Accelerated searches, made possible by machine learning techniques, are of growing interest in materials discovery. A suitable case involves the solution processing of components that ultimately form thin films of solar cell materials known as hybrid organic-inorganic perovskites (HOIPs). The number of molecular species that combine in solution to form these films constitutes an overwhelmingly large "compositional" space (at times, exceeding 500,000 possible combinations). Selecting a HOIP with desirable characteristics involves choosing different cations, halides, and solvent blends from a diverse palette of options. An unguided search by experimental investigations or molecular simulations is prohibitively expensive. In this work, we propose a Bayesian optimization method that uses an application-specific kernel to overcome challenges where data is scarce, and in which the search space is given by binary variables indicating whether a constituent is present or not. We demonstrate that the proposed approach identifies HOIPs with the targeted maximum intermolecular binding energy between HOIP salt and solvent at considerably lower cost than previous state-of-the-art Bayesian optimization methodology and at a fraction of the time (less than 10%) needed to complete an exhaustive search. We find an optimal composition within 15 ± 10 iterations in a HOIP compositional space containing 72 combinations, and within 31 ± 9 iterations when considering mixed halides (240 combinations). Exhaustive quantum mechanical simulations of all possible combinations were used to validate the optimal prediction from a Bayesian optimization approach. This paper demonstrates the potential of the Bayesian optimization methodology reported here for new materials discovery.

We report the preparation of discrete nanometer-scale zinc-based clusters and use them to form sub-15 nm structures by means of extreme ultraviolet lithography. By taking advantage of a metal-containing building unit derived by a metal–organic frameworkMOF-2, we found the 3-methyl-phenyl-modified Zn-mTA cluster that formed is well-defined with controlled size and structure and demonstrates extremely high solubility. Progress in recent years in metal–organic frameworks has created a rich variety of metal-containing structures that are useful for numerous applications. Substitution of the bridging ligands with monovalent ligands produces a discrete metal–organic cluster that strongly interacts with soft X-rays at a wavelength of 13 nm. Here we describe the design, preparation, computational modeling, and physical characterization of these new materials. Such metal-containing structures may form the basis of photoresists that enable the next generation of microelectronic devices.

Secondary electrons play critical roles in several imaging technologies, including extreme ultraviolet (EUV) lithography. At longer wavelengths of light (e.g. 193 and 248 nm), the photons are directly involved in the photochemistry occurring during photolysis. EUV light (13.5 nm, 92 eV), however, first creates a photoelectron, and this electron, or its subsequent daughter electrons create most of the chemical changes that occur during exposure. Despite the importance of these electrons, the details surrounding the chemical events leading to acid production remain poorly understood. Previously reported experimental results using high PAG-loaded resists have demonstrated that up to five or six photoacids can be generated per incident photon. Until recently, only electron recombination events were thought to play a role in acid generation, requiring that at least as many secondary electrons are produced to yield a given number of acid molecules. However, the initial results we have obtained using a Monte Carlo-based modeling program, LESiS, demonstrate that only two to three secondary electrons are made per absorbed EUV photon. A more comprehensive understanding of EUVinduced acid generation is therefore needed for the development of higher performance resists.

The nudged elastic band (NEB) algorithm is the leading method of calculating transition states in chemical systems. However, the current literature lacks adequate guidance for users wishing to implement a key part of NEB, namely, the optimization method. Here, we provide details of this implementation for the following six gradient descent algorithms: steepest descent, quick-min Verlet, FIRE, conjugate gradient, Broyden-Fletcher-Goldfarb-Shanno (BFGS), and limited-memory BFGS (LBFGS). We also construct and implement a new, accelerated backtracking line search method in concert with a partial Procrustes superimposition to improve upon existing methods. Validation is achieved through benchmark calculations of two test cases, the isomerization of CNX and BOX (where X ∈ {H, Li, Na}) and the study of a conformational change within an alanine dipeptide. We also make direct comparisons to the well-established codebase known as the atomic simulation environment.