From isomorphism to polymorphism: connecting interzeolite transformations to structural and graph similarity

Zeolites are nanoporous crystalline materials with abundant industrial applications. Despite sustained research, only 235 different zeolite frameworks have been realized out of millions of hypothetical ones predicted by computational enumeration. Structure-property relationships in zeolite synthesis are very complex and only marginally understood. Here, we apply structure and graph-based unsupervised machine learning to gain insight on zeolite frameworks and how they relate to experimentally observed polymorphism and phase transformations. We begin by describing zeolite structures using the Smooth Overlap of Atomic Positions method, which clusters crystals with similar cages and density in a way consistent with traditional hand-selected composite building units. To also account for topological differences, zeolite crystals are represented as multigraphs and compared by isomorphism tests. We find that fourteen different pairs and one trio of known frameworks are graph isomorphic. Based on experimental interzeolite conversions and occurrence of competing phases, we propose that the availability of kinetic-controlled transformations between metastable zeolite frameworks is related to their similarity in the graph space. When this description is applied to enumerated structures, over 3,400 hypothetical structures are found to be isomorphic to known frameworks, and thus might be realized from their experimental counterparts. Using a continuous similarity metric, the space of known zeolites shows additional overlaps with experimentally observed phase transformations. Hence, graph-based similarity approaches suggest a venue for realizing novel zeolites from existing ones by providing a relationship between pairwise structure similarity and experimental transformations.Comment: 11 pages, 6 figure

The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures

Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Large-scale analysis of these synthesis procedures would facilitate deeper scientific understanding of materials synthesis and enable automated synthesis planning. Such analysis requires extracting structured representations of synthesis procedures from the raw text as a first step. To facilitate the training and evaluation of synthesis extraction models, we introduce a dataset of 230 synthesis procedures annotated by domain experts with labeled graphs that express the semantics of the synthesis sentences. The nodes in this graph are synthesis operations and their typed arguments, and labeled edges specify relations between the nodes. We describe this new resource in detail and highlight some specific challenges to annotating scientific text with shallow semantic structure. We make the corpus available to the community to promote further research and development of scientific information extraction systems.Comment: Accepted as a long paper at the Linguistic Annotation Workshop (LAW) at ACL 201

Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks

Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated method for connecting scientific literature to synthesis insights. Starting from natural language text, we apply word embeddings from language models, which are fed into a named entity recognition model, upon which a conditional variational autoencoder is trained to generate syntheses for arbitrary materials. We show the potential of this technique by predicting precursors for two perovskite materials, using only training data published over a decade prior to their first reported syntheses. We demonstrate that the model learns representations of materials corresponding to synthesis-related properties, and that the model's behavior complements existing thermodynamic knowledge. Finally, we apply the model to perform synthesizability screening for proposed novel perovskite compounds.Comment: Added new funding support to the acknowledgments section in this versio

From Mission to Competition: The Experiences of 10 LDS Missionary Student-Athletes Returning to Competition in the National Collegiate Athletic Association Division I

The purpose of the current study was to explore the experiences of LDS missionary student-athletes returning to competition in the National Collegiate Athletic Association (NCAA) Division I (DI). Using Consensual Qualitative Research methods (CQR; Hill, 2012) including a semi-structured interview guide, 10 DI student-athletes/returned LDS missionaries were interviewed regarding their experience (i.e., mean age of 25 years; baseball, cross-country/track and field, football, and swimming). A research team with five members constructed four domains and 16 categories representing DI student-athlete/returned LDS missionary chronological identity changes during this experience: (a) the development of an LDS missionary identity; (b) challenges associated with returning to DI student-athlete identity; (c) benefits of mission identity on DI student-athlete identity; and (d) practical implications for sport psychology professionals and other support staff. Suggestions for future research are also given

A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction

[EN] Zeolites are porous, aluminosilicate materials with many industrial and "green" applications. Despite their industrial relevance, many aspects of zeolite synthesis remain poorly understood requiring costly trial and error synthesis. In this paper, we create natural language processing techniques and text markup parsing tools to automatically extract synthesis information and trends from zeolite journal articles. We further engineer a data set of germanium-containing zeolites to test the accuracy of the extracted data and to discover potential opportunities for zeolites containing germanium. We also create a regression model for a zeolite's framework density from the synthesis conditions. This model has a cross-validated root mean squared error of 0.98 T/1000 angstrom(3) , and many of the model decision boundaries correspond to known synthesis heuristics in germanium-containing zeolites. We propose that this automatic data extraction can be applied to many different problems in zeolite synthesis and enable novel zeolite morphologies.We would like to acknowledge funding from the National Science Foundation Award No. 1534340, DMREF that provided support to make this work possible, support from the Office of Naval Research (ONR) under Contract No. N00014-16-1-2432, and the MIT Energy Initiative. Early work was collaborative under the Department of Energy Basic Energy Science Program through the Materials Project under Grant No. EDCBEE. This work has also been supported by the Spanish Government through the Severo Ochoa Program SEV-2016-0683 and the Grant No. MAT2015971261-R, and by La Caxia Foundation through the MIT-SPAIN SEED FUND Program (LCF/PR/MIT17/11820002).Jensen, Z.; Kim, E.; Kwon, S.; Gani, TZ.; Román-Leshkov, Y.; Moliner Marin, M.; Corma Canós, A.... (2019). A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction. ACS Central Science. 5(5):892-899. https://doi.org/10.1021/acscentsci.9b00193S8928995

A Prototype Virginia Ground Station Network

This paper provides a detailed technical description of a prototype ground station network, the Virginia Ground Station Network (VGSN), developed for the Virginia Cubesat Constellation (VCC) mission. Virginia Tech (VT), University of Virginia (UVA), and Old Dominion University (ODU) have each constructed ground stations to communicate with their respective VCC spacecraft. Initially, each university was responsible for commanding its own spacecraft via its own ground station. As the mission progressed, it was decided to network the ground stations and operations centers together to provide backup communications capability for the overall mission. The NASA Wallops Flight Facility (WFF) UHF smallsat ground station was also included in this network. Implementing the VGSN led to the establishment of successful communications with UVA’s Libertas spacecraft via the VT Ground Station (VTGS), demonstrating the utility of collaboration and of the VGSN. This paper provides a technical overview of the VGSN, details concerning signal processing requirements for the mission, a discussion concerning the radio regulatory process as applied to the VCC mission, and plans for future upgrades of the network to continue to support Virginia (and partner institution) small satellite missions

The Highly Energetic Expansion of SN2010bh Associated with GRB 100316D

We present the spectroscopic and photometric evolution of the nearby (z = 0.059) spectroscopically confirmed type Ic supernova, SN 2010bh, associated with the soft, long-duration gamma-ray burst (X-ray flash) GRB 100316D. Intensive follow-up observations of SN 2010bh were performed at the ESO Very Large Telescope (VLT) using the X-shooter and FORS2 instruments. Owing to the detailed temporal coverage and the extended wavelength range (3000--24800 A), we obtained an unprecedentedly rich spectral sequence among the hypernovae, making SN 2010bh one of the best studied representatives of this SN class. We find that SN 2010bh has a more rapid rise to maximum brightness (8.0 +/- 1.0 rest-frame days) and a fainter absolute peak luminosity (L_bol~3e42 erg/s) than previously observed SN events associated with GRBs. Our estimate of the ejected (56)Ni mass is 0.12 +/- 0.02 Msun. From the broad spectral features we measure expansion velocities up to 47,000 km/s, higher than those of SNe 1998bw (GRB 980425) and 2006aj (GRB 060218). Helium absorption lines He I lambda5876 and He I 1.083 microm, blueshifted by ~20,000--30,000 km/s and ~28,000--38,000 km/s, respectively, may be present in the optical spectra. However, the lack of coverage of the He I 2.058 microm line prevents us from confirming such identifications. The nebular spectrum, taken at ~186 days after the explosion, shows a broad but faint [O I] emission at 6340 A. The light-curve shape and photospheric expansion velocities of SN 2010bh suggest that we witnessed a highly energetic explosion with a small ejected mass (E_k ~ 1e52 erg and M_ej ~ 3 Msun). The observed properties of SN 2010bh further extend the heterogeneity of the class of GRB supernovae.Comment: 37 pages and 12 figures (one-column pre-print format), accepted for publication in Ap

A randomized controlled phase III study of VB-111 combined with bevacizumab vs bevacizumab monotherapy in patients with recurrent glioblastoma (GLOBE).

BackgroundOfranergene obadenovec (VB-111) is an anticancer viral therapy that demonstrated in a phase II study a survival benefit for patients with recurrent glioblastoma (rGBM) who were primed with VB-111 monotherapy that was continued after progression with concomitant bevacizumab.MethodsThis pivotal phase III randomized, controlled trial compared the efficacy and safety of upfront combination of VB-111 and bevacizumab versus bevacizumab monotherapy. Patients were randomized 1:1 to receive VB-111 1013 viral particles every 8 weeks in combination with bevacizumab 10 mg/kg every 2 weeks (combination arm) or bevacizumab monotherapy (control arm). The primary endpoint was overall survival (OS), and secondary endpoints were objective response rate (ORR) by Response Assessment in Neuro-Oncology (RANO) criteria and progression-free survival (PFS).ResultsEnrolled were 256 patients at 57 sites. Median exposure to VB-111 was 4 months. The study did not meet its primary or secondary goals. Median OS was 6.8 versus 7.9 months in the combination versus control arm (hazard ratio, 1.20; 95% CI: 0.91-1.59; P = 0.19) and ORR was 27.3% versus 21.9% (P = 0.26). A higher rate of grades 3-5 adverse events was reported in the combination arm (67% vs 40%), mainly attributed to a higher rate of CNS and flu-like/fever events. Trends for improved survival with combination treatment were seen in the subgroup of patients with smaller tumors and in patients who had a posttreatment febrile reaction.ConclusionsIn this study, upfront concomitant administration of VB-111 and bevacizumab failed to improve outcomes in rGBM. Change of treatment regimen, with the lack of VB-111 monotherapy priming, may explain the differences from the favorable phase II results.Clinical trials registrationNCT02511405