Exploring 1,2-Hydrogen Shift in Silicon Nanoparticles: Reaction Kinetics from Quantum Chemical Calculations and Derivation of Transition State Group Additivity Database

Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy

Quantitative estimates of reaction barriers are essential for developing kinetic mechanisms and predicting reaction outcomes. However, the lack of experimental data and the steep scaling of accurate quantum calculations often hinder the ability to obtain reliable kinetic values. Here, we train a directed message passing neural network on nearly 24,000 diverse gas-phase reactions calculated at CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP. Our model uses 75% fewer parameters than previous studies, an improved reaction representation, and proper data splits to accurately estimate performance on unseen reactions. Using information from only the reactant and product, our model quickly predicts barrier heights with a testing MAE of 2.6 kcal mol^-1 relative to the coupled-cluster data, making it more accurate than a good density functional theory calculation. Furthermore, our results show that future modeling efforts to estimate reaction properties would significantly benefit from fine-tuning calibration using a transfer learning technique. We anticipate this model will accelerate and improve kinetic predictions for small molecule chemistry.

Competitive Hydrogen Migration in Silicon Nitride Nanoclusters: Reaction Kinetics Generalized from Supervised Machine Learning

The rate coefficients for 52 hydrogen shift reactions for silicon nitrides containing up to 6 atoms of silicon and nitrogen have been calculated using the G3//B3LYP composite method and statistical thermodynamics. The overall reaction of substituted acyclic and cyclic silylenes to their respective silene and imine species by a 1,2-hydrogen shift reaction was sorted by three different types of H shift reactions using overall reaction thermodynamics: (1) endothermic H shift between N and Si:, (2) endothermic H shift between Si and Si:, and (3) exothermic H shift between Si and Si:. Endothermic H shift reactions between Si atoms have one dominant activation barrier where the exothermic H shift reaction between Si atoms has two barriers and a stable intermediate. The rate-determining step was determined to be from the intermediate to the substituted silene, and then kinetic parameters for the overall reaction were calculated for the two-step pathway. The single event pre-exponential factors, Ã, and activation energies, E_a, for the three different classes of hydrogen shift reactions of silicon nitrides were computed. The hydrogen shift reaction was explored for acyclic and cyclic monofunctional silicon nitrides, and the type of hydrogen shift reaction gives the most significant influence on the kinetic parameters. Using a supervised machine learning approach, the models for predicting the energy barrier of three different hydrogen shift reactions were generalized and suggested based on selected descriptors.

Deamidation reaction network mapping of pharmacologic and related proteins: impact of solvation dielectric on the degradation energetics of asparagine dipeptides

Asn-X deamidation pathways in the FV region of the monoclonal antibody (mAb).

Isolating key reaction energetics and thermodynamic properties during hardwood model lignin pyrolysis

Computational studies on the pyrolysis of lignin using electronic structure methods have been largely limited to dimeric or trimeric models. In the current work we have modeled a lignin oligomer consisting of 10 syringyl units linked through 9 β-O-4' bonds. A lignin model of this size is potentially more representative of the polymer in angiosperms; therefore, we used this representative model to examine the behavior of hardwood lignin during the initial steps of pyrolysis. Using this oligomer, the present work aims to determine if and how the reaction enthalpies of bond cleavage vary with positions within the chain. To accomplish this, we utilized a composite method using molecular mechanics based conformational sampling and quantum mechanically based density functional theory (DFT) calculations. Our key results show marked differences in bond dissociation enthalpies (BDE) with the position. In addition, we calculated standard thermodynamic properties, including enthalpy of formation, heat capacity, entropy, and Gibbs free energy for a wide range of temperatures from 25 K to 1000 K. The prediction of these thermodynamic properties and the reaction enthalpies will benefit further computational studies and cross-validation with pyrolysis experiments. Overall, the results demonstrate the utility of a better understanding of lignin pyrolysis for its effective valorization.

Data-Driven Investigation of Monosilane and Ammonia Co-Pyrolysis to Silicon-Nitride-Based Ceramic Nanomaterials

With its high strength, high thermal stability, low density, and high electrical resistance, silicon-nitride-based ceramics have been widely used as gate insulating layers, oxidation masks, and passivation layers. Employing SiN nanomaterials in anode applications also improves rate performances and cycling stability of the lithium-ion batteries. However, a fundamental understanding of the SiN synthetic process remains elusive. SiN gas-phase synthesis can be tailored with a comprehensive understanding of the underlying thermodynamics. In comparison to the characterization data available for solid-state SiN materials, high-level theoretical studies on gas-phase materials possessing Si-N bonds and comprehensive investigation of the SiN chemistry, particularly for nanoclusters, are very uncommon. Thus, we performed a theoretical study of Si and SiN alloy acyclic hydrides and polycyclic clusters to predict electronic structures and thermochemistry using quantum chemical calculation and statistical thermodynamics. Electronic properties by way of highest and lowest occupied molecular orbital energy gap and natural bonding orbitals analysis were calculated to explore the influence of elemental composition and geometry on the stability. Our studies provide characteristic data of SiN species for a data-driven approach to map the design space for discovery of novel silicon-nitride-based ceramic materials for advanced electronic and coating applications.

Tuning Hydrogenated Silicon, Germanium, and SiGe Nanocluster Properties Using Theoretical Calculations and a Machine Learning Approach

There are limited studies available that predict the properties of hydrogenated silicon-germanium (SiGe) clusters. For this purpose, we conducted a computational study of 46 hydrogenated SiGe clusters (Si _xGe _yH _z, 1 < X + Y ≤ 6) to predict the structural, thermochemical, and electronic properties. The optimized geometries of the Si _xGe _yH _z clusters were investigated using quantum chemical calculations and statistical thermodynamics. The clusters contained 6 to 9 fused Si-Si, Ge-Ge, or Si-Ge bonds, i.e., bonds participating in more than one 3- to 4-membered rings, and different degrees of hydrogenation, i.e., the ratio of hydrogen to Si/Ge atoms varied depending on cluster size and degree of multifunctionality. Our studies have established trends in standard enthalpy of formation, standard entropy, and constant pressure heat capacity as a function of cluster composition and structure. A novel bond additivity correction model for SiGe chemistry was regressed from experimental data on seven acyclic Si/Ge/SiGe species to improve the accuracy of the standard enthalpy of formation predictions. Electronic properties were investigated by analysis of the HOMO-LUMO energy gap to study the effect of elemental composition on the electronic stability of Si _xGe _yH _z clusters. These properties will be discussed in the context of tailored nanomaterials design and generalized using a machine learning approach.

Thermochemistry of gas-phase and surface species via LASSO-assisted subgraph selection

Graph theory-based regression techniques, such as group additivity, have widely been implemented for fast estimation of thermochemistry of large molecules.

Automated Transition State Theory Calculations for High-Throughput Kinetics

A scarcity of known chemical kinetic parameters leads to the use of many reaction rate estimates, which are not always sufficiently accurate, in the construction of detailed kinetic models. To reduce the reliance on these estimates and improve the accuracy of predictive kinetic models, we have developed a high-throughput, fully automated, reaction rate calculation method, AutoTST. The algorithm integrates automated saddle-point geometry search methods and a canonical transition state theory kinetics calculator. The automatically calculated reaction rates compare favorably to existing estimated rates. Comparison against high level theoretical calculations show the new automated method performs better than rate estimates when the estimate is made by a poor analogy. The method will improve by accounting for internal rotor contributions and by improving methods to determine molecular symmetry.

New singlet oxygen donors based on naphthalenes: synthesis, physical chemical data, and improved stability

Singlet oxygen donors are of current interest for medical applications, but suffer from a short half-life leading to low singlet oxygen yields and problems with storage. We have synthesized more than 25 new singlet oxygen donors based on differently substituted naphthalenes in only a few steps. The influence of functional groups on the reaction rate of the photooxygenations, thermolysis, half-life, and singlet oxygen yield has been thoroughly studied. We determined various thermodynamic data and compared them with density functional calculations. Interestingly, remarkable stabilities of functional groups during the photooxygenations and stabilizing effects for some endoperoxides during the thermolysis have been found. Furthermore, we give evidence for a partly concerted and partly stepwise thermolysis mechanism leading to singlet and triplet oxygen, respectively. Our results might be interesting for "dark oxygenations" and future applications in medicine.

The role of multifunctional kinetics during early-stage silicon hydride pyrolysis: reactivity of Si2H2 isomers with SiH4 and Si2H6

Kinetic parameters for the dominant pathways during the addition of the four Si(2)H(2) isomers, i.e., trans-HSiSiH, SiSiH(2), Si(H)SiH, and Si(H(2))Si, to monosilane, SiH(4), and disilane, Si(2)H(6), have been calculated using G3//B3LYP, statistical thermodynamics, conventional and variational transition state theory, and internal rotation corrections. The direct addition products of the multifunctional Si(2)H(2) isomers were monofunctional substituted silylenes, hydrogen-bridged species, and silenes. During addition to monosilane and disilane, the SiSiH(2) isomer was found to be most reactive over the temperature range of 800 to 1200 K. Revised parameters for the Evans-Polanyi correlation and a representative pre-exponential factor for multifunctional silicon hydride addition and elimination reaction families under pyrolysis conditions were regressed from the reactions in this study. This revised kinetic correlation was found to capture the activation energies and rate coefficients better than the current literature methods.