Additional file 1 of Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions

Additional file 1: Figure S1. The label distribution of A reagents and B solvents after data reprocessing. Detailed names of reagents and solvents can be found in the data/reaxys_output/ label_processed directory. Figure S2. The distribution of temperatures in the reaction dataset used in this work. Figure S3. The distribution of yields in the reaction dataset used in this work. Figure S4. The distribution of reactions documented with varying numbers of conditionsin the dataset. Figure S5. The hyperparameter tuning results of the first candidate generation model. Figure S6. The hyperparameter tuning results of the second temperature prediction and ranking model. Table S1. Optimized hyperparameters for the first model. Table S2. Optimized hyperparameters for the second model

Integrating Chemical Information into Reinforcement Learning for Enhanced Molecular Geometry Optimization

Geometry optimization is a crucial step in computational chemistry, and the efficiency of optimization algorithms plays a pivotal role in reducing computational costs. In this study, we introduce a novel reinforcement-learning-based optimizer that surpasses traditional methods in terms of efficiency. What sets our model apart is its ability to incorporate chemical information into the optimization process. By exploring different state representations that integrate gradients, displacements, primitive type labels, and additional chemical information from the SchNet model, our reinforcement learning optimizer achieves exceptional results. It demonstrates an average reduction of about 50% or more in optimization steps compared to the conventional optimization algorithms that we examined when dealing with challenging initial geometries. Moreover, the reinforcement learning optimizer exhibits promising transferability across various levels of theory, emphasizing its versatility and potential for enhancing molecular geometry optimization. This research highlights the significance of leveraging reinforcement learning algorithms to harness chemical knowledge, paving the way for future advancements in computational chemistry

Integrating Chemical Information into Reinforcement Learning for Enhanced Molecular Geometry Optimization

Geometry optimization is a crucial step in computational chemistry, and the efficiency of optimization algorithms plays a pivotal role in reducing computational costs. In this study, we introduce a novel reinforcement-learning-based optimizer that surpasses traditional methods in terms of efficiency. What sets our model apart is its ability to incorporate chemical information into the optimization process. By exploring different state representations that integrate gradients, displacements, primitive type labels, and additional chemical information from the SchNet model, our reinforcement learning optimizer achieves exceptional results. It demonstrates an average reduction of about 50% or more in optimization steps compared to the conventional optimization algorithms that we examined when dealing with challenging initial geometries. Moreover, the reinforcement learning optimizer exhibits promising transferability across various levels of theory, emphasizing its versatility and potential for enhancing molecular geometry optimization. This research highlights the significance of leveraging reinforcement learning algorithms to harness chemical knowledge, paving the way for future advancements in computational chemistry

Theoretical Study of 4‑(Hydroxymethyl)benzoic Acid Synthesis from Ethylene and 5‑(Hydroxymethyl)furoic Acid Catalyzed by Sn-BEA

A sustainable route has been reported for the production of terephthalic acid (PTA) from 5-(hydroxymethyl)­furoic acid (HMFA) and ethylene, both of which can be derived from biomass. This process starts with the production of 4-(hydroxymethyl)­benzoic acid (HMBA) from HMFA and ethylene catalyzed by Sn-BEA. The subsequent oxidation of HMBA leads to PTA. The present study reports the results of a detailed computational investigation of the mechanism of HMBA synthesis from ethylene and HMFA mediated by Sn-BEA. Density functional theory calculations show that the formation of HMBA proceeds via Diels–Alder cycloaddition of HMFA and ethylene, which is rate-limiting, followed by Lewis acid-catalyzed dehydration. The solution-phase reaction and six different pathways in Sn-BEA, including one pathway on the Si site and five different pathways on the Sn site, are investigated for the Diels–Alder cycloaddition of HMFA and ethylene. Energy decomposition analysis (EDA) shows that the Sn site stabilizes the transition state of the Diels–Alder reaction electrostatically instead of facilitating charge transfer between HMFA and ethylene. Therefore, the preferred pathway for the Diels–Alder reaction starts with binding HMFA to the Sn site by the carbonyl oxygen, which is the configuration that maximizes electrostatic interactions between substrates and the catalyst in the transition state. The effect of substituting Sn in the active site by Zr and Ti was examined and the highest reaction barriers were for the Ti sites. Using EDA, we found that though the barriers of the Sn and Zr site are comparable, the individual contributing effects are different: lower energy penalty associated with distortion of the geometry of the Zr site overcomes less favorable electrostatic and charge transfer effects compared to the Sn site

Thermodynamics of Anharmonic Systems: Uncoupled Mode Approximations for Molecules

The partition functions, heat capacities, entropies, and enthalpies of selected molecules were calculated using uncoupled mode (UM) approximations, where the full-dimensional potential energy surface for internal motions was modeled as a sum of independent one-dimensional potentials for each mode. The computational cost of such approaches scales the same with molecular size as standard harmonic oscillator vibrational analysis using harmonic frequencies (HO^hf). To compute thermodynamic properties, a computational protocol for obtaining the energy levels of each mode was established. The accuracy of the UM approximation depends strongly on how the one-dimensional potentials of each modes are defined. If the potentials are determined by the energy as a function of displacement along each normal mode (UM-N), the accuracies of the calculated thermodynamic properties are not significantly improved versus the HO^hf model. Significant improvements can be achieved by constructing potentials for internal rotations and vibrations using the energy surfaces along the torsional coordinates and the remaining vibrational normal modes, respectively (UM-VT). For hydrogen peroxide and its isotopologs at 300 K, UM-VT captures more than 70% of the partition functions on average. By contrast, the HO^hf model and UM-N can capture no more than 50%. For a selected test set of C2 to C8 linear and branched alkanes and species with different moieties, the enthalpies calculated using the HO^hf model, UM-N, and UM-VT are all quite accurate comparing with reference values though the RMS errors of the HO model and UM-N are slightly higher than UM-VT. However, the accuracies in entropy calculations differ significantly between these three models. For the same test set, the RMS error of the standard entropies calculated by UM-VT is 2.18 cal mol^–1 K^–1 at 1000 K. By contrast, the RMS error obtained using the HO model and UM-N are 6.42 and 5.73 cal mol^–1 K^–1, respectively. For a test set composed of nine alkanes ranging from C5 to C8, the heat capacities calculated with the UM-VT model agree with the experimental values to within a RMS error of 0.78 cal mol^–1 K^–1, which is less than one-third of the RMS error of the HO^hf (2.69 cal mol^–1 K^–1) and UM-N (2.41 cal mol^–1 K^–1) models

Analysis of the Reaction Mechanism and Catalytic Activity of Metal-Substituted Beta Zeolite for the Isomerization of Glucose to Fructose

Glucose–fructose isomerization mediated by Sn-BEA is investigated using an extended QM/MM model containing 208 tetrahedral atoms. The isomerization mechanism consists of a sequence of ring-opening, isomerization, and ring-closing processes, consistent with the previously reported experimental observations. In agreement with the experimentally observed kinetic isotope effect, the rate-determining step is found to involve a hydride shift from the C₂ carbon to the C₁ carbon. The apparent activation energy for the rate-limiting step is 22.3 kcal/mol at 343 K. The difference in the reaction barriers for the partially hydrolyzed and the fully coordinated Sn sites was investigated using energy decomposition analysis. It is found that the higher activity of the partially hydrolyzed site comes from the extra flexibility provided by the defect in the lattice. The effect of substituting Sn in the active site by Ti, Zr, V, Nb, Si, and Ge was examined, and it was found that Sn and Zr are metals that result in the lowest reaction barrier for glucose isomerization. By using energy decomposition analysis, two physical properties are shown to contribute to the magnitude of the reaction barrier: the polarizability of the metal atom in the active site and the Brønsted basicity of the oxygen atom bound to the metal atom

Computational Study of <i>p</i>‑Xylene Synthesis from Ethylene and 2,5-Dimethylfuran Catalyzed by H‑BEA

Detailed mechanisms for the synthesis of <i>p</i>-xylene as well as the primary byproducts observed experimentally, 2,5-hexadione and 2,5-dimethyl-3-[(4-methyl-1,3-cyclohexadien-1-yl)­methyl]­furan, from ethylene and 2,5-dimethylfuran (DMF) mediated by H-BEA are obtained using an extended QM/MM model containing 208 tetrahedral atoms. The formation of <i>p</i>-xylene proceeds via Diels–Alder cycloaddition of ethylene and DMF, which is rate-limiting, followed by Brønsted acid-catalyzed dehydration. Secondary addition of DMF to the substrate following the Diels–Alder reaction leads to 2,5-dimethyl-3-[(4-methyl-1,3-cyclohexadien-1-yl)­methyl]­furan. The analysis of the free energies associated with the mechanisms suggests that the secondary addition can be eliminated by introducing <i>n</i>-heptane as an inert solvent to decrease the loading of DMF in the zeolite or by using a weak Brønsted acid site to facilitate the dehydration of the Diels–Alder product, for which the rate is determined by the deprotonation via the conjugate base of the active site. Water formed in the dehydration process can react directly with DMF to form 2,5-hexadione, thereby decreasing the yield of <i>p</i>-xylene. However, the free-energy barriers for the formation of 2,5-heaxdione compared to the Diels–Alder reaction indicate that DMF and 2,5-hexadione will be equilibrated. Therefore, the 2,5-hexadione yield can be minimized by operating at a high conversion of DMF

Unimolecular Reaction Pathways of a γ‑Ketohydroperoxide from Combined Application of Automated Reaction Discovery Methods

Ketohydroperoxides are important in liquid-phase autoxidation and in gas-phase partial oxidation and pre-ignition chemistry, but because of their low concentration, instability, and various analytical chemistry limitations, it has been challenging to experimentally determine their reactivity, and only a few pathways are known. In the present work, 75 elementary-step unimolecular reactions of the simplest γ-ketohydroperoxide, 3-hydroperoxypropanal, were discovered by a combination of density functional theory with several automated transition-state search algorithms: the Berny algorithm coupled with the freezing string method, single- and double-ended growing string methods, the heuristic KinBot algorithm, and the single-component artificial force induced reaction method (SC-AFIR). The present joint approach significantly outperforms previous manual and automated transition-state searches – 68 of the reactions of γ-ketohydroperoxide discovered here were previously unknown and completely unexpected. All of the methods found the lowest-energy transition state, which corresponds to the first step of the Korcek mechanism, but each algorithm except for SC-AFIR detected several reactions not found by any of the other methods. We show that the low-barrier chemical reactions involve promising new chemistry that may be relevant in atmospheric and combustion systems. Our study highlights the complexity of chemical space exploration and the advantage of combined application of several approaches. Overall, the present work demonstrates both the power and the weaknesses of existing fully automated approaches for reaction discovery which suggest possible directions for further method development and assessment in order to enable reliable discovery of all important reactions of any specified reactant(s)

Experimental and Theoretical Study of <i>n</i>‑Butanal Self-Condensation over Ti Species Supported on Silica

The effects of the coordination environment and connectivity of Ti on the rate of <i>n</i>-butanal self-condensation over Ti-silica catalysts were investigated. Ti was introduced in two ways, either during the synthesis of mesoporous SBA-15 or via grafting onto amorphous silica with a disordered pore structure. The connectivity of Ti was then characterized by XANES, UV–vis, and Raman spectroscopy. For the lowest Ti loadings, the Ti is found to be predominantly in isolated monomeric species, irrespective of the manner of sample preparation, and as the Ti loading is increased, a progressively larger fraction of Ti is present in oligomeric species and anatase nanoparticles. The turnover frequency for butanal condensation decreased monotonically with increasing Ti loading, and the apparent activation energy increased from 60 kJ mol^–1 for monomeric species to 120 kJ mol^–1 for oligomeric species. A kinetic H/D isotope effect was observed over isolated titanol and Ti dimer catalysts suggesting that α-H abstraction is the rate-determining step. This conclusion is supported by theoretical analysis of the reaction mechanism. In agreement with experimental results, the calculated activation barrier for alkanal condensation over a Ti dimer is roughly two times greater than that over Ti-OH sites. The cause for this difference was explained by energy decomposition analysis of the enolate formation step which showed that there is a large energetic penalty for the substrate to distort over the Ti–O–Ti dimer than the Ti-OH monomer