SINGAPORE/KUALA LUMPUR - The stench of curdled milk wafted from a shipping container of waste at Malaysia's Port Klang as Environment Minister Yeo Bee Yin told a group of journalists in May she would send the maggot-infested rubbish back where it came from. Yeo was voicing a concern that has spread across Southeast Asia, fueling a media storm over the dumping of rich countries' unwanted waste. About 5.8 million tons of trash was exported between January and November last year, led by shipments from the U.S., Japan and Germany, according to Greenpeace. Now governments across Asia are saying no to the imports, which for decades fed mills that recycled waste plastic. As more and more waste came, the importing countries faced a mounting problem of how to deal with tainted garbage that couldn't be easily recycled.

Using what they call DASH (DNA-based Assembly and Synthesis of Hierarchical) materials, engineers constructed a DNA material with capabilities of metabolism, in addition to self-assembly and organization -- three key traits of life. "We are introducing a brand-new, lifelike material concept powered by its very own artificial metabolism. We are not making something that's alive, but we are creating materials that are much more lifelike than have ever been seen before," said Dan Luo, professor of biological and environmental engineering. The paper published in Science Robotics. For any living organism to maintain itself, there must be a system to manage change.

Most attempts at giving robots muscles tend to be heavy, slow or both. Scientists might finally have a solution that's both light and nimble, though. They've developed fibers that can serve as artificial muscles for robots while remaining light, responsive and powerful. They bonded two polymers with very different thermal expansion rates (a cyclic copolymer elastomer and a thermoplastic polyethylene) that reacts with a strong pulling force when subjected to even slight changes in heat. They're so strong that just one fiber can lift up to 650 times its weight, and response times can be measured in milliseconds.

In data mining, machine learning, and other areas of AI, we are often faced with datasets that contain many more attributes than needed, or that can even be helpful for tasks such as clustering or classification. Problems associated with such high dimensional data are for example the concentration effect of distances [13, 20] or irrelevant features [25, 49]. For clustering [31] and outlier detection [49], researchers have made use of various techniques to identify relevant subspaces, as defined by subsets of features that are informative for a particular task. Examples of how relevant subspaces can be determined for individual clusters or outliers include local density estimation in a systematic search through candidate subspaces (often following the Apriori principle [7] in various adaptations to the subspace search problem [48]), or the adaptation of distance measures based on the distribution within local neighborhoods (using some analysis of variance or even covariance -- typically based on PCA -- to allow also for an adaptation to correlated features). For sufficiently tight local neighborhoods, the underlying local data manifold can be regarded as approaching a linear form [40], an assumption that further justifies the determination of locally relevant features for subspace determination.

Over the last 15 years there has been a surge in the use of machine learning to gain materials chemistry insights. These methods use existing data (largely computed with ab-initio methods) to train statistical models that can make useful predictions about whether chemical compounds will be stable, and the properties they are likely to exhibit. However, a large majority of the knowledge the scientific community has generated to date is recorded as "unstructured" text, and has therefore been largely inaccessible to machine-learning and statistical analysis. In recent years however, the Natural Language Processing (NLP) research community has made great progress on methods to computationally parse and learn from unstructured text. In our paper, we show how the application of an unsupervised NLP model can capture information from the materials chemistry literature in a way that also uncovers latent knowledge previously unknown to the research community.

This work demonstrates the execution of a novel process model for knowledge discovery and data mining for metabolomics (MeKDDaM). It aims to illustrate MeKDDaM process model applicability using four different real-world applications and to highlight its strengths and unique features. The demonstrated applications provide coverage for metabolite profiling, target analysis, and metabolic fingerprinting. The data analysed in these applications were captured by chromatographic separation and mass spectrometry technique (LC-MS), Fourier transform infrared spectroscopy (FT-IR), and nuclear magnetic resonance spectroscopy (NMR) and involve the analysis of plant, animal, and human samples. The process was executed using both data-driven and hypothesis-driven data mining approaches in order to perform various data mining goals and tasks by applying a number of data mining techniques. The applications were selected to achieve a range of analytical goals and research questions and to provide coverage for metabolite profiling, target analysis, and metabolic fingerprinting using datasets that were captured by NMR, LC-MS, and FT-IR using samples of a plant, animal, and human origin. The process was applied using an implementation environment which was created in order to provide a computer-aided realisation of the process model execution.

This work presents MeKDDaM-SAGA, computer-aided automation software for implementing a novel knowledge discovery and data mining process model that was designed for performing justifiable, traceable and reproducible metabolomics data analysis. The process model focuses on achieving metabolomics analytical objectives and on considering the nature of its involved data. MeKDDaM-SAGA was successfully used for guiding the process model execution in a number of metabolomics applications. It satisfies the requirements of the proposed process model design and execution. The software realises the process model layout, structure and flow and it enables its execution externally using various data mining and machine learning tools or internally using a number of embedded facilities that were built for performing a number of automated activities such as data preprocessing, data exploration, data acclimatization, modelling, evaluation and visualization. MeKDDaM-SAGA was developed using object-oriented software engineering methodology and was constructed in Java. It consists of 241 design classes that were designed to implement 27 use-cases. The software uses an XML database to guarantee portability and uses a GUI interface to ensure its user-friendliness. It implements an internal embedded version control system that is used to realise and manage the process flow, feedback and iterations and to enable undoing and redoing the execution of the process phases, activities, and the internal tasks within its phases.

Materials design can be cast as an optimization problem with the goal of achieving desired properties, by varying material composition, microstructure morphology, and processing conditions. Existence of both qualitative and quantitative material design variables leads to disjointed regions in property space, making the search for optimal design challenging. Limited availability of experimental data and the high cost of simulations magnify the challenge. This situation calls for design methodologies that can extract useful information from existing data and guide the search for optimal designs efficiently. To this end, we present a data-centric, mixed-variable Bayesian Optimization framework that integrates data from literature, experiments, and simulations for knowledge discovery and computational materials design. Our framework pivots around the Latent Variable Gaussian Process (LVGP), a novel Gaussian Process technique which projects qualitative variables on a continuous latent space for covariance formulation, as the surrogate model to quantify "lack of data" uncertainty. Expected improvement, an acquisition criterion that balances exploration and exploitation, helps navigate a complex, nonlinear design space to locate the optimum design. The proposed framework is tested through a case study which seeks to concurrently identify the optimal composition and morphology for insulating polymer nanocomposites. We also present an extension of mixed-variable Bayesian Optimization for multiple objectives to identify the Pareto Frontier within tens of iterations. These findings project Bayesian Optimization as a powerful tool for design of engineered material systems.

Rapidly estimating the remaining wall thickness (RWT) is paramount for the non-destructive condition assessment evaluation of large critical metallic pipelines. A robotic vehicle with embedded magnetism-based sensors has been developed to traverse the inside of a pipeline and conduct inspections at the location of a break. However its sensing speed is constrained by the magnetic principle of operation, thus slowing down the overall operation in seeking dense RWT mapping. To ameliorate this drawback, this work proposes the partial scanning of the pipe and then employing Gaussian Processes (GPs) to infer RWT at the unseen pipe sections. Since GP prediction assumes to have normally distributed input data - which does correspond with real RWT measurements - Gaussian mixture (GM) models are proven in this work as fitting marginal distributions to effectively capture the probability of any RWT value in the inspected data. The effectiveness of the proposed approach is extensively validated from real-world data collected in collaboration with a water utility from a cast iron water main pipeline in Sydney, Australia.

Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care and many others. In chemistry, conventional methodologies for innovation usually rely on expensive and incremental strategies to optimize properties from molecular structures. On the other hand, inverse approaches map properties to structures, thus expediting the design of novel useful compounds. In this chapter, we examine the way in which current deep generative models are addressing the inverse chemical discovery paradigm. We begin by revisiting early inverse design algorithms. Then, we introduce generative models for molecular systems and categorize them according to their architecture and molecular representation. Using this classification, we review the evolution and performance of important molecular generation schemes reported in the literature. Finally, we conclude highlighting the prospects and challenges of generative models as cutting edge tools in materials discovery.