Ansys partner webinar - Machine learning for materials and processes
Date: Tuesday, 21 March 2023
Time: 2pm UK time // 10am US Eastern
Intellegens is participating in the Ansys Connect Series – join our partner Ansys online to hear us present on ‘Using machine learning for designing new materials and processes’.
Case studies of machine learning for sustainable technologies
Date: Tuesday, 18 April 2023
Time: 4pm UK time // 11am US Eastern
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 will present 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.
Apply Alchemite™ machine learning to academic research
Date: Wednesday, 14 June 2023
Time: 4pm UK time // 11am US Eastern
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 will introduce the method and provide examples of its application to academic research in materials science, chemistry, battery research, and life sciences.
Optimising Additive Manufacturing processes with machine learning
Guest speaker: Guss Gabe, 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.
Sustainable formulation development with machine learning
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.
Machine learning for formulation development - A food industry case study
This webinar demonstrates the use of machine learning for formulation development, including a case study presentation from leading global food producer, Yili.
Machine learning for construction chemicals and structural materials
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.
Machine learning for efficient clinical study design
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 experimental programs in chemical and formulation development
Focusing on the development of new and improved chemicals and formulated products, we walk through a complete workflow for building a machine learning model, then improving it through focused acquisition of new data. Such an approach typically results in 50-80% fewer experiments than conventional methods, and has been proven in applications including specialty chemicals, food and beverage, paints, dyes, fragrances, cosmetics, and plastics.
Turning uncertainty into good decisions for chemicals, materials, and formulations R&D
There are in-built uncertainties in the data that we collect and the predictions that we make when developing chemicals, materials, or formulations. But that should not stop us from making good decisions using this information. We can even extract useful knowledge from what appears to be noise in the data. Intellegens CTO Dr Gareth Conduit will discuss this topic, delving into how we can gain useful insights with the right tools and approaches to uncertainty and noise in machine learning studies.
Machine learning for polymers R&D
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.
Towards net zero for manufacturing processes with machine learning
Machine learning can make products and processes more efficient, reducing waste, energy usage, and resource consumption. Join us to find out how, with insights from the recent ‘Build UK India’ project focused on reducing wastage in an industrial manufacturing process for aerospace.
3 ways that machine learning can supercharge battery development
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.
Transforming materials and process development through machine learning
In this webinar, we hear from guest speaker Dr Richard Padbury 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.
Machine learning for material and component design (with NASA)
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.
Solving the 'small data' problem for formulations, materials, and processes
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.
Machine learning and digitalisation strategies
Is your digital transformation strategy actually slowing you down? Many technical organisations are investing in digitalisation. In this webinar, we discuss how machine learning interacts with digitalisation projects.
Deep learning for materials development and additive manufacturing
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.
Efficient formulation design using machine learning
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.
How Alchemite™ is Revolutionising Design of Experiments with AI
Find out how Alchemite™ guides Design of Experiment campaigns.
Applications of Alchemite™ to Drug Discovery
Introducing the Alchemite™ deep learning method for sparse experimental data.