Machine learning matrix product state ansatz for strongly correlated systems.

Machine learning (ML) has been used to optimize the matrix product state (MPS) ansatz for the wavefunction of strongly correlated systems. The ML optimization of MPS has been tested for the Heisenberg Hamiltonian on one-dimensional and ladder lattices, which correspond to conjugated molecular systems. The input descriptors and output for the supervised ML are lattice configurations and configuration interaction coefficients, respectively. Efficient learning can be achieved from data over the full Hilbert space via exact diagonalization or full configuration interaction, as well as over a much smaller sub-space via Monte Carlo Configuration Interaction. We show that this circumvents the need to calculate energy and operator expectation values and is therefore a computationally efficient alternative to variational optimization.

Configuration interaction trained by neural networks: Application to model polyaromatic hydrocarbons.

The main bottleneck of a stochastic or deterministic configuration interaction method is determining the relative weights or importance of each determinant or configuration, which requires large scale matrix diagonalization. Therefore, these methods can be improved significantly from a computational standpoint if the relative importance of each configuration in the ground and excited states of molecular/model systems can be learned using machine learning techniques such as artificial neural networks (ANNs). We have used neural networks to train the configuration interaction coefficients obtained from full configuration interaction and Monte Carlo configuration interaction methods and have tested different input descriptors and outputs to find the more efficient training techniques. These ANNs have been used to calculate the ground states of one- and two-dimensional Heisenberg spin chains along with Heisenberg ladder systems, which are good approximations of polyaromatic hydrocarbons. We find excellent efficiency of training and the model this trained was used to calculate the variational ground state energies of the systems.

In the quest for a stable triplet state in small polyaromatic hydrocarbons: an in silico tool for rational design and prediction.

Combining the roles of spin frustration and geometry of odd and even numbered rings in polyaromatic hydrocarbons (PAHs), we design small molecules that show exceedingly small singlet-triplet gaps and stable triplet ground states. Furthermore, a computationally efficient protocol with a model spin Hamiltonian is shown to be capable of qualitative agreement with respect to high level multireference calculations and therefore, can be used for fast molecular discovery and screening.

Combination of best promoter and micellar catalyst for more than kilo-fold rate acceleration in favor of chromic acid oxidation of D-galactose to D-galactonic acid in aqueous media at room temperature.

Picolinic acid, 2,2'-bipyridine and 1,10-phenanthroline promoted Cr(VI) oxidation of D-galactose to D-galactonic acid in three representative aqueous micellar media has been studied. The anionic surfactant (SDS) accelerated the rate of reaction while the cationic surfactant (CPC) and neutral surfactant (TX-100) retarded the reaction rate. Combination of bipy and SDS is the best choice for chromic acid oxidation of D-galactose to D-galactonic acid in aqueous media although 1,10-phenanthroline is best promoter in absence of micellar catalyst.

Suitable combination of promoter and micellar catalyst for kilo fold rate acceleration on benzaldehyde to benzoic acid conversion in aqueous media at room temperature: a kinetic approach.

The kinetics of oxidation of benzaldehyde by chromic acid in aqueous and aqueous surfactant (sodium dodecyl sulfate, SDS, alkyl phenyl polyethylene glycol, Triton X-100 and N-cetylpyridinium chloride, CPC) media have been investigated in the presence of promoter at 303 K. The pseudo-first-order rate constants (kobs) were determined from a logarithmic plot of absorbance as a function time. The rate constants were found to increase with introduction of heteroaromatic nitrogen base promoters such as Picolinic acid (PA), 2,2'-bipyridine (bipy) and 1,10-phenanthroline (phen). The product benzoic acid has been characterized by conventional melting point experiment, NMR, HRMS and FTIR spectral analysis. The mechanism of both unpromoted and promoted reaction path has been proposed for the reaction. In presence of the anionic surfactant SDS, cationic surfactant CPC and neutral surfactant TX-100 the reaction can undergo simultaneously in both aqueous and micellar phase with an enhanced rate of oxidation in the micellar phase. Both SDS and TX-100 produce normal micellar effect whereas CPC produce reverse micellar effect in the presence of benzaldehyde. The observed net enhancement of rate effects has been explained by considering the hydrophobic and electrostatic interaction between the surfactants and reactants. SDS and bipy combination is the suitable one for benzaldehyde oxidation.

Efficient combination of promoter and catalyst for chromic acid oxidation of propan-2-ol to acetone in aqueous acid media at room temperature.

Oxidation of propan-2-ol to acetone was carried out in aqueous media at room temperature. The effect of promoter (PA, bpy, phen), micellar catalyst (SDS, CPC, TX-100) and their combination has been studied. The reactions were performed under the condition [Propan-2-ol]T≫[Cr(VI)]T at 30°C. Then kobs and half life of all the reaction were determined to identify which promoter and which combination are the most effective for this oxidation. Among the promoters phen accelerates the reaction most in aqueous media. In absence of promoters anionic surfactant SDS increases the rate more effectively than neutral surfactant TX-100. CPC retards the rate in comparison to aqueous media. The rate of the oxidation is highest in presence of the combination of bpy and SDS.