Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their use in the lab. This means that designing and running experiments proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), a solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.

Pumping carbon dioxide underground may help combat the warming of the atmosphere but finding appropriate underground sites that could safely serve as reservoirs can be complicated. To address this complexity, a Penn State-led research team combined an artificial intelligence technique with an understanding of physics to develop an efficient, cost-effective predictive modeling approach. They published their results in the Journal of Contaminant Hydrology. "Storing carbon dioxide underground is one environmentally friendly way to reduce the amount of the gas in the atmosphere," said Parisa Shokouhi, associate professor of engineering science and mechanics. "But the geological structure can be unfavorable to carbon dioxide injection. For example, if pressure surpasses a certain limit, there can be fractures, gas leakage and earthquakes, and if you over-inject with too much gas, you can have similar issues."

Electron density $\rho(\vec{r})$ is the fundamental variable in the calculation of ground state energy with density functional theory (DFT). Beyond total energy, features in $\rho(\vec{r})$ distribution and modifications in $\rho(\vec{r})$ are often used to capture critical physicochemical phenomena in functional materials and molecules at the electronic scale. Methods providing access to $\rho(\vec{r})$ of complex disordered systems with little computational cost can be a game changer in the expedited exploration of materials phase space towards the inverse design of new materials with better functionalities. We present a machine learning framework for the prediction of $\rho(\vec{r})$. The model is based on equivariant graph neural networks and the electron density is predicted at special query point vertices that are part of the message passing graph, but only receive messages. The model is tested across multiple data sets of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and LixNiyMnzCo(1-y-z)O2 lithium ion battery cathodes (NMC). For QM9 molecules, the accuracy of the proposed model exceeds typical variability in $\rho(\vec{r})$ obtained from DFT done with different exchange-correlation functional and show beyond the state of the art accuracy. The accuracy is even better for the mixed oxide (NMC) and electrolyte (EC) datasets. The linear scaling model's capacity to probe thousands of points simultaneously permits calculation of $\rho(\vec{r})$ for large complex systems many orders of magnitude faster than DFT allowing screening of disordered functional materials.

Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. To properly represent molecules, graph contrastive learning is a promising paradigm as it utilizes self-supervision signals and has no requirements for human annotations. However, prior works fail to incorporate fundamental domain knowledge into graph semantics and thus ignore the correlations between atoms that have common attributes but are not directly connected by bonds. To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning. KCL framework consists of three modules. The first module, knowledge-guided graph augmentation, augments the original molecular graph based on the Chemical Element KG. The second module, knowledge-aware graph representation, extracts molecular representations with a common graph encoder for the original molecular graph and a Knowledge-aware Message Passing Neural Network (KMPNN) to encode complex information in the augmented molecular graph. The final module is a contrastive objective, where we maximize agreement between these two views of molecular graphs. Extensive experiments demonstrated that KCL obtained superior performances against state-of-the-art baselines on eight molecular datasets. Visualization experiments properly interpret what KCL has learned from atoms and attributes in the augmented molecular graphs. Our codes and data are available in supplementary materials.

With the global issue of plastic debris ever expanding, it is about time that the technology industry stepped in. This study aims to assess whether deep learning can successfully distinguish between marine life and man-made debris underwater. The aim is to find if we are safely able to clean up our oceans with Artificial Intelligence without disrupting the delicate balance of the aquatic ecosystems. The research explores the use of Convolutional Neural Networks from the perspective of protecting the ecosystem, rather than primarily collecting rubbish. We did this by building a custom-built, deep learning model, with an original database including 1,644 underwater images and used a binary classification to sort synthesised material from aquatic life. We concluded that although it is possible to safely distinguish between debris and life, further exploration with a larger database and stronger CNN structure has the potential for much more promising results.

Complex systems show how surprising and beautiful phenomena can emerge from structures or agents following simple rules. With the recent success of deep reinforcement learning (RL), a natural path forward would be to use the capabilities of multiple deep RL agents to produce emergent behavior of greater benefit and sophistication. In general, this has proved to be an unreliable strategy without significant computation due to the difficulties inherent in multi-agent RL training. In this paper, we propose some criteria for creativity in multi-agent RL. We hope this proposal will give artists applying multi-agent RL a starting point, and provide a catalyst for further investigation guided by philosophical discussion.

Across industries, value chains are facing increasing uncertainty from climatic anomalies, market volatility, and the COVID-19 pandemic, among other factors. Industries as diverse as agriculture, oil and gas, and mining face essentially the same problem: they need the ability to both run with increased efficiency and recover quickly from unforeseen or unexpected challenges. But these two goals often conflict. If companies simply increase production levels, they'll inevitably run into bottlenecks--and if failures occur that worsen those bottlenecks, the entire network can slow down and become less resilient. For more on how COVID-19 has affected supply chains, see Knut Alicke, Richa Gupta, and Vera Trautwein, "Resetting supply chains for the next normal," July 21, 2020. Resolving this conflict presents several challenges.

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.

No, it's'super jelly' -- a bizarre new material that can survive being run over by a car even though it's composed of 80 per cent water. The'glass-like hydrogel' may look and feel like a squishy jelly, but when compressed it acts like shatterproof glass, its University of Cambridge developers said. It is formed using a network of polymers held together by a series of reversible chemical interactions that can be tailored to control the gel's mechanical properties. This is the first time that a soft material has been produced that is capable of such significant resistance to compressive forces. Super jelly could find various applications, the team added, from use for building soft robotics and bioelectronics through to replacement for damaged cartilage.

--Based on the analysis of the proportion of utility in the supporting transactions used in the field of data mining, high utility-occupancy pattern mining (HUOPM) has recently attracted widespread attention. Unlike high-utility pattern mining (HUPM), which involves the enumeration of high-utility (e.g., profitable) patterns, HUOPM aims to find patterns representing a collection of existing transactions. In practical applications, however, not all patterns are used or valuable. For example, a pattern might contain too many items, that is, the pattern might be too specific and therefore lack value for users in real life. T o achieve qualified patterns with a flexible length, we constrain the minimum and maximum lengths during the mining process and introduce a novel algorithm for the mining of flexible high utility-occupancy patterns. In addition, a utility-occupancy nested list (UO-nlist) and a frequency-utility-occupancy table (FUO-table) are employed to avoid multiple scans of the database. Evaluation results of the subsequent experiments confirm that the proposed algorithm can effectively control the length of the derived patterns, for both real-world and synthetic datasets. Moreover, it can decrease the execution time and memory consumption. HE initial motivation for frequent pattern mining (FPM) was to analyze the shopping behavior of customers using transactional databases and recommend frequently purchased patterns to customers [1], [2], [3], [4], [5]. In this case, researchers believed that the item is binary and whether an item appears in a transaction is considered primary. However, frequent purchase patterns are occasionally less profitable than infrequent purchase patterns with high profits, which poses a fundamental problem. Hence, the discovery of high-utility patterns that consider not only the internal utility (e.g., quantity) but also the external utility (e.g., profit, interest, or weight) [6], [7], [8], [9] has gained substantial research attention. Moreover, a framework called high-utility pattern mining (HUPM) [10], [11] was proposed to address this practical issue. In contrast with frequent pattern mining (FPM), the lack of a downward closure property makes HUPM more difficult and intractable. This research was partially supported by National Natural Science Foundation of China (Grant No. 62002136), Guangzhou Basic and Applied Basic Research Foundation (Grant No. 202102020277). Wensheng Gan is with the College of Cyber Security, Jinan University, Guangzhou 510632, China.