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Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials.

Graph neural networks (GNNs) have recently been used to learn the representations of crystal structures through an end-to-end data-driven approach. However, a systematic top-down approach to evaluate and understand the limitations of GNNs in accurately capturing crystal structures has yet to be established. In this study, we introduce an approach using human-designed descriptors as a compendium of human knowledge to investigate the extent to which GNNs can comprehend crystal structures. Our findings reveal that current state-of-the-art GNNs fall short in accurately capturing the periodicity of crystal structures. We analyze this failure by exploring three aspects: local expressive power, long-range information processing, and readout function. To address these identified limitations, we propose a straightforward and general solution: the hybridization of descriptors with GNNs, which directly supplements the missing information to GNNs. The hybridization enhances the predictive accuracy of GNNs for specific material properties, most notably phonon internal energy and heat capacity, which heavily rely on the periodicity of materials.

Solid polymer electrolytes (SPEs) have the potential to improve lithium-ion batteries by enhancing safety and enabling higher energy densities. However, SPEs suffer from significantly lower ionic conductivity than liquid and solid ceramic electrolytes, limiting their adoption in functional batteries. To facilitate more rapid discovery of high ionic conductivity SPEs, we developed a chemistry-informed machine learning model that accurately predicts ionic conductivity of SPEs. The model was trained on SPE ionic conductivity data from hundreds of experimental publications. Our chemistry-informed model encodes the Arrhenius equation, which describes temperature activated processes, into the readout layer of a state-of-the-art message passing neural network and has significantly improved accuracy over models that do not encode temperature dependence. Chemically informed readout layers are compatible with deep learning for other property prediction tasks and are especially useful where limited training data are available. Using the trained model, ionic conductivity values were predicted for several thousand candidate SPE formulations, allowing us to identify promising candidate SPEs. We also generated predictions for several different anions in poly(ethylene oxide) and poly(trimethylene carbonate), demonstrating the utility of our model in identifying descriptors for SPE ionic conductivity.

Generating a data set that is representative of the accessible configuration space of a molecular system is crucial for the robustness of machine learned interatomic potentials (MLIP). However, the complexity of molecular systems, characterized by intricate potential energy surfaces (PESs) with numerous local minima and energy barriers, presents a significant challenge. Traditional methods of data generation, such as random sampling or exhaustive exploration, are either intractable or may not capture rare, but highly informative configurations. In this study, we propose a method that leverages uncertainty as the collective variable (CV) to guide the acquisition of chemically-relevant data points, focusing on regions of the configuration space where ML model predictions are most uncertain. This approach employs a Gaussian Mixture Model-based uncertainty metric from a single model as the CV for biased molecular dynamics simulations. The effectiveness of our approach in overcoming energy barriers and exploring unseen energy minima, thereby enhancing the data set in an active learning framework, is demonstrated on the alanine dipeptide benchmark system., Comment: 13 pages, 4 figures, 10 pages of Supplementary Information

This paper evaluates the effectiveness and safety of XEN63 stent, either standalone or in combination with phacoemulsification, in patients with primary open-angle glaucoma (POAG). Eighty eyes from 80 patients with medically uncontrolled POAG were assigned to undergo XEN63 implant. The primary outcome was the surgical success, defined as an intraocular pressure (IOP) lowering from preoperative values ≥ 20% and an IOP absolute value between 6 and 18 mmHg, with or without antiglaucoma medications. Forty-three (53.7%) eyes underwent XEN63-standalone and 37(46.2%) eyes a XEN63 + Phacoemulsification procedure. Success rate was 68.8% (55/80) eyes in the overall study sample, 69.8% (30/43) eyes in the XEN63-standalone group; and 67.6% (25/37) eyes in the XEN63 + Phaco group (p = 0.6133). Preoperative IOP was significantly lowered from 22.1 ± 4.9 mmHg and 19.8 ± 3.7 mmHg to 14.7 ± 5.3 mmHg and 13.8 ± 3.4 mmHg in the XEN63-standalone and XEN63 + Phaco groups, respectively (p < 0.0001 each, respectively); without significant differences between them at any of the time-points measured. Preoperative number of ocular-hypotensive drugs was significantly reduced from 2.3 ± 0.8 to 0.3 ± 0.7 drugs, from 2.5 ± 0.7 to 0.3 ± 0.7 drugs; and from 2.0 ± 0.8 to 0.3 ± 0.7 drugs, in the overall, XEN63-standalone, and XEN63 + Phaco groups, respectively. Regarding safety, 3(42.5%) eyes had transient hypotony at some point during the study, although only in one (1.2%) eye was clinically significant. Four (5.0%) eyes underwent a needling, 4 (5.0%) eyes underwent surgical-bleb-revision, 1 (1.2%) eye required a device replacement and 1 (1.2%) eye a device removal due to maculopathy. XEN63, either alone or in combination with phacoemulsification, significantly lowered IOP and reduced the number of ocular hypotensive medications. The rate of ocular hypotony was relatively high, although it was clinically relevant only in one eye., Depto. de Inmunología, Oftalmología y ORL, Unidad Docente de Inmunología, Oftalmología y ORL, Fac. de Óptica y Optometría, TRUE, pub

Purpose: Food waste is a latent problem for society, one of the main causes being poor management of the operating processes within the food industry, which is the main reason the present research aimed to reduce raw material waste in a nectar factory. Design/methodology/approach: The methodology led to carrying out an experimental investigation where a Green Lean Six Sigma Model was applied, whose foundation was the use of tools such as Environmental Value Stream Mapping (E - VSM), DMAIC (Define - Measure - Analyze - Improve - Control), Poka Yoke and DOE (Design of Experiments), the data obtained were exposed to an inferential statistical analysis using tests such as Anderson - Darling and T-Student. Findings: As a final result, a waste reduction of 2.23% was obtained, which is equivalent to approximately 120 kg of useful raw material, in addition the environmental impact was reduced by 2.2%, waste costs were also reduced by approximately 5.5% and produced an increase in global productivity by 2.4%. Research limitations/implications: It is shown that the application of methodologies such as Green, Lean and Six Sigma in the food industry present benefits during and after the application of the model; such as preventive control of waste, standardization in processes; benefits that, in coordination with an organizational culture of continuous improvement, can significantly improve the current state of companies. Practical implications: The positive obtained results support the methodology proposed by the authors to reduce the waste of agricultural raw material. It is expected to be a benchmark for other similar organizations that seek to generate greater productivity in line with the search for environmentally sustainable operations. Originality/value: This is one of the first studies that aims to reduce the waste of raw materials in food processing companies, considering aspects of mitigating environmental impact, Peer Reviewed

Integrated Science, Technology, Engineering, and Mathematics (STEM) education is recognised as the latest development stage on the pathway to a highly capable future workforce, and is thus linked to a nation’s future development and prosperity. However, despite various appeals and efforts by the Australian Government, effective processes for constructing relevant educational programs within local curricula remain under-researched. A comprehensive review of the design, and design-led education literature, and of related attempts to develop suitable instructional content and approaches, has been conducted. Its outcome is a recommendation to adopt a design-based pedagogy utilising 3D printing for the development and implementation of integrated STEM programs. Accordingly, this paper proposes a new area of research that requires a deep understanding of appropriate design processes, skills, and mindsets, for integrated STEM education. This study also prototypes a design-led conceptual framework to assist educators in developing integrated STEM programs. Based on the learning theory of Distributed Cognition and the Four-component Instructional Design (4C/ID) model, this conceptual framework begins to address an urgent need to prioritise methodologically rigorous evidence-based research on integrated STEM education. It also has the potential to lead to the formation and collaboration of STEM education communities of teachers, students, and researchers as external instructional leaders to co-develop design-led integrated STEM curriculum.

The role of design in the exploration of integrated Science, Technology, Engineering, and Mathematics (STEM) education has rapidly expanded in recent years. Design has made an important contribution by providing instructional scaffolds in synthesising knowledge from multiple disciplines to solve real-world problems. Despite the potential of Design-based Pedagogy (DBP), there is a paucity of applicable DBP process models for constructing integrated STEM tasks and formulating implementation approaches. To address this issue, we enquire into the Solution-based Design Process (SBDP) that reconciles curriculum content knowledge and teaching through authentic problem finding and solving. Through an exploratory study of a self-critical reflection in autoethnographic design practice, we explore how the optimised SBDP can inform (i) The design skills and mindsets of DBP for integrated STEM education, and (ii) The instructional processes for constructing and implementing integrated STEM tasks involving design models. As found in this study, a unique feature of the Solution-based DBP is that it relates to the processes of thinking and reasoning—highly cognitive and logical; its application requires the skilled operation of 3D printing-based digital fabrication. Additionally, we demonstrated a Solution-based DBP, combined with 3D printing-based digital fabrication, and providing teachers with a structural blueprint for developing integrated STEM programs. Overall, this research contributes to the corpus of methodologically rigorous and evidence-based studies on design-led integrated STEM curriculums in the context of a specific country’s education system.

The impacts of Covid-19 pandemic on the Australian health system have been significant, but the past eighteen months have also demonstrated our healthcare practitioners’ immense resilience and ability to deliver services in the face of severe and persistent disruptions. Despite this, many opportunities for improvement, innovation and redesign have been uncovered over this period. One such area of emerging global concern has been the impact of personal protective equipment (PPE) on healthcare workers from prolonged use. Specifically, there are growing reports of discomfort and irritation experienced by frontline healthcare workers after the implementation of stringent PPE protocols in clinical settings. In response to similar reports arising in Queensland, the government committed to undertake research in collaboration with the frontline workforce to improve personal protective equipment design to mitigate these risks where possible. This priority was taken up by the Healthcare Excellence Accelerator (HEAL) Bridge Lab which is a collaboration program between Queensland Health and the QUT Design Lab. The Bridge Labs, initiated by Clinical Excellence Queensland’s (CEQ) Healthcare Improvement Unit (HIU) were established to amplify our health system’s ability to innovate rapidly around emerging improvement priorities. A collaborative front-line focused quality improvement project was developed and led by team from the QUT Design Lab comprising of Associate Professor Marianella Chamorro-Koc (Program Co-Lead Design for Health, QUT Design Lab), Associate Professor Rafael Gomez (School of Design, QUT) and Ms Erina Wannenburg (Research Associate and Project Manager, QUT Design Lab). The project was supported internally by Dr Satyan Chari (Program Director – CEQ Bridge Labs), Ms Linda McCormack (Clinical Director, HIU) and Ms Anna Wesselman (Project officer – HIU). The project utilised a mixed-methods iterative approach comprising of an initial rapid review of the pu

Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization., QC 20220407

[ES] En la presente invención, se describe un nuevo procedimiento de síntesis del material intercrecido CHA/AEI en su forma zeolítica utilizando un único agente director de estructura orgánico (ADEO), mucho más sencillo que las mezclas descritas hasta el momento, y en ausencia de aniones fluoruro en el medio de síntesis. En la presente invención también se describe el material obtenido con dicho procedimiento y su uso como catalizador en diversos procesos catalíticos, como metanol a olefinas, y la reducción catalítica selectiva (RCS) de distintos óxidos de nitrógeno (NOx) en emisiones gaseosas., [EN] The present invention relates to a new method for synthesising CHA/AEI intergrown material in its zeolitic form using a single organic structure-directing agent (OSDA), which is much simpler than previously described mixtures, and in which the synthesis medium is free of fluoride anions. The present invention also relates to the material obtained with said method and the use thereof as a catalyst in various catalytic processes, such as the methanol-to-olefin process, and the selective catalytic reduction (SCR) of different nitrogen oxides (NOx) in gaseous emissions., [FR] La présente invention porte sur un nouveau procédé de synthèse du matériau entrecroisé CHA/AEI dans sa forme zéolithique au moyen d'un agent directeur unique de structure organique (ADEO), beaucoup plus simple que les mélanges décrits jusqu'à ce jour, et en l'absence d'anions fluorure dans le milieu de synthèse. L'invention porte également sur le matériau obtenu au moyen dudit procédé et sur son utilisation comme cstalyseur dans divers processus catalytiques, comme la conversion de méthanol en oléfines et la réduction catalytique sélective (RCS) de différents oxydes d'azote (NOx) en émissions gazeuses.

Molecular dynamics (MD) simulation techniques are widely used for various natural science applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab-initio simulations by predicting forces directly from atomic structures. Despite significant progress in this area, such techniques are primarily benchmarked by their force/energy prediction errors, even though the practical use case would be to produce realistic MD trajectories. We aim to fill this gap by introducing a novel benchmark suite for learned MD simulation. We curate representative MD systems, including water, organic molecules, a peptide, and materials, and design evaluation metrics corresponding to the scientific objectives of respective systems. We benchmark a collection of state-of-the-art (SOTA) ML FF models and illustrate, in particular, how the commonly benchmarked force accuracy is not well aligned with relevant simulation metrics. We demonstrate when and how selected SOTA methods fail, along with offering directions for further improvement. Specifically, we identify stability as a key metric for ML models to improve. Our benchmark suite comes with a comprehensive open-source codebase for training and simulation with ML FFs to facilitate future work., Comment: 31 pages, 18 figures

Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceeds through rotation mediated by intersystem crossing, and incorporate this mechanism into our automated workflow. We use our approach to predict the thermal half-lives of 19,000 azobenzene derivatives. We explore trends and tradeoffs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology.

Zeolites span a large variety of microporous crystal structures, making them useful materials for catalysis and separations. However, controlling phase competition in their synthesis often requires organic structure-directing agents (OSDAs) to selectively crystallize the desired topologies. Whereas computational design of OSDAs can help in selecting adequate candidates for zeolite synthesis, machine-generated templates are often complex or expensive. In this work, we use shape and binding metrics to propose templates for over 100 zeolites and to rationalize dual-OSDA approaches. Starting from OSDAs from the literature, promising templates were selected for zeolites ranging from clathrasil frameworks to extra large-pore structures. Selectivity maps derived from phase competition metrics show that small- and medium-pore zeolites tend to be more shape-selective toward their templates than their large-pore counterparts. Finally, shape and volume descriptors allow identification of OSDAs that may act as synergistic pore-fillers for different cavities of zeolites. The application of this theory is demonstrated for the case of the KFI zeolite, which may be synthesized using tetraethylammonium and OSDAs repurposed from high-silica LTA synthesis as dual OSDAs. This work may help in the discovery of new synthesis routes for known zeolites using shape descriptors and repurposed OSDAs.

A novel ab initio methodology based on high-throughput simulations has permitted designing unique biselective organic structure-directing agents (OSDAs) that allow the efficient synthesis of CHA/AEI zeolite intergrowth materials with controlled phase compositions. Distinctive local crystallographic ordering of the CHA/AEI intergrowths was revealed at the nanoscale level using integrated differential phase contrast scanning transmission electron microscopy (iDPC STEM). These novel CHA/AEI materials have been tested for the selective catalytic reduction (SCR) of NOx, presenting an outstanding catalytic performance and hydrothermal stability, even surpassing the performance of the well-established commercial CHA-type catalyst. This methodology opens the possibility for synthetizing new zeolite intergrowths with more complex structures and unique catalytic properties.

Data-driven approaches based on high-throughput capabilities and machine learning hold promise in revolutionizing human-centred materials discovery for sustainability and decarbonization. This Review examines the strengths and limitations of different traditional and emerging approaches to demonstrate their inherent connection and highlight the evolving paradigms of materials design., Breakthroughs in molecular and materials discovery require meaningful outliers to be identified in existing trends. As knowledge accumulates, the inherent bias of human intuition makes it harder to elucidate increasingly opaque chemical and physical principles. Moreover, given the limited manual and intellectual throughput of investigators, these principles cannot be efficiently applied to design new materials across a vast chemical space. Many data-driven approaches, following advances in high-throughput capabilities and machine learning, have tackled these limitations. In this Review, we compare traditional, human-centred methods with state-of-the-art, data-driven approaches to molecular and materials discovery. We first introduce the limitations of human-centred Edisonian, model-system and descriptor-based approaches. We then discuss how data-driven approaches can address these limitations by promoting throughput, reducing cognitive overload and biases, and establishing atomistic understanding that is transferable across a broad chemical space. We examine how high-throughput capabilities can be combined with active learning and inverse design to efficiently optimize materials out of millions or an intractable number of candidates. Lastly, we pinpoint challenges to accelerate future workflows and ultimately enable self-driving platforms, which automate and streamline the optimization of molecules and materials in iterative cycles.

Optical properties are central to molecular design for many applications, including solar cells and biomedical imaging. A variety of ab initio and statistical methods have been developed for their prediction, each with a trade-off between accuracy, generality, and cost. Existing theoretical methods such as time-dependent density functional theory (TD-DFT) are generalizable across chemical space because of their robust physics-based foundations but still exhibit random and systematic errors with respect to experiment despite their high computational cost. Statistical methods can achieve high accuracy at a lower cost, but data sparsity and unoptimized molecule and solvent representations often limit their ability to generalize. Here, we utilize directed message passing neural networks (DMPNNs) to represent both dye molecules and solvents for predictions of molecular absorption peaks in solution. Additionally, we demonstrate a multi-fidelity approach based on an auxiliary model trained on over 28,000 TD-DFT calculations that further improves accuracy and generalizability, as shown through rigorous splitting strategies. Combining several openly-available experimental datasets, we benchmark these methods against a state-of-the-art regression tree algorithm and compare the DMPNN solvent representation to several alternatives. Finally, we explore the interpretability of the learned representations using dimensionality reduction and evaluate the use of ensemble variance as an estimator of the epistemic uncertainty in our predictions of molecular peak absorption in solution. The prediction methods proposed herein can be integrated with active learning, generative modeling, and experimental workflows to enable the more rapid design of molecules with targeted optical properties., National Science Foundation (Grant 1745302)

En la presente invención, se describe un nuevo procedimiento de síntesis del material intercrecido CHA/AEI en su forma zeolítica utilizando un único agente director de estructura orgánico (ADEO), mucho más sencillo que las mezclas descritas hasta el momento, y en ausencia de aniones fluoruro en el medio de síntesis. En la presente invención también se describe el material obtenido con dicho procedimiento y su uso como catalizador en diversos procesos catalíticos, como metanol a olefinas, y la reducción catalítica selectiva (RCS) de distintos óxidos de nitrógeno (NOx) en emisiones gaseosas

Thinking into Being explores QUT’s unique cross-disciplinary and collaborative approach to teaching and learning. The exhibition brings together work by QUT graduates from the Schools of Architecture and Built Environment, Creative Practice, and Design, who have become leading creative practitioners both nationally and internationally. Thinking into Being is a wide-ranging exploration of the often unseen creative processes that bring into being the objects, products and experiences of our culture. The exhibition reveals how these processes may bring about social, political, ecological, and economic change—now and into the future.

In hospitals admissions, tiredness, business, and the heightened emotions of a child in pain might influence the nurse and clinician’s accurate assessment of a child in pain. Assessing pain in paediatric environment is a core task that is currently informed by a 1 to 10 scale or a happy to sad faces. The literature identifies paediatric pain as a factor that impacts negatively in the entire healthcare experience and recovery rates of the paediatric patient. This project is positioned in the context of Admissions to the Emergency room, in that initial stage or touchpoint when the nurse asks a child about how much pain they have. People experience and express pain differently, and in the context of the admissions to the emergency room, children might not respond to the protocol of questions accurately. TAME is an interactive research prototype that employs sensors to capture 2 types of pain vitals in low acuity scenarios of care. TAME's functionality aims to enhance the decision making of the clinician at that moment, facilitating more accurate and effective manner to empathise with and understand how much pain the child is having, and by transforming the pain assessment moment in a more positive experience for the child and the clinician.

Personal protective equipment (PPE) has become ubiquitous terminology in 2020 due to the Covid-19 global pandemic. Yet, within the midst of supply shortages, access and efficacy debates of healthcare worker PPE, the voice of one group has remained largely silent – children. PPE can be scary, unfriendly, and confronting for children. This can have a significant impact upon doctors, nurses, and allied health staff’s ability to build rapport with children and their families and ultimately supporting their healthcare needs. Rapport is essential for Health Care Professionals (HCPs) to build to conduct thorough clinical assessment, positioning child-friendly PPE as “more than a cute thing to do”. Understanding the views, experiences and preferences of children and their families regarding PPE is lacking. Previous studies have explored the wearing of uniforms (casual versus formal); use of face shields in preference to surgical face masks; and the colours and patterns which may be seen as less intimidating and authoritative to children. However, the cumulative level of PPE which the pandemic has seen applied (face mask, googles, face shield and gown) present a significant variation and is likely to further affect the ability of HCPs to build a safe, trusting and welcoming relationship with children and their families. There is also suggestion that PPE is significantly underutilised in paediatric settings – due to the perceived or actual impediment on clinical assessment and should be addressed at a broader level, and within a healthcare sector beyond the immediate pandemic. As such, an improved design for PPE will be useful for now and in the future within the healthcare context.

In academia there is a strong push to forge meaningful partnerships with external industry and community. There are various reasons for this including to help generate income, expand networks, increase institutional reputation – but importantly it is also to try and make sure that the research conducted has practical and real-world impacts that tackle important social challenges and drive economic growth. However this is challenging territory in both corporate and not-for-profit (NFP) sectors. For corporate partnerships, the two sides – academic and business – have very distinct cultures and different incentives for establishing partnerships. For NFP partners, while the incentive to contribute to public good is shared, the challenges may be operational, e.g. funding to keep abreast and in the lead of latest technological innovation. Academics are not often trained in how to effectively engage and manage these relationships, and organisations are often unaware of the relevance of academic research. So, as design academics we asked; can we leverage a human-centred approach to help create more effective university-industry collaborations? By sharing three case studies, and contextualising our reflections within a co-design model, we hope to provide academics with useful insights to help establish, build and manage these important collaborations in the future.

PPE can be scary, unfriendly, and confronting for children (1). This can have a significant impact upon doctors, nurses, and allied health staff’s ability to build rapport with children and their families. Rapport is essential for Health Care Professionals (HCPs) to build a trusting relationship with the patient and to conduct thorough clinical assessment, positioning child-friendly PPE as “more than a cute thing to do”. Understanding the views, experiences and preferences of children and their families regarding PPE is lacking.

Synopsis of 17 projects funded by the Health Excellence AcceLerator and presented at the HEAL Symposium, which showcased the outcomes of bringing Design Thinking to Queensland Health in 2020-2021.

En la presente invención, se describe un nuevo procedimiento de síntesis del material intercrecido CHA/AEI en su forma zeolítica utilizando un único agente director de estructura orgánico (ADEI), mucho más sencillo que las mezclas descritas hasta el momento, y en ausencia de aniones fluoruro en el medio de síntesis. En la presente invención también se describe en el material obtenido con dicho procedimiento y su uso como catalizador en diversos procesos catalíticos, como metanol a olefinas, y la reducción catalítica selectiva (RCS) de distintos óxidos de nitrógenos (NOx) en emisiones gaseosas.

Organic structure directing agents (OSDAs) play a crucial role in the synthesis of micro-and mesoporous materials especially in the case of zeolites. Despite the wide use of OSDAs, their interaction with zeolite frameworks is poorly understood, with researchers relying on synthesis heuristics or computationally expensive techniques to predict whether an organic molecule can act as an OSDA for a certain zeolite. In this paper, we undertake a data-driven approach to unearth generalized OSDA-zeolite relationships using a comprehensive database comprising of 5,663 synthesis routes for porous materials. To generate this comprehensive database, we use natural language processing and text mining techniques to extract OSDAs, zeolite phases, and gel chemistry from the scientific literature published between 1966 and 2020. Through structural featurization of the OSDAs using weighted holistic invariant molecular (WHIM) descriptors, we relate OSDAs described in the literature to different types of cage-based, small-pore zeolites. Lastly, we adapt a generative neural network capable of suggesting new molecules as potential OSDAs for a given zeolite structure and gel chemistry. We apply this model to CHA and SFW zeolites generating several alternative OSDA candidates to those currently used in practice. These molecules are further vetted with molecular mechanics simulations to show the model generates physically meaningful predictions. Our model can automatically explore the OSDA space, reducing the amount of simulation or experimentation needed to find new OSDA candidates.

Zeolites are versatile catalysts and molecular sieves with large topological diversity, but managing phase competition in zeolite synthesis is an empirical, labor-intensive task. In this work, we controlled phase selectivity in templated zeolite synthesis from first principles by combining high-throughput atomistic simulations, literature mining, human-computer interaction, synthesis, and characterization. Proposed binding metrics distilled from more than 586,000 zeolite-molecule simulations reproduced the extracted literature and rationalized framework competition in the design of organic structure-directing agents. Energetic, geometric, and electrostatic descriptors of template molecules were found to regulate synthetic accessibility windows and aluminum distributions in pure-phase zeolites. Furthermore, these parameters allowed us to realize an intergrowth zeolite through a single bi-selective template. The computation-first approach enables control of both zeolite synthesis and structure composition using a priori theoretical descriptors.

Zeolites are inorganic materials with wide industrial applications due to their topological diversity. Tailoring confinement effects in zeolite pores, for instance by crystallizing intergrown frameworks, can improve their catalytic and transport properties, but controlling zeolite crystallization often relies on heuristics. In this work, we use computational simulations and data mining to design organic structure-directing agents (OSDAs) to favor the synthesis of intergrown zeolites. First, we propose design principles to identify OSDAs which are selective toward both end members of the disordered structure. Then, we mine a database of hundreds of thousands of zeolite-OSDA pairs and downselect OSDA candidates to synthesize known intergrowth zeolites such as CHA/AFX, MTT/TON, and BEC/ISV. The computationally designed OSDAs balance phase competition metrics and shape selectivity toward the frameworks, thus bypassing expensive dual-OSDA approaches typically used in the synthesis of intergrowths. Finally, we propose potential OSDAs to obtain hypothesized disordered frameworks such as AEI/SAV. This work may accelerate zeolite discovery through data-driven synthesis optimization and design.

The spacecraft in which we have traveled for eight months has just landed gently on the surface. Passengers stared at each other in disbelief, still feeling numb from the brutality of the supersonic retropropulsion during the braked descent. It seems unbelievable... We are on Mars! Many of us have arrived in recent years. Thousands upon thousands of people traveling from our comfortable blue home to the uncertain red dream. The promise of a new world. An opportunity to rethink everything, in order not to repeat the same mistakes. Beyond the polarized glass windows, the light of the Martian dawn spills over Tempe Mensa. The Sun is a small golden circle, surrounded by two bright points: Venus and the Earth. A few kilometers from the spaceport, the gigantic extensions of the greenhouses domes are outlined. Beyond, the land descends steeply down the south-facing cliff. On its vertical walls awaits Nüwa, the wonderful city that leads the accelerated settlement of Mars. Its name recalls the goddess who protects humans. And, certainly, the city protects and provides us with everything we need. Now, I’ll become part of Mars; I will dissolve into Nüwa, and join all those that give life to it. And we will become Nüwa, forever

The frames of reference model developed by Fox, and extended by a number of other authors, is arguably the central paradigm framework in the employment/industrial relations field. Despite its importance and popularity, use of frames of reference to structure empirical analysis and develop hypotheses is relatively rare and, to the best of our knowledge, the framework and its key constructs and principles have themselves never been empirically examined with data from a representative cross-section of workplaces using quantitative methods. This article, with the aid of a new four-country (Australia, Canada, UK, and US) survey data set on 7000+ workplaces, initiates this kind of empirical study. The frames of reference distinguish three main types of employment relationships: unitarist, pluralist, radical. We select six attitudinal/behavioral indicators from the data set that distinguish which frame a workplace is in, combine them to form a Relational Quality Index, plot the 7000+ Relational Quality Index observations as four-country frequency distributions, and use different statistical criteria to indicate the relative size of each frame. We next do regression analysis in which the 7000+ workplace Relational Quality Index scores are the dependent variable and construct from the data set 20 frames of reference explanatory variables. As theory predicts, workplaces with stronger common (opposed) interests have better (worse) employer–employee relations. © Australian Labour and Employment Relations Association (ALERA) 2020 SAGE Publications Ltd, Los Angeles, London, New Delhi, Singapore and Washington DC.

En la presente invención, se describe un nuevo procedimiento de síntesis del material intercrecido CHA/AEI en su forma zeolítica utilizando un único agente director de estructura orgánico (ADEO), mucho más sencillo que las mezclas descritas hasta el momento, y en ausencia de aniones fluoruro en el medio de síntesis. En la presente invención también se describe el material obtenido con dicho procedimiento y su uso como catalizador en diversos procesos catalíticos, como metanol a olefinas, y la reducción catalítica selectiva (RCS) de distintos óxidos de nitrógeno (NOx) en emisiones gaseosas

Predicting molecular conformations (or 3D structures) from molecular graphs is a fundamental problem in many applications. Most existing approaches are usually divided into two steps by first predicting the distances between atoms and then generating a 3D structure through optimizing a distance geometry problem. However, the distances predicted with such two-stage approaches may not be able to consistently preserve the geometry of local atomic neighborhoods, making the generated structures unsatisfying. In this paper, we propose an end-to-end solution for molecular conformation prediction called ConfVAE based on the conditional variational autoencoder framework. Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program. Extensive experiments on several benchmark data sets prove the effectiveness of our proposed approach over existing state-of-the-art approaches. Code is available at \url{https://github.com/MinkaiXu/ConfVAE-ICML21}., Comment: Accepted by ICML 2021

Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology. Large scale screening of polymer electrolytes is hindered by the significant cost of molecular dynamics (MD) simulation in amorphous systems: the amorphous structure of polymers requires multiple, repeated sampling to reduce noise and the slow relaxation requires long simulation time for convergence. Here, we accelerate the screening with a multi-task graph neural network that learns from a large amount of noisy, unconverged, short MD data and a small number of converged, long MD data. We achieve accurate predictions of 4 different converged properties and screen a space of 6247 polymers that is orders of magnitude larger than previous computational studies. Further, we extract several design principles for polymer electrolytes and provide an open dataset for the community. Our approach could be applicable to a broad class of material discovery problems that involve the simulation of complex, amorphous materials., Comment: 29 pages, 6 figures + supplementary information

The frames of reference model developed by Fox, and extended by a number of other authors, is arguably the central paradigm framework in the employment/industrial relations field. Despite its importance and popularity, use of frames of reference to structure empirical analysis and develop hypotheses is relatively rare and, to the best of our knowledge, the framework and its key constructs and principles have themselves never been empirically examined with data from a representative cross-section of workplaces using quantitative methods. This article, with the aid of a new four-country (Australia, Canada, UK, and US) survey data set on 7000+ workplaces, initiates this kind of empirical study. The frames of reference distinguish three main types of employment relationships: unitarist, pluralist, radical. We select six attitudinal/behavioral indicators from the data set that distinguish which frame a workplace is in, combine them to form a Relational Quality Index, plot the 7000+ Relational Quality Index observations as four-country frequency distributions, and use different statistical criteria to indicate the relative size of each frame. We next do regression analysis in which the 7000+ workplace Relational Quality Index scores are the dependent variable and construct from the data set 20 frames of reference explanatory variables. As theory predicts, workplaces with stronger common (opposed) interests have better (worse) employer–employee relations. © Australian Labour and Employment Relations Association (ALERA) 2020 SAGE Publications Ltd, Los Angeles, London, New Delhi, Singapore and Washington DC.

The frames of reference model developed by Fox, and extended by a number of other authors, is arguably the central paradigm framework in the employment/industrial relations field. Despite its importance and popularity, use of frames of reference to structure empirical analysis and develop hypotheses is relatively rare and, to the best of our knowledge, the framework and its key constructs and principles have themselves never been empirically examined with data from a representative cross-section of workplaces using quantitative methods. This article, with the aid of a new four-country (Australia, Canada, UK, and US) survey data set on 7000+ workplaces, initiates this kind of empirical study. The frames of reference distinguish three main types of employment relationships: unitarist, pluralist, radical. We select six attitudinal/behavioral indicators from the data set that distinguish which frame a workplace is in, combine them to form a Relational Quality Index, plot the 7000+ Relational Quality Index observations as four-country frequency distributions, and use different statistical criteria to indicate the relative size of each frame. We next do regression analysis in which the 7000+ workplace Relational Quality Index scores are the dependent variable and construct from the data set 20 frames of reference explanatory variables. As theory predicts, workplaces with stronger common (opposed) interests have better (worse) employer–employee relations. © Australian Labour and Employment Relations Association (ALERA) 2020 SAGE Publications Ltd, Los Angeles, London, New Delhi, Singapore and Washington DC.

A distinctive characteristic of study tours is their immersive nature, and the type of learning developed in such circumstances often relies on instinctive skills related to confidence, flexibility, curiosity, resilience and risk taking. To discover more about these learning experiences for adult undergraduate students studying varying disciplines in the creative industries at an Australian University, the researchers employed educational action research and developed assessment modules to foster student awareness of intangible skills and personal characteristics during short-term international study tours. On the completion of the tours, participating staff and students provided feedback, via a questionnaire and focus group discussions, that assessed the value of these modules in promoting learning and their contribution to personal growth and cultural sensitivity. Findings show that these modules were highly effective at enabling adult students to become aware of the skills they employed while being actively engaged in international study tours. This study highlights the potential significance of immersive assessment modules that provide specific ways for students to engage with their host country and the cultural differences to which they are exposed.

Machine learning (ML) outperforms traditional approaches in many molecular design tasks. ML models usually predict molecular properties from a 2D chemical graph or a single 3D structure, but neither of these representations accounts for the ensemble of 3D conformers that are accessible to a molecule. Property prediction could be improved by using conformer ensembles as input, but there is no large-scale dataset that contains graphs annotated with accurate conformers and experimental data. Here we use advanced sampling and semi-empirical density functional theory (DFT) to generate 37 million molecular conformations for over 450,000 molecules. The Geometric Ensemble Of Molecules (GEOM) dataset contains conformers for 133,000 species from QM9, and 317,000 species with experimental data related to biophysics, physiology, and physical chemistry. Ensembles of 1,511 species with BACE-1 inhibition data are also labeled with high-quality DFT free energies in an implicit water solvent, and 534 ensembles are further optimized with DFT. GEOM will assist in the development of models that predict properties from conformer ensembles, and generative models that sample 3D conformations.

Virtual screening can accelerate drug discovery by identifying promising candidates for experimental evaluation. Machine learning is a powerful method for screening, as it can learn complex structure-property relationships from experimental data and make rapid predictions over virtual libraries. Molecules inherently exist as a three-dimensional ensemble and their biological action typically occurs through supramolecular recognition. However, most deep learning approaches to molecular property prediction use a 2D graph representation as input, and in some cases a single 3D conformation. Here we investigate how the 3D information of multiple conformers, traditionally known as 4D information in the cheminformatics community, can improve molecular property prediction in deep learning models. We introduce multiple deep learning models that expand upon key architectures such as ChemProp and Schnet, adding elements such as multiple-conformer inputs and conformer attention. We then benchmark the performance trade-offs of these models on 2D, 3D and 4D representations in the prediction of drug activity using a large training set of geometrically resolved molecules. The new architectures perform significantly better than 2D models, but their performance is often just as strong with a single conformer as with many. We also find that 4D deep learning models learn interpretable attention weights for each conformer.

Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis1–3. Interzeolite transformations enable selective crystallization4–7, but are often too complex to be designed by comparing crystal structures. Here, computational and theoretical tools are combined to both exhaustively data mine polymorphic transformations reported in the literature and analyse and explain interzeolite relations. It was found that crystallographic building units are weak predictors of topology interconversion and insufficient to explain intergrowth. By introducing a supercell-invariant metric that compares crystal structures using graph theory, we show that diffusionless (topotactic and reconstructive) transformations occur only between graph-similar pairs. Furthermore, all the known instances of intergrowth occur between either structurally similar or graph similar frameworks. We identify promising pairs to realize diffusionless transformations and intergrowth, with hundreds of low-distance pairs identified among known zeolites, and thousands of hypothetical frameworks connected to known zeolite counterparts. The theory may enable the understanding and control of zeolite polymorphism. ©2019, The Author(s), under exclusive licence to Springer Nature Limited., NSF Award (no. 1534340), ONR - Contract (no. N00014-16-1-2432)

In the past, data in which science and engineering is based, was scarce and frequently obtained by experiments proposed to verify a given hypothesis. Each experiment was able to yield only very limited data. Today, data is abundant and abundantly collected in each single experiment at a very small cost. Data-driven modeling and scientific discovery is a change of paradigm on how many problems, both in science and engineering, are addressed. Some scientific fields have been using artificial intelligence for some time due to the inherent difficulty in obtaining laws and equations to describe some phenomena. However, today data-driven approaches are also flooding fields like mechanics and materials science, where the traditional approach seemed to be highly satisfactory. In this paper we review the application of data-driven modeling and model learning procedures to different fields in science and engineering.

Molecular dynamics simulations provide theoretical insight into the microscopic behavior of condensed-phase materials and, as a predictive tool, enable computational design of new compounds. However, because of the large spatial and temporal scales of thermodynamic and kinetic phenomena in materials, atomistic simulations are often computationally infeasible. Coarse-graining methods allow larger systems to be simulated by reducing their dimensionality, propagating longer timesteps, and averaging out fast motions. Coarse-graining involves two coupled learning problems: defining the mapping from an all-atom representation to a reduced representation, and parameterizing a Hamiltonian over coarse-grained coordinates. We propose a generative modeling framework based on variational auto-encoders to unify the tasks of learning discrete coarse-grained variables, decoding back to atomistic detail, and parameterizing coarse-grained force fields. The framework is tested on a number of model systems including single molecules and bulk-phase periodic simulations.

Robots are starting to share our social space and will continue to do so in the future. These interactive devices have the capability of facilitating various interaction modalities which could go beyond voice and screen based interactions. When developing interaction technologies, theories such as psychobiological and media compensation suggest to replicate natural ways of human interactions such as face to face. As a result, it is vital to study how people interpret robot's physical appearance and actions. According to previous studies, designing acceptable humanoid robots is far more challenging than designing robots with less human qualities. This paper emphasizes the importance of human non-verbal communication in emotional interactions and analyzes how people use full body expression to communicate basic emotions, in particular happiness, surprise, and anger. Furthermore, this paper presents a list of emotion specific movement behaviors that can be used and applied to better design forms and movements for both humanoid or non-humanoid robots.

Exploring wearable technology and user interfaces to assist in capturing and visualising relevant data for Parkinson's patients, carers and healthcare professionals.

A recent meeting focused on accelerated materials design and discovery examined user requirements for a general, collaborative, integrative, and on-demand materials research platform.

Co-creation is a promising business strategy as it focuses on customer experience and interactive relationships. 3D printing helps achieve co-creation goals due to its capabilities of product customisation. However, few researchers have acknowledged the method of integrating the two elements for valuable services. To address this procedural knowledge gap, the current study adopts a case study based on the DART (dialogue, access, risk-benefits, and transparency) model of co-creation. Four cases of online 3D design applications were selected and investigated. Initially, through a systematic analysis of the qualitative data, this study conceptualises the design model of the experience of co-creation with 3D printing. Its feasibility is then tested through the co-creation event ‘Designing Necklace Pendant as Mother’s Day Gifts’. Despite its effectiveness, participants’ limitations in creating 3D printable models constitute a challenge in the high-quality co-creation experience. The research findings will inspire service providers and product designers to embrace an entrepreneurship model of experiential consumption business and personalised economy incorporating 3D printing. Besides, the findings will benefit the lead author’s PhD research that might engage students in STEM educational programmes through co-creation with 3D printing for cultivating problem-solving ability.

People use music as a wellbeing resource in commute, ‘tuning-in’ to ‘tune out’ to mitigate stresses. These studies typically assume a listener’s mental representation of music. Instead, a listener’s engagement with music is more than a mental representation in real life; music moves people. People in commute 'tune-in' to music using portable music devices like headphones. In 'tuning-in' to music, people physicalize movement- synchronizing their heads or hands to the beat, play “air-piano” or imagine gestures through the use of portable music devices. These movements may help them to 'tune-out', temporary distancing themselves from their immediate commute environment. Research in music perception lags development of portable music devices, like headphones compensated for hearing loss and haptic devices that afford users to feel bass frequencies of music. This research explores what constitutes embodied interactions with music and how a combination of emergent devices- headphones and haptics can enhance these interactions in commute. Video observations record how a participant synchronizes and gestures with music, interacting with devices, music and context in real-time. Retrospective interviews probe at a participant’s quality of music experience in 'tuning-in' to 'tune-out', through understanding the mediators of causality, presence and flow and how these relate to his experience and wellbeing. Interviews corroborate with video data to provide an enriched understanding of context of a user’s interaction with music and device as a dynamic process in qualitative methodology. Results serve twofold objectives: (1) They inform technology providers with the knowledge to design and adapt devices for based on commuter’s embodied interactions with devices. (2) They provide technology providers, concert makers and transportation operators an understanding of commuter’s embodied interactions with music and devices for collaborations.

International short-term study tours are a fast-growing format for outbound education and provide exciting experiential learning opportunities for students in the creative industries disciplines. This success has encouraged researchers to seek a comprehensive view of the various concrete experiences that contribute to student learning during study tours. To that end, this article presents evidence from a qualitative online post-tour questionnaire administered with students studying various art and design disciplines who participated in short-term international study tours at a large Australian university in 2015. Using Kolb's Experiential Learning Theory as a framework, data collected explored student perceptions of which concrete experiences on a study tour are most impactful and which may actually hinder learning. Findings revealed: experiencing key industry venues are the most significant aspects of a study tour, rather than networking or personal growth; poor itinerary planning and travel issues are the least liked aspects of a tour, yet are often the most easily addressed with better tour management and planning; and students were positively impacted by unexpected gains in knowledge about their creative industries discipline. This study, therefore, provides unique insights into which concrete experiences may be most impactful as part of the experiential learning cycle within an international study tour for creative industries students.