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In this webinar, we’ll demonstrate ML for materials design and development and hear a case study presentation from Welding Alloys Group. This will detail a project in which ML found an improved, cost-effective, and more environmentally-friendly hardfacing material.
In this webinar we discussed, with case studies and a software demonstration, the use of the Alchemite™ machine learning software for DOE. Alchemite™ typically results in a 50-80% reduction in the number of experiments required compared to conventional DOE methods.
Physics and chemistry simulation and analytical methods are now standard tools in the development of improved materials and formulations, and the use of machine learning (ML) is increasing. Each of these classes of method has advantages and drawbacks. In this webinar, we shared the results of a project that developed a framework allowing such methods to be combined, including case studies of studying different phases in an alloy and of predicting surfactant properties.
Alchemite™ is a novel machine learning method originally developed in the group of Dr Gareth Conduit at the University of Cambridge. The new Alchemite™ Academic Programme makes access to Alchemite™ affordable and easy for academic researchers. In this webinar, with live Q&A, Gareth Conduit introduces the method and provides examples of its application to academic research in materials science, chemistry, battery research, and life sciences.
Guest speaker: Lukas Kuerten, CPI
Machine learning methods generate insights from data to accelerate innovation in areas including drug discovery, translational medicine, formulation, and manufacturing processes. In this webinar, we explored the potential and some of the challenges of applying this technology. We heard a case study from Lukas Kuerten of CPI on using machine learning to predict viability of biopharmaceuticals early in development.
Guest speaker: Claire Hatfield, Johnson Matthey
Designing and developing new and improved products at pace is challenging when responding to rising costs, supply chain and regulatory constraints and the need to meet sustainability targets. Johnson Matthey presents case studies of the use of machine learning in designing catalyst formulations for clean air and life science applications. Benefits include increased process yields and reductions in experimental time and cost.
Guest speaker: Gabe Guss, Lawrence Livermore National Laboratory
In this webinar, we heard how machine learning can be applied to predict and optimise print parameters for additively-manufactured parts and in the development of materials for AM processes.
In this webinar, based on a presentation at the recent SCI Formulation Forum meeting, Dr Tom Whitehead discusses challenges associated with making the chemicals industry more sustainable and provides some examples of how machine learning is helping.
Guest speaker: Matthias Eisner, Yili
This webinar demonstrates the use of machine learning for formulation development, including a case study presentation from leading global food producer, Yili.
In this webinar, we drew on experience of real-world projects to demonstrate how Alchemite™ can be applied to applications including design of of new concrete formulations and development of steels and other alloys for structural applications. Key aims are to improve material properties while reducing energy consumption to support net zero objectives.
Designing and implementing consumer research and clinical studies is expensive, intrusive, and can introduce critical delays in getting products to market. In this webinar, we’ll see how machine learning (ML) can enable faster progress and insightful learnings. We share experience including from a recent project at BAT that applied ML as a tool when optimising clinical studies on the pharmacokinetic response of participants using a new product.
Optimising the chemistry, blending, and formulation of polymers can have profound impacts on the performance, economics, and sustainability of an array of products and industrial processes. Find out how innovative machine learning can enable a data-driven approach.
Join Intellegens and battery experts Deregallera as we discuss how machine learning can supercharge battery development. We will look at three key applications: (1) New battery materials and formulations; (2) Design of battery packs; (3) Battery management. We’ll discuss the challenges of applying machine learning to these problems and how these challenges are being overcome to improve charging capacity and energy density, lower development times and costs, and enable more efficient operation.
Guest speaker: Dr Richard Padbury, Lucideon
Find out how materials technology experts Lucideon are applying machine learning to research applications including controlled release drug delivery, battery technology, ceramics, polymers, and complex formulations. We discuss how machine learning might transform the future of material and process development and see a demonstration of the Alchemite™ deep learning technology.
Development organisations in engineering, manufacturing, and materials need to design new and improved materials and components and to wring every drop of performance from existing systems. In this webinar, we hear about a project to validate and apply Alchemite™ machine learning in pursuit of these goals at NASA Glenn Research Center.
We hear a lot about ‘big data’. But how can we solve problems where we have little data? This is common when exploring new territory — for example, designing materials, chemicals, formulations, and processes. In this webinar, we explore strategies and tools to meet this challenge, including demonstrations of the Alchemite™ deep learning software.
Find out how machine learning methods are being applied to solve key problems in the design, characterization, and processing of metal alloys and other advanced materials. With guest speaker from the Advanced Manufacturing Research Centre, discussing a joint project between the AMRC, Intellegens, and Boeing.
Find out how Alchemite™ helps to guide testing and find optimal formulations, quickly, overcoming the challenges of sparse and complex data. With guest speaker from Domino Printing Sciences.