Treating Semiempirical Hamiltonians as Flexible Machine Learning Models Yields Accurate and Interpretable Results.

Quantum chemistry provides chemists with invaluable information, but the high computational cost limits the size and type of systems that can be studied. Machine learning (ML) has emerged as a means to dramatically lower the cost while maintaining high accuracy. However, ML models often sacrifice interpretability by using components such as the artificial neural networks of deep learning that function as black boxes.

A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians.

Current neural networks for predictions of molecular properties use quantum chemistry only as a source of training data. This paper explores models that use quantum chemistry as an integral part of the prediction process. This is done by implementing self-consistent-charge Density-Functional-Tight-Binding (DFTB) theory as a layer for use in deep learning models.

Constant size descriptors for accurate machine learning models of molecular properties.

Two different classes of molecular representations for use in machine learning of thermodynamic and electronic properties are studied. The representations are evaluated by monitoring the performance of linear and kernel ridge regression models on well-studied data sets of small organic molecules. One class of representations studied here counts the occurrence of bonding patterns in the molecule.

A Least-Squares Commutator in the Iterative Subspace Method for Accelerating Self-Consistent Field Convergence.

A least-squares commutator in the iterative subspace (LCIIS) approach is explored for accelerating self-consistent field (SCF) calculations. LCIIS is similar to direct inversion of the iterative subspace (DIIS) methods in that the next iterate of the density matrix is obtained as a linear combination of past iterates. However, whereas DIIS methods find the linear combination by minimizing a sum of error vectors, LCIIS minimizes the Frobenius norm of the commutator between the density matrix and the Fock matrix.

Modeling Field-Induced Quenching in Poly(p-phenylene vinylene) Polymers and Oligomers.

Field-induced fluorescence quenching of poly(p-phenylene vinylene) (PPV) oligomers due to nonradiative relaxation through free electron-hole pair (FEHP) states is modeled using singles configuration interaction computations with the intermediate neglect of differential overlap Hamiltonian. The computations find FEHP states with energies that drop linearly with applied field and undergo avoided crossings with the fluorescent state. The coupling between the FEHP and fluorescent state, computed for multiple FEHP states on a variety of oligomer lengths, is found to depend primarily on the field strength required for the state to cross the fluorescent state.

Brownian dynamics simulations of charge mobility on conjugated polymers in solution.

A model is developed for the mobility of a charge carrier along a conjugated polymer dissolved in solution, as measured by time-resolved microwave conductivity. Each unit cell of the polymer is assigned a torsional degree of freedom, with Brownian dynamics used to include the effects of solvent on the torsions. The barrier to torsional motion is substantially enhanced in the vicinity of the charge, leading to self-trapping of the charge onto a planarized region of the polymer chain.

Using Molecular Similarity to Develop Reliable Models of Chemical Reactions in Complex Environments.

The use of molecular similarity to develop reliable low-cost quantum mechanical models for use in quantum mechanical/molecular mechanical simulations of chemical reactions is explored, using the H + HF → H2 + F collinear reaction as a test case. The approach first generates detailed quantum chemical data for the reaction center in geometries and electrostatic environments that span those expected to arise during the molecular dynamics simulations. For each geometry and environment, both high- and low-level ab initio calculations are performed.

Computational design of a light-driven molecular motor.

Light-driven molecular motors may be useful for nanotechnology applications. The possibility of building such a motor based on the tolane framework is explored here. In the ground electronic state of tolane, the barrier to internal rotation is comparable to room temperature thermal energies, k(B)T.

Modeling outer-sphere disorder in the symmetry breaking of PPV.

Disorder plays an important role in the photophysics of conjugated polymers such as poly(para-phenylene vinylene) (PPV). The dipole moments measured by electroabsorption spectroscopy for a centrosymmetric system such as PPV provide a direct quantitative measure of disorder-induced symmetry breaking. Although inner-sphere (structural) disorder is present, outer-sphere (environmental) disorder dominates the symmetry breaking in PPV.