Phase diagrams of complex fluids are essential tools for understanding solubility and miscibility. Using a new objective function coupled with a constrained Bayesian optimization algorithm, we demonstrate the efficient location of phase boundaries in a sample two-phase ternary modeled using polymer self-consistent field theory, regularly seeing 50% fewer observations than an exhaustive search. Our approach is general, gradient-free, and can be applied to either simulation or experimental campaigns.

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional highperformance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

New tools enable new ways of working, and materials science is no exception. In materials discovery, traditional manual, serial, and human-intensive work is being augmented by automated, parallel, and iterative processes driven by Artificial Intelligence (AI), simulation and experimental automation. In this perspective, we describe how these new capabilities enable the acceleration and enrichment of each stage of the discovery cycle. We show, using the example of the development of a novel chemically amplified photoresist, how these technologies’ impacts are amplified when they are used in concert with each other as powerful, heterogeneous workflows.

Artificial Intelligence (AI)-driven material design has been attracting great attentions as a groundbreaking technology across a wide spectrum of industries. Molecular design is particularly important owing to its broad application domains and boundless creativity attributed to progresses in generative models. The recent maturity of molecular generative models has stimulated expectations for practical use among potential users, who are not necessarily familiar with coding or scripting, such as experimental engineers and students in chemical domains. However, most of the existing molecular generative models are Python libraries on GitHub, that are accessible for only IT-savvy users. To fill this gap, we newly developed a graphical user interface (GUI)-based web application of molecular generative models, Molecule Generation Experience, that is open to the general public. This is the first web application of molecular generative models enabling users to work with built-in datasets to carry out molecular design. In this paper, we describe the background technology extended from our previous work. Our new online evaluation and structural filtering algorithms significantly improved the generation speed by 30 to 1,000 times with a wider structural variety, satisfying chemical stability and synthetic reality. We also describe in detail our Kubernetes-based scalable cloud architecture and user-oriented GUI that are necessary components to achieve a public service. Finally, we present actual use cases in industrial research to design new photoacid generators (PAGs) as well as release cases in educational events.

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.

Advances in polymer science have made polymers essential in our everyday life and have yielded unprecedented quantities of data over the past several decades. However, it is still challenging and inefficient to organize such scattered and accumulated "big data" in a text format through mass journals, patents, and web pages due to the complexity and ambiguity of polymer representations. In this paper, we report the first automated framework, PolyName2Structure (PN2S), which is able to convert various polymer name representations to their corresponding polymer structures. In PN2S, machine learning models were built to predict the polymerization pathway, identify the reacting group(s), and generate repeating units after polymerization. This PN2S system achieved over 90% accuracy when applied to polymer names listed in a commercial catalog, embodying the first step toward resolving the complexity of the data structure for polymers by building a practical model that enables text mining of structural polymer information.

Designing novel materials that possess desired properties is a central need across many manufacturing industries. Driven by that industrial need, a variety of algorithms and tools have been developed that combine AI (machine learning and analytics) with domain knowledge in physics, chemistry, and materials science. AI-driven materials design can be divided to mainly two stages; the first one is the modeling stage, where the goal is to build an accurate regression or classification model to predict material properties (e.g. glass transition temperature) or attributes (e.g. toxic/non-toxic). The next stage is design, where the goal is to assemble or tune material structures so that they can achieve user-demanded target property values based on a prediction model that is trained in the modeling stage. For maximum benefit, these two stages should be architected to form a coherent workflow. Today there are several emerging services and tools for AI-driven material design, however, most of them provide only partial technical components (e.g. data analyzer, regression model, structure generator, etc.), that are useful for specific purposes, but for comprehensive material design, those components need to be orchestrated appropriately. Our material design system provides an end-to-end solution to this problem, with a workflow that consists of data input, feature encoding, prediction modeling, solution search, and structure generation. The system builds a regression model to predict properties, solves an inverse problem on the trained model, and generates novel chemical structure candidates that satisfy the target properties. In this paper we will introduce the methodology of our system, and demonstrate a simple example of inverse design generating new chemical structures that satisfy targeted physical property values.

The discovery of novel materials can generate immense technological, economic, and social benefits. However, these are slow, challenging, expert-intensive efforts. Our thesis is that new capabilities of cognitive computing—particularly natural language processing, knowledge representation, and automated reasoning—are poised to transform the process of materials discovery and take us from our current “4th paradigm” of discovery driven by data science and machine learning to a “5th paradigm” era where cognitive systems seamlessly integrate information from human experts, experimental data, physics-based models, and data-driven models to speed discovery. We discuss the key bottlenecks to discovery that need to be removed to enable this new approach and illustrate progress towards this cognitive future with examples from IBM research efforts as well as the broader literature.

Hardware has taken on a supporting role in the maturation and proliferation of narrow AI, but will take a leading role to enable the innovation and adoption of broad AI. The concurrent evolution of broad AI with purpose-built hardware will shift traditional balances between cloud and edge, structured and unstructured data, and training and inference. Heterogeneous system architectures are already being delivered where varied compute resources, including high-bandwidth CPUs, specialized AI accelerators, and high-performance networking are infused in each node to yield significant performance improvements. Looking to the future, we envision a roadmap of specialized technologies to accelerate AI, starting with heterogeneous digital von Neumann machines, exploring reduced-precision accelerator approaches, finding the limits of conventional device power-performance with analog AI devices, and finishing with quantum computing for AI.

We report a systematic study of the feasibility of using directed self-assembly (DSA) in real product design for 7-nm fin field effect transistor (FinFET) technology. We illustrate a design technology co-optimization (DTCO) methodology and two test cases applying both line/space type and via/cut type DSA processes. We cover the parts of DSA process flow and critical design constructs as well as a full chip capable computational lithography framework for DSA. By co-optimizing all process flow and product design constructs in a holistic way using a computational DTCO flow, we point out the feasibility of manufacturing using DSA in an advanced FinFET technology node and highlight the issues in the whole DSA ecosystem before we insert DSA into manufacturing. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

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.

Orientation control of thin film nanostructures derived from block copolymers (BCPs) are of great interest for various emerging technologies like separation membranes, nanopatterning, and energy storage. While many BCP compositions have been developed for these applications, perpendicular orientation of these BCP domains is still very challenging to achieve. Herein we report on a new, integration-friendly approach in which small amounts of a phase-preferential, surface active polymer (SAP) was used as an additive to a polycarbonate-containing BCP formulation to obtain perpendicularly oriented domains with 19 nm natural periodicity upon thermal annealing. In this work, the vertically oriented BCP domains were used to demonstrate next generation patterning applications for advanced semiconductor nodes. Furthermore, these domains were used to demonstrate pattern transfer into a hardmask layer via commonly used etch techniques and graphoepitaxy-based directed self-assembly using existing lithographic integration schemes. We believe that this novel formulation-based approach can easily be extended to other applications beyond nanopatterning.

Directed self-assembly (DSA) of block copolymers (BCPs) has become a promising patterning technique for 7nm node hole shrink process due to its material-controlled CD uniformity and process simplicity.[1] For such application, cylinder-forming BCP system has been extensively investigated compared to its counterpart, lamella-forming system, mainly because cylindrical BCPs will form multiple vias in non-circular guiding patterns (GPs), which is considered to be closer to technological needs.[2-5] This technological need to generate multiple DSA domains in a bar-shape GP originated from the resolution limit of lithography, i.e. those vias placed too close to each other will merge and short the circuit. In practice, multiple patterning and self-aligned via (SAV) processes have been implemented in semiconductor manufacturing to address this resolution issue.[6] The former approach separates one pattern layer with unresolvable dense features into several layers with resolvable features, while the latter approach simply utilizes the superposition of via bars and the pre-defined metal trench patterns in a thin hard mask layer to resolve individual vias, as illustrated in Fig 1 (upper). With proper design, using DSA to generate via bars with the SAV process could provide another approach to address the resolution issue.In this paper, DSA of lamella-forming BCP was evaluated as a candidate for forming SAV, which requires the DSA process to support structures from circular via to lines and spaces. The basic process flow is similar to general graphoepitaxy method as shown in Fig. 1 (lower). The morphologies of the DSA vias derived from lamellar BCPs were found to be less sensitive to the BCP coating thickness compared to the cylindrical BCP system of similar L0, as reported by Liu et al. This implies that lamellar BCP may provide a larger process window and higher tolerance for local pattern density variation. The profile and the thickness of the residual PS layer of DSA structures were studied using Monte Carlo simulation and FIB cross-section SEM. Furthermore, a series of defectivity study using the lamellar system will be discussed, including film stack, DSA, and etch process fine-tuning. Structural and electrical demo using DSA of lamellar BCP and SAV process will be presented. Finally, the benefits and challenges of implementing DSA for 7nm via process will be discusses.