Browse our archive of recorded webinars for case studies and presentations on machine learning applied to R&D.
For upcoming webinars, visit the events page.
Noise is the enemy of machine learning. Noise in the training data leads to uncertain predictions. However, noise can contain physical information, so we introduce a machine learning architecture that can extract crucial information out of noise itself. We apply this formalism to specify two concrete mixes: one has high resistance to carbonation, and the other has low environmental impact. The proposed mixes are experimentally validated. So watch this webinar to hear how Alchemite improves accuracy, requiring up to 85% fewer experiments to finalize a formation, saving you time and money.
Hear a case study from sustainability innovator Plantsea, learning how they are creating products based on a natural seaweed polymer to replace petroleum-based plastics and avoid microplastic pollution. See a demonstration of the Alchemite software from Intellegens, showing how ML can be used to reduce carbon footprint and energy consumption, minimize waste, and design formulations and processes to avoid use of chemicals that are harmful to human health or the environment.
Find out about a new software solution, developed and validated in a two-year collaborative project involving Intellegens, CPI, and leading pharma/biotech partners, that applies the power of machine learning to enable development of oligonucleotide manufacturing.
Agentic AI – the use of autonomous, tool-using AI systems with Large Language Models (LLMs) as decision engines – is emerging as a transformative force for R&D. Rather than isolated models, agentic systems coordinate and automate workflows across diverse software and data ecosystems. In this webinar, we share early insights from industry discussions and show results from our development work in this area at Intellegens.
Learn from the experience of Fuchs, the world’s largest independent lubricant manufacturer, as it applies machine learning to accelerate R&D. Dr Richard Bellizzi explains how Alchemite™ machine learning has aided experimental design and the development of improved lubricant formulations.
Machine learning accelerates innovation in chemicals, materials, life sciences, and manufacturing by delivering deep data insights, guiding experiment, and finding new solutions to complex optimization problems. But just getting started can be difficult. There are many challenges in building a machine learning model from real, messy, experimental or process data. In this webinar, we outline some of those difficulties and how they are overcome in the Alchemite Suite software.
This online session demonstrated a new generation of machine learning tools for R&D in chemicals, materials, life sciences, FMCG, and manufacturing. The newly-launched Alchemite™ Suite delivers a set of supremely easy-to-use software apps targeted on key tasks for development teams.
Hear from leading steel and technology provider voestalpine as they explain how they have applied machine learning (ML) in additive manufacturing (AM) applications.
Learn about simple-to-use software to save experimental time and cost and find optimal formulations. With applications from dairy products to aerospace coatings, pharmaceuticals to paint, plastics to perfume.
See how machine learning is powering innovative 3D printing technology to enable an autonomous manufacturing process. In partnership with Photocentric.
See how machine learning-led DOE can be applied in just a few button-clicks, cutting experimental workloads by 50-80%.
Learn how machine learning can design new formulations and chemicals, reduce experimental workloads by 50-80%, and enable response to regulatory constraints. Domino Printing Sciences give a case study presentation on their use of machine learning to develop ink formulations.
In this webinar, we demonstrate ML for materials design and development and hear a case study presentation from Welding Alloys Group. This details a project in which ML found an improved, cost-effective, and more environmentally-friendly hardfacing material.
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: 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.
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.
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.
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.
