Patent categorization, which is to assign multiple International Patent Classification (IPC) codes to a patent document, relies heavily on expert efforts, as it requires substantial domain knowledge. When formulated as a multi-label text classification (MTC) problem, it draws two challenges to existing models: one is to learn effective document representations from text content; the other is to model the cross-section behavior of label set. In this work, we propose a label attention model based on graph convolutional network. It jointly learns the document-word associations and word-word co-occurrences to generate rich semantic embeddings of documents. It employs a non-local attention mechanism to learn label representations in the same space of document representations for multi-label classification. On a large CIRCA patent database, we evaluate the performance of our model and as many as seven competitive baselines. We find that our model outperforms all those prior state of the art by a large margin and achieves high performance on P@k and nDCG@k.

The discovery of new materials has been the essential force which brings a discontinuous improvement to industrial products' performance. However, the extra-vast combinatorial design space of material structures exceeds human experts' capability to explore all, thereby hampering material development. In this paper, we present a material industry-oriented web platform of an AI-driven molecular inverse-design system, which automatically designs brand new molecular structures rapidly and diversely. Different from existing inverse-design solutions, in this system, the combination of substructure-based feature encoding and molecular graph generation algorithms allows a user to gain high-speed, interpretable, and customizable design process. Also, a hierarchical data structure and user-oriented UI provide a flexible and intuitive workflow. The system is deployed on IBM's and our client's cloud servers and has been used by 5 partner companies. To illustrate actual industrial use cases, we exhibit inverse-design of sugar and dye molecules, that were carried out by experimental chemists in those client companies. Compared to a general human chemist's standard performance, the molecular design speed was accelerated more than 10 times, and greatly increased variety was observed in the inverse-designed molecules without loss of chemical realism.

In recent decades, materials science literature and patents have grown exponentially. This has also contributed to an ever-growing challenge whether the literature is current, as there can be a gap between when the patent was filed and when it was approved. Moreover, it is difficult to ensure that a patent cites the appropriate prior art due to variety and volume of materials science data, especially when it is in two separate sources that have different curation mechanisms and purpose - publications and patents. The existing relational database schema, generally used to store publications, also presents challenges given the strict tabular schema, which may not be appropriate for organizing and querying highly interconnected information about materials in these publications and patents. For example, elements are chemically combined to form a compound, which can then be converted to other compounds via chemical reactions. Furthermore, relational database is not designed for handling combining data from multiple sources and with various formats, thus it makes discover relevance between publications and patents become difficult. In order to explore an alternative approach to represent materials data and combine data from multiple sources into the same repository, in this work, we propose a solution to integrate data from Open Quantum Materials Database (OQMD) and patent data from USPTO1 database into a network and named it heterogeneous materials information network (HMIN). We generalize prior work which based on using meta path-based topological features to explore the network, and we propose features to identify network noise and investigate relatedness between different-typed objects to meet our application needs. We built several machine learning models by using these features to explore relevance between materials science publications and patents. Experiment results show that HMIN can help researchers effectively discover related publications and patents originally kept in different sources. Our work exhibits to materials community a new way of appro-priately representing materials data and discovering connections between data from multiple sources.

Statistical analysis is a fundamental method in analysis, design, and optimization of large systems with uncertainties. It is being applied in drug development, analyzing financial markets, search for new oil fields, and many more areas. As the silicon process technology scales to its limits, uncertainty plays an increasing role and has made its way into tools such as yield analysis, statistical time analysis, etc. In this educational panel, we will start with a short tutorial on Monte Carlo methods including their use in EDA. Three experts from distinct application fields will then follow and discuss their experience in using Monte Carlo methods for solving large-scale problems in their domain. It may come as a surprise to some attendees of DAC to learn how much commonality there is between methods used in EDA and other field that seem far-fetched.

The replica exchange method is a popular approach for studying the folding thermodynamics of small to modest size proteins in explicit solvent, since it is easily parallelized. However, replica exchange can become computationally expensive for large-scale studies, due to the number of replicas needed as well as interprocess or communication requirements both between and within replicas. In this paper, we discuss an implementation of replica exchange molecular dynamics on Blue Gene/L for performing large scale simulation studies of systems of biological interest. The algorithm is tuned with an awareness of the physical network topology and hardware performance features of the Blue Gene/L architecture. Performance measurements for replica exchange using the blue matter molecular dynamics application are presented on Blue Gene/L hardware with up to 256 replicas simulated on 8,192 compute nodes. Both scalability and performance are achieved with this implementation.