AI Materia

CEO & Founder

Materials Informatics and Sustainability

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.

Air Force Research Lab

Autonomous Materials Lead

Moore's Law and New Material Discovery: Self-Driving Labs and Machine Learning

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Berlinguette Research Group, University of British Columbia

Professor

Self driving lab: automatic discovery and optimization of thin film inorganic and organic materials

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Boston University

Associate Professor

Let the Robot Design it: Autonomous Experimentation for Mechanical Design

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.

Carnegie Mellon University

Associate Professor

Designing and Understanding Complex Chemical/Material Formulations with Hierarchical Machine Learning

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.

Citrine Informatics

Director External Research Programs

Accelerating Materials Discovery, Design, and Development with Materials Informatics

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.

Exponential Technologies Ltd

CEO & Co Founder

How to democratize machine learning in material science.

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.

Freie Universität Berlin

Scientist

Blueprints for automated material discovery using artificial intelligence

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.

GE Research

Materials Scientist

A Materials Informatics Approach to Refractory High Entropy Alloy Development

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.

Kebotix

Chief Commercial Officer

Autonomous self-driving labs and AI: Energy Materials

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Kyulux

Materials Scientist

OLED Materials Discovery with ML : how to deal with clean and dirty data simultaneously

Kyulux has been developing emissive small molecules for OLED devices for the past few years. In Particular, we have been trying to build a comprehensive web-based platform that can cover a wide range of key elements for material informatics, including experimental/computational data collection, automated data pre-processing, machine learning using those data, and data visualization. A few practical challenges we have faced will be discussed.

Lawrence Berkeley National Laboratory

Associate Professor

Optimal Autonomous Data Acquisition for Large-Scale Experimental Facilities

Autonomous experimentation has had a significant impact on how many large-scale experimental facilities operate, however; the concept is often linked to mystery and confusion. In this talk, I will take a very practical look at the subject. I will introduce Gaussian-process-driven autonomous experimentation on a very high level, followed by the presentation of a few examples from across large-scale experimental facilities. This talk is also meant as guidance for everyone in the audience who wants to use autonomous experimentation for their research and will present some readily-available tools.

Materials Zone

VP Business Development

From Materials Data to AI Accelerated Results, Fast!

Transforming multi-dimensional, unstructured, and dispersed materials data into AI/ML driven results for R&D, supply chain and manufacturing is a challenge. Doing so rapidly, cost-effectively, and sustainably on a collaborative organizational platform, is an even bigger challenge. Like the “Rolodex-to-CRM” evolution in marketing/sales before it, the Materials Informatics Platform (MIP) is the next organizational platform evolution for materials/products. We will showcase via solid-state batteries and perovskite solar cells from the www.perovskitedatabase.com, although domain agnostic and proven on hydrogen cells, building materials, polymers, 3D printing, alloys, coating, packaging, healthcare products and more.

NASA

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Automatic microstructure segmentation and quantification with deep learning encoders pre-trained on a large microscopy dataset called MicroNet

A transfer learning approach for the automatic segmentation of microscopy data is presented. Many encoder architectures, including VGG, Inception, ResNet, and others, were trained on ~100,000 microscopy images from 54 material classes to create pre-trained models that learn representations that are more relevant to downstream microscopy analysis tasks than models pre-trained on natural images. The pre-trained encoders were embedded into segmentation architectures including U-Net and DeepLabV3+ to evaluate the performance of models pre-trained on a large microscopy dataset. Each encoder/decoder pair was evaluated on several benchmark datasets. Our testing shows that models pre-trained on a large microscopy dataset generalize better to out-of-distribution data (micrographs taken under different imaging or sample conditions) and are more accurate when training data is limited than models pre-trained on ImageNet. The application of this technique to segment and quantify microscopy features from Ni-superalloys and environmental barrier coatings is demonstrated.

North Carolina State University

Associate Professor

Rise of Self-Driving Labs in Chemical & Materials Sciences: Accelerated Discovery and Manufacturing of Energy Materials

Despite the intriguing properties and widespread applications of semiconductor nanomaterials in energy technologies, their discovery, synthesis, and manufacturing are still based on Edisonian trial-and-error based techniques. Existing materials development strategies using resource-intensive batch reactors with irreproducible and uncontrollable heat/mass transport rates very often fail to overcome the demands of the vast synthesis and processing universe of energy materials, resulting in a slow and expensive discovery and development timeframe (8-10 years). Recent advances in lab automation and machine learning (ML)-guided modeling/decision-making strategies provide an exciting opportunity to reshape the discovery and manufacturing of emerging solution-processed energy materials.1 In this talk, I will present an end-to-end ‘self-driving lab’ for autonomous discovery, development, and on-demand manufacturing of energy materials through convergence of flow chemistry, robotics, and in-situ material characterization with ML.2 I will discuss how modularization of different stages of materials synthesis and processing in tandem with an ensemble neural network modeling and decision-making under uncertainty can enable a resource-efficient navigation through an experimentally accessible high dimensional space. An application of the self-driving lab for the autonomous synthesis of metal halide perovskite quantum dots will be presented

Northwestern University

Professor

Metal Organic Frameworks: Large scale screening of MOFsfor methane storage

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OTI Lumionics Inc.

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Designing display materials without a wet lab: progress in machine learning

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