ssFTIR-MPL) method reported in 2006 is analyzed with highlights on its advantages of anti-interference, sensitivity and signal-to-noise ratio, followed by enumerating its effectiveness demonstration and application progress in many research groups worldwide. Further developments in recent years are then summarized of wide-band, high-throughput scanning imaging and spatial micro-resolution infrared modulated PL spectroscopic experimental systems, and the technological progresses are demonstrated of infrared-modulated PL spectroscopy from 0.56-20 μm visible-far-infrared broadband coverage to > 1k high-throughput spectra imaging and ≤2-3 μm spatial micro-resolution. Typical achievements of collaborative research are enumerated in the visible-far-infrared semiconductor materials of dilute nitrogen/dilute bismuth quantum wells, HgCdTe epitaxial films, and InAs/GaSb superlattices. The results presented demonstrate the advancement of infrared modulated PL spectroscopy and the effectiveness of the experimental systems, and foresee further application and development in the future.

Low temperature decoherence dynamics in molecular spin systems using the Lindblad master equation.

Understanding the spin dynamics in low-temperature settings is crucial to designing and optimizing molecular spin systems for use in emerging quantum technologies. At low temperatures, irreversible loss occurs due to ensemble dynamics facilitated by electronic-nuclear spin interactions. We develop a combined open quantum systems and electronic structure theory capable of predicting trends in relaxation rates in molecular spin ensembles. We use the Gorini-Kossakowski-Sudarshan-Lindblad master equation and explicitly include electronic structure information in the decoherence channels. We apply this theory to several molecular systems pertinent to contemporary quantum technologies. Our theory provides a framework to describe irreversible relaxation effects in molecular spin systems with applications in quantum information science, quantum sensing, molecular spintronics, and other spin systems dominated by spin-spin relaxation.

Ab initio calculations of the spectra and lifetimes of the lead dimer.

Owing to the key role of the lead dimer (Pb2) as a heavy element benchmark for the Group IV-A dimers the assignment of its spectroscopic properties and chemical bonding is an important undertaking. To meet this demand, the present work provides comprehensive and detailed information on electronic structure and properties comprising a wide set of Pb2 states. Calculations are performed by a high-level ab initio approach. Firstly, the potential energy curves (PECs) of 19 Λ-S states as well as those of 24 ungerade Ω states are calculated by utilizing the multi-reference configuration interaction plus Davidson correction (MRCI + Q) method taking into account core-valence correlation (CV) and spin-orbit coupling (SOC) effect, where Ω is a quantum number of the total (Λ + S) angular momentum projection. Secondly, interactions between the bound F3Σ-u, 23Σ+u states and repulsive 15Πu state induced by strong SOC are discussed based on the PECs analysis and calculated SOC matrix, which also indicates that the F3Σ-u state is predissociative. Thirdly, based on the calculated electric dipole transition moments and energy gaps between the 0+u(III), F0+u(II), C0+u(I) and X0+g states, the intense absorption bands of Pb2 due to these transitions are interpreted. Our results indicate that the trends in intensity of absorption spectra (F0+u(II), C0+u(I) ← X0+g) in the range of 12 600-13 600 and 22 200-23 800 cm-1 are consistent with the previously observed spectra of Pb2 in the qualitatively similar regions (15 200-16 200 and 19 800-21 800 cm-1). Finally, the calculated intensity of the weak magnetic-dipole transitions from the singlet excited b1Σ+g and a1Δg states to the triplet ground X3Σ-g state and their electric quadrupole components are presented for the Pb2 molecule in terms of SOC perturbations for the calculated Ω states expressed in Λ-S state notation. Based on our theoretical assignment, we predict that the weak emission a1Δg2 → X3Σ-g1 bands could be observed experimentally. The present work provides comprehensive electronic structure information and sheds new light on the absorption and emission spectra of the Pb2 dimer.

Synergy of semiempirical models and machine learning in computational chemistry.

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort-design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive abinitio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency.

PyStructureFactor: A Python code for the molecular structure factor in tunneling ionization rates

Tunneling ionization is at the core of strong-field and attosecond science. In this paper, we present PyStructureFactor — a general Python code towards the calculation of the structure factor in the tunneling ionization rate of common molecules under intense laser fields. The numerical implementation is based on the well-developed weak-field asymptotic theory in the integral representation. The information of the electronic structure of the molecules is obtained via the PySCF quantum chemistry package. PyStructureFactor is a general computational framework that can be utilized to compute the molecular structure factor of various types of molecules, including polar and nonpolar diatomic molecules, degenerate molecules, and open-shell molecules. Examples are given that are benchmarked against known results with good agreements. The present PyStructureFactor is implemented in an efficient manner and is easily applicable towards larger molecules. Program summaryProgram title: PyStructureFactorCPC Library link to program files:https://doi.org/10.17632/td88mhw2sg.1Developer's repository link:https://github.com/TheStarAlight/PyStructureFactorLicensing provisions: Apache-2.0Programming language: Python 3Nature of problem: The structure factor of a molecule in intense laser fields determines its orientation-dependent tunneling ionization rate, which is crucial for the studies of ultrafast molecular dynamics and its control. However, the strong-field community lacks an open-source code to calculate the molecular structure factor, and can only resort to known results of a few molecules.Solution method: We developed the PyStructureFactor program with the structure factor of arbitrary molecules calculated using the weak-field asymptotic theory in the integral representation. The underlying electronic structure necessary for the calculation is obtained using the PySCF quantum chemistry package.Additional comments including restrictions and unusual features: The accuracy of the molecular structure factor calculated by PyStructureFactor is restricted by the level of precision of the electronic structure information extracted from the PySCF package.The running time depends on the size of the molecule, the basis set of the calculation, the level of precision of the electronic structure calculations, and other parameters passed to the program. The example in Fig. 2 took 1.2 seconds to finish on an AMD Ryzen 9 7950X CPU on the WSL Ubuntu 22.04 LTS.

The AXEAP2 program for Kβ X-ray emission spectra analysis using artificial intelligence.

The processing and analysis of synchrotron data can be a complex task, requiring specialized expertise and knowledge. Our previous work addressed the challenge of X-ray emission spectrum (XES) data processing by developing a standalone application using unsupervised machine learning. However, the task of analyzing the processed spectra remains another challenge. Although the non-resonant Kβ XES of 3d transition metals are known to provide electronic structure information such as oxidation and spin state, finding appropriate parameters to match experimental data is a time-consuming and labor-intensive process. Here, a new XES data analysis method based on the genetic algorithm is demonstrated, applying it to Mn, Co and Ni oxides. This approach is also implemented as a standalone application, Argonne X-ray Emission Analysis 2 (AXEAP2), which finds a set of parameters that result in a high-quality fit of the experimental spectrum with minimal intervention. AXEAP2 is able to find a set of parameters that reproduce the experimental spectrum, and provide insights into the 3d electron spin state, 3d-3p electron exchange force and Kβ emission core-hole lifetime.

Prediction of Electronic Properties of Radical-Containing Polymers at Coarse-Grained Resolutions

The properties of soft electronic materials depend on the coupling of electronic and conformational degrees of freedom over a wide range of spatiotemporal scales. The description of such properties requires multiscale approaches capable of, at the same time, accessing electronic properties and sampling the conformational space of soft materials. This could in principle be realized by connecting the coarse-grained (CG) methodologies required for adequate conformational sampling to conformationally averaged electronic property distributions via backmapping to atomistic-resolution level models and repeated quantum-chemical calculations. Computational demands of such approaches, however, have hindered their application in high-throughput computer-aided soft materials discovery. Here, we present a method that, combining machine learning and CG techniques, can replace traditional backmapping-based approaches without sacrificing accuracy. We illustrate the method for an emerging class of soft electronic materials, namely, nonconjugated, radical-containing polymers, promising materials for all-organic energy storage. Supervised machine learning models are trained to learn the dependence of electronic properties on polymer conformation at CG resolutions. We then parametrize CG models that retain electronic structure information, simulate CG condensed phases, and predict the electronic properties of such phases solely from the CG degrees of freedom. We validate our method by comparing it against a full backmapping-based approach and find good agreement between both methods. This work demonstrates the potential of the proposed method to accelerate multiscale workflows and provides a framework for the development of CG models that retain electronic structure information.

Does Serial Femtosecond Crystallography Depict State-Specific Catalytic Intermediates of the Oxygen-Evolving Complex?

Recent advances in serial femtosecond crystallography (SFX) of photosystem II (PSII), enabled by X-ray free electron lasers (XFEL), provided the first geometric models of distinct intermediates in the catalytic S-state cycle of the oxygen-evolving complex (OEC). These models are obtained by flash-advancing the OEC from the dark-stable state (S1) to more oxidized intermediates (S2 and S3), eventually cycling back to the most reduced S0. However, the interpretation of these models is controversial because geometric parameters within the Mn4CaO5 cluster of the OEC do not exactly match those expected from coordination chemistry for the spectroscopically verified manganese oxidation states of the distinct S-state intermediates. Here we focus on the first catalytic transition, S1 → S2, which represents a one-electron oxidation of the OEC. Combining geometric and electronic structure criteria, including a novel effective oxidation state approach, we analyze existing 1-flash (1F) SFX-XFEL crystallographic models that should depict the S2 state of the OEC. We show that the 1F/S2 equivalence is not obvious, because the Mn oxidation states and total unpaired electron counts encoded in these models are not fully consistent with those of a pure S2 state and with the nature of the S1 → S2 transition. Furthermore, the oxidation state definition in two-flashed (2F) structural models is practically impossible to elucidate. Our results advise caution in the extraction of electronic structure information solely from the literal interpretation of crystallographic models and call for re-evaluation of structural and mechanistic interpretations that presume exact correspondence of such models to specific catalytic intermediates of the OEC.

Heteroepitaxial Control of Fermi Liquid, Hund Metal, and Mott Insulator Phases in Single-Atomic-Layer Ruthenates.

Interfaces between dissimilar correlated oxides can offer devices with versatile functionalities, and great efforts have been made to manipulate interfacial electronic phases. However, realizing such phases is often hampered by the inability to directly access the electronic structure information; most correlated interfacial phenomena appear within a few atomic layers from the interface. Here, atomic-scale epitaxy and photoemission spectroscopy are utilized to realize the interface control of correlated electronic phases in atomic-scale ruthenate-titanate heterostructures. While bulk SrRuO3 is a ferromagnetic metal, the heterointerfaces exclusively generate three distinct correlated phases in the single-atomic-layer limit. The theoretical analysis reveals that atomic-scale structural proximity effects yield Fermi liquid, Hund metal, and Mott insulator phases in the quantum-confined SrRuO3 . These results highlight the extensive interfacial tunability of electronic phases, hitherto hidden in the atomically thin correlated heterostructure. Moreover, this experimental platform suggests a way to control interfacial electronic phases of various correlated materials.

Improving Accuracy and Transferability of Machine Learning Chemical Activation Energies by Adding Electronic Structure Information.

Predicting chemical activation energies is one of the longstanding and important challenges in computational chemistry. Recent advances have shown that machine learning can be used to create tools to predict them. Such tools can significantly decrease the computational cost for these predictions compared to traditional methods, which require an optimal path search along a high-dimensional potential energy surface. To enable this new route, we need both large and accurate datasets and a compact yet complete description of the reactions. Although data for chemical reactions is becoming increasingly available, the key step of encoding the reaction as an efficient descriptor remains a big challenge. In this paper, we demonstrate that including electronic energy levels in the description of the reaction significantly improves the prediction accuracy and transferability. Feature importance analysis further demonstrates that electronic energy levels have a higher importance than some structural information and typically require less space in the reaction encoding vector. In general, we observe that the results of the feature importance analysis relate well to the domain knowledge of fundamental chemical principles. This work can help to build better chemical reaction encodings for machine learning and thus improve the predictions of machine learning models for reaction activation energies. These models could ultimately be used to recognize reaction limiting steps in large reaction systems, allowing to account for bottlenecks at the design stage.

Inorganic Solid Electrolyte Interphase Engineering Rationales Inspired by Hexafluorophosphate Decomposition Mechanisms

Solid electrolyte interphase (SEI) engineering is an efficient approach to enhancing the cycling performance of lithium metal batteries. Lithium hexafluorophosphate (LiPF6) is a popular electrolyte salt. Mechanistic insights into its degradation pathways near the lithium metal anode are critical in modifying the battery electrolyte and SEI. In this work, we elucidate plausible reaction pathways in multiple representative electrolyte systems. Through ab initio molecular dynamics simulations, lithiation and electron transfer are identified as the triggering factors for LiPF6 degradation. Meanwhile, we find that lithium morphology and charge distribution substantially impact the interfacial dissociation pathways. Thermodynamic evaluation of the solvation effects shows that higher electrolyte dielectric constant and lithiation extent profoundly assist the LiPF6 decomposition. These findings offer quantitative thermodynamic and electronic structure information, which promotes rational SEI engineering and electrolyte tuning for lithium metal anode performance enhancement.

Automatic Generation of Local Vibrational Mode Parameters: From Small to Large Molecules and QM/MM Systems.

LModeAGen, a new protocol for the automatic determination of a nonredundant, complete set of local vibrational modes is reported, which is based on chemical graph concepts. Whereas local mode properties can be calculated for a selection of parameters targeting specific local modes of interest, a complete set of nonredundant local mode parameters is requested for the adiabatic connection scheme (ACS), relating each local vibrational mode with a normal mode counterpart, and for the decomposition of normal modes (CNM) in terms of local mode contributions, a unique way to analyze vibrational spectra. So far, nonredundant parameter sets have been generated manually following chemical intuition or from a set of redundant parameters in a trial-and-error fashion, which has hampered the study of larger systems with hundreds of parameters. LModeAGen was successfully applied for a test set of 11 systems, ranging from small molecules to the large QM (>100 atoms) subsystem of carbomonoxy-neuroglobin protein, described with a hybrid QM/MM method. The ωB97X-D/aug-cc-pVDZ, M06L/def2-TZVP, and QM/MM ωB97X-D/6-31G(d,p)/AMBER model chemistries were adopted for the description of the molecules in the test set. Our new protocol is an important step forward for a routine ACS and CNM analysis of the vibrational spectra of complex and large systems with hundreds of atoms, providing new access to important encoded electronic structure information.