De Novo Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen: Part 2.

Organic semiconductors have many desirable properties including improved manufacturing and flexible mechanical properties. Due to the vastness of chemical space, it is essential to efficiently explore chemical space when designing new materials, including through the use of generative techniques. New generative machine learning methods for molecular design continue to be published in the literature at a significant rate but successfully adapting methods to new chemistry and problem domains remains difficult. These challenges necessitate continual method evaluation to probe method viability for use in alternative applications not covered in the original works. In continuation of our previous work, we evaluate four additional machine-learning-based de novo methods for generating molecules with high predicted hole mobility for use in semiconductor applications. The four generative methods evaluated here are (1) Molecule Deep Q-Networks (MolDQN), which utilizes Deep-Q learning to directly optimize molecular structure graphs for desired properties instead of generating SMILES, (2) Graph-based Genetic Algorithm (GraphGA), which uses a genetic algorithm for optimization where crossovers and mutations are defined in terms of RDKit's reaction SMILES, (3) Generative Tensorial Reinforcement Learning (GENTRL), which is a variational autoencoder (VAE) with a learned prior distribution and optimized using reinforcement learning, and (4) Monte Carlo tree search exploration of chemical space in conjunction with a recurrent neural network (RNN) decoder (ChemTS). The generated molecules were evaluated using density functional theory (DFT) and we discovered better performing molecules with the GraphGA method compared to the other approaches.

De Novo Design of Molecules with Low Hole Reorganization Energy Based on a Quarter-Million Molecule DFT Screen.

Materials exhibiting higher mobilities than conventional organic semiconducting materials such as fullerenes and fused thiophenes are in high demand for applications in printed electronics. To discover new molecules in the heteroacene family that might show improved hole mobility, three de novo design methods were applied. Machine learning (ML) models were generated based on previously calculated hole reorganization energies of a quarter million examples of heteroacenes, where the energies were calculated by applying density functional theory (DFT) and a massive cloud computing environment. The three generative methods applied were (1) the continuous space method, where molecular structures are converted into continuous variables by applying the variational autoencoder/decoder technique; (2) the method based on reinforcement learning of SMILES strings (the REINVENT method); and (3) the junction tree variational autoencoder method that directly generates molecular graphs. Among the three methods, the second and third methods succeeded in obtaining chemical structures whose DFT-calculated hole reorganization energy was lower than the lowest energy in the training dataset. This suggests that an extrapolative materials design protocol can be developed by applying generative modeling to a quantitative structure-property relationship (QSPR) utility function.

Encapsulation of carbon chain molecules in single-walled carbon nanotubes.

The vacuum space inside carbon nanotubes offers interesting possibilities for the inclusion, transportation, and functionalization of foreign molecules. Using first-principles density functional calculations, we show that linear carbon-based chain molecules, namely, polyynes (C(m)H(2), m = 4, 6, 10) and the dehydrogenated forms C(10)H and C(10), as well as hexane (C(6)H(14)), can be spontaneously encapsulated in open-ended single-walled carbon nanotubes (SWNTs) with edges that have dangling bonds or that are terminated with hydrogen atoms, as if they were drawn into a vacuum cleaner. The energy gains when C(10)H(2), C(10)H, C(10), C(6)H(2), C(4)H(2), and C(6)H(14) are encapsulated inside a (10,0) zigzag-shaped SWNT are 1.48, 2.04, 2.18, 1.05, 0.55, and 1.48 eV, respectively. When these molecules come inside a much wider (10,10) armchair SWNT along the tube axis, they experience neither an energy gain nor an energy barrier. They experience an energy gain when they approach the tube walls inside. Three hexane molecules can be encapsulated parallel to each other (i.e., nested) inside a (10,10) SWNT, and their energy gain is 1.98 eV. Three hexane molecules can exhibit a rotary motion. One reason for the stability of carbon chain molecules inside SWNTs is the large area of weak wave function overlap. Another reason concerns molecular dependence, that is, the quadrupole-quadrupole interaction in the case of the polyynes and electron charge transfer from the SWNT in the case of the dehydrogenated forms. The very flat potential surface inside an SWNT suggests that friction is quite low, and the space inside SWNTs serves as an ideal environment for the molecular transport of carbon chain molecules. The present theoretical results are certainly consistent with recent experimental results. Moreover, the encapsulation of C(10) makes an SWNT a (purely carbon-made) p-type acceptor. Another interesting possibility associated with the present system is the direction-controlled transport of C(10)H inside an SWNT under an external field. Because C(10)H has an electric dipole moment, it is expected to move under a gradient electric field. Finally, we derive the entropies of linear chain molecules inside and outside an open-ended SWNT to discuss the stability of including linear chain molecules inside an SWNT at finite temperatures.

Formation of Al13I-: evidence for the superhalogen character of Al13.

Al13- is a cluster known for the pronounced stability that arises from coincident closures of its geometric and electronic shells. We present experimental evidence for a very stable cluster corresponding to Al13I-. Ab initio calculations show that the cluster features a structurally unperturbed Al13- core and a region of high charge density on the aluminum vertex opposite from the iodine atom. This ionically bound magic cluster can be understood by considering that Al13 has an electronic structure reminiscent of a halogen atom. Comparisons to polyhalides provide a sound explanation for our chemical observations.