Effect of packing on the cohesive and electronic properties of methanofullerene crystals

"The crystal structure, cohesive energy, and electronic properties of bulk phases of the fullerene derivative [6, 6]-phenyl-C(61)-butyric-acid-methyl-ester (PCBM) have been calculated using ab initio density-functional theory (DFT) techniques. We have only considered cubic and hexagonal crystal lattices with one PCBM molecule per primitive cell. It was found that the cohesive properties of these systems are determined mainly by two types of mechanisms, namely, the van der Waals interaction and the formation of weak hydrogen bonds. Among the considered crystal structures, the most stable one, which is also the most compact structure, is the simple cubic which has a cohesive energy difference of 1.27 eV with respect to the isolated PCBM molecule. Regarding the electronic properties, the simple-cubic PCBM crystal is found to be a semiconductor with an indirect band gap of 1.21 eV. In addition, we have also investigated the electronic contribution of the phenyl-butyric-acid-methyl-ester tail to the electronic states of the entire system. By analyzing the projected density of states (DOS), we found that the states introduced by the tail are too far from the valence and conduction bands, so that the reduction of the band gap of bulk PCBM compared to PCBM molecule results only from the close packing. In addition, the tail introduces a splitting of the degenerate states of the molecule reducing the gap by about 0.2 eV compared to the C(60) molecule. On the other hand, it is shown that the simple hexagonal structure presents a layered structure with the separation between layers of 12.6 angstrom. Furthermore, in the cohesive curve, there is a nonvanishing cohesive energy for noninteracting layers. The study of the hexagonal monolayers shows a stable structure with a cohesive energy of 0.72 eV, which indicates that PCBM can form two-dimensional systems when the PCBM molecules are deposited on the appropriate substrates. The results provided by this work may be important to improve our understanding concerning the mechanisms of formation of PCBM supramolecular structures, and how they can be modified to reach a desired particular property.

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.Comment: 34 pages, 15 figures, comments and suggestions for additional references are welcome

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings—most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science

Naica's "Cueva de los Cristales": Synchrotron radiation characterization of the wall-crystal interface

Naica's "Cueva de los Cristales" was discovered in 2000. It has been considered particularly interesting for its beauty and the challenges it poses to crystallography. This article focuses on the study of the wall-selenite interface by various techniques, particularly X-ray diffraction (XRD), scanning electron microscopy (SEM), with emphasis on micro-X-ray fluorescence (micro-XRF) and micro-X-ray absorption near edge structure (micro-XANES). The main phases calcite, quartz, goethite and montmorillonite were identified by XRD, as well as the association of crystalline and amorphous minor and trace phases of Zn, Mn, Cu, As and Pb. The latter were identified in micro-XRF maps and micro-XANES spectra. The results for the morphology and the chemical description of the crystal-wall interface may contribute to propose a nucleation and growth mechanism for Naica megacrystals

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the upcoming v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development

The density matrix renormalization group applied to an electron-phonon hamiltonian.

Tesis (Maestría en Nanociencias y Nanotecnología)"En esta tesis hemos aplicado el método de renormalización de grupo de la matriz densidad a un Hamiltoniano que introduce grados de libertad fonónicos, con el fin de entender al acoplamiento electrón-fonón que ocurre en los superconductores cerámicos de alta temperatura. El trabajo se basa en resultados previos publicados en el campo [27, 28, 32, 34]. La investigación se enfoca en la energía de estado base y se exploran varios regímenes del acoplamiento electrón-fonón. Los sistemas físicos considerados aquí son cadenas de iones de oxígeno y cobre. Las cadenas de menor tamaño consisten de tres sitios que aparecen como cadenas aisladas en el cerámico YBa2Cu3O7. Aquí se han explorado dos aproximaciones al estudio de sistemas bosónicos, la base fonónica óptima y el método de pseudositios. Como complemento, se ha investigado el caso simple del modelo de Hubbard unidimensional.""In this thesis we apply the density matrix renormalization group2 method to a Hamiltonian which introduces phononic degrees of freedom with the aim of understanding the electron-phonon coupling occurring in ceramic high Tc superconductors. The work is supported on previous published results in the field [27, 28, 32, 34]. The investigation focuses on the ground state energy exploring several regimes of the electron phonon coupling. The physical systems considered here are Oxygen-Copper chains. The lowest size chains are the three site O-Cu-O clusters which appear as isolated chains in YBa2Cu3O7. Here, we explore two approaches for studying boson systems, the Optimal Phonon Basis and the Method of Pseudosites. As a complement, the simplest case of the onedimensional Hubbard model is investigated.