Machine Learning & Robotics in New Material Discovery: Innovations, Start-Ups, Applications

The advancement of transformative technologies for building a sustainable future requires significant innovations in the design, development, and manufacturing of advanced materials and chemicals. To meet global sustainability needs faster, we must transition to the data-centric approaches of materials informatics. Materials informatics leverages the synergies between materials science, data science, and artificial intelligence and provides the foundations of a paradigm shift in materials discovery and development.
By increasing the agility of research and accelerating the development cycles, materials informatics enables transformative discoveries to reduce emissions and resource intensity, improve energy efficiency and circularity, expand the market share of sustainable products, and increase responsible raw material sourcing.

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Many important mechanical properties can only be measured using physical experiments, which means that design for such properties happens through a slow and expensive iterative cycle. Here, we describe our efforts to reinvent this paradigm using autonomous experimentation. In particular, we combine robots to perform experiments in an automated and reliable fashion with machine learning to select each subsequent design. Benchmarking has revealed that this process converges on high performing structures nearly 60 times faster than conventional grid searching. We discuss how autonomous experimentation accelerates the design process and allow us to identify tough and resilient structures for numerous applications ranging from personal protective equipment to crumple zones on cars.

Chemical/material formulations are characterized by large numbers of components but also a diversity of interacting and competing forces that determine system properties, making them difficult to model. Most formulated products are complex, but even after decades of development they are still designed using a combination of experience and heuristics. Hierarchical machine learning (HML) was developed as a tool for designing these systems based on the small datasets that are common in research and development.

HML is both an algorithm for designing and a process for understanding complex chemical/material systems. The goal in machine learning is to generate a response surface that accurately relates the input variables with the output responses of a system. HML generates a second response surface that is parameterized by latent variables that represent the underlying forces and interactions. These latent variables are obtained from theoretical or empirical models or surrogate physical measurements and represent conceptual understanding of the system. However, in contrast with heuristic approaches that are often reductionist, HML retains the full complexity of the forces and interactions that govern the system properties. It provides a path to both optimized design based on input parameters and their constraints as well as conceptual understanding of how these systems work, thus serving as a tool for applied research and development.

Ansatz AI has applied HML to a diversity of industrial technologies with corporate clients, ranging from molecular engineering of polymeric elastomers, lubricants, and dielectrics to liquid formulations of paints, coatings and personal care products. In these technologies, the goals have ranged from increasing performance to minimizing costs to shifting to renewable feedstocks. This seminar will explain the HML approach to modeling complex chemical/material formulations and highlight some of the solutions that it has provided.

Accelerating the discovery and commercialization of novel materials is necessary for maintaining economic competitiveness and timely addressing many societal issues (e.g., sustainable manufacturing and clean energy). For several decades now, simulations have complemented empirical science for such acceleration, culminating in several successful industrial applications of this approach, termed integrated computational materials engineering (ICME). In 2011, the Materials Genome Initiative (MGI) sought to apply this idea at scale across all materials industries, including a third “digital data” pillar. Materials informatics is the practical manifestation of “digital data” methods to materials science problems, including: (1) the collection, generation, and distribution of materials data, (2) the use of that data to train machine learning models for predicting process-structure-property relationships, and (3) the design of experiments using artificial intelligence (AI) algorithms based on those models.
Citrine Informatics is a software company building a scalable, enterprise-level materials informatics platform for data-driven materials and chemicals development. The Citrine Platform combines smart materials data infrastructure and AI, which accelerates development of cutting-edge materials, facilitates product portfolio optimization, and codifies research IP, enabling its reuse and preventing its loss. Citrine's customers include Panasonic, Michelin, LANXESS, and others in the materials, chemicals, and product manufacturing industries.
In this talk, the concepts around materials informatics will be introduced, Citrine’s software will be described, and several case studies demonstrating the value of materials informatics will be discussed.

As materials and manufacturing processes get more and more complicated also R&D processes become more complex. Traditional R&D methods are often too inefficient to harness the full potential of these new materials and manufacturing processes. Machine learning based R&D software is faster, more efficient and offers many other benefits. However, many ML tools are built from data scientists for data scientists, hence, are complicated to use and require user expertise. In my talk I will show you how easy to use tools can help engineers and researchers to reduce R&D time by up to 95% and mitigate supply chain risks without the need of ML or programming knowledge.

Tailor-making materials for a given application is one of the most desired, yet challenging, technological advancements of our century. We need these materials to reach the global sustainability goals of our society, including climate action and affordable clean energy. The success in generating large quantities, high-quality data on materials in the past decade makes the field ready for an abrupt growth toward this aim by applying the tools from the field of artificial intelligence. To enable this, however, we need to develop material-specific machine learning approaches and methodologies. In my talk, I will discuss how we are approaching this challenge by discussing a few success stories from the field of nanoporous materials for energy applications, including quantifying the novelty of new materials, learning from failures, and multi-scale design from atoms to chemical plants.

Most commercial refractory alloys were designed with high temperature strength and manufacturability prioritized over oxidation resistance. This drives the need for complex and expensive coatings in aggressive service conditions. By lifting classic composition constraints through a high entropy alloying approach, it is possible to achieve improved balance-of-properties in refractory metals. Tailoring properties individually, as required for a specific application or as input for design trades, is also enabled. Here, we review recent work combining high throughput experimental screening, machine learning, and multi-objective optimization to explore a wide refractory alloy composition space. We demonstrate a materials informatics alloy selection process for extreme service conditions where oxidation resistance is prioritized alongside mechanical properties and manufacturability. The general methods presented here can be applied to other applications and highlight the benefits of a materials informatics approach to alloy design.