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.
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.
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.
Hear from leading steel and technology provider voestalpine as they explain how they have applied machine learning (ML) in additive manufacturing (AM) applications.
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.
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.
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.
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.
To streamline the development of its biomaterials, Modern Synthesis Ltd. deployed Intellegens’ Alchemite machine learning platform to master the complex parameter spaces of biopolymer formulations. By extracting critical insights from sparse and noisy experimental data, Alchemite drastically reduced trial-and-error cycles, successfully accelerating the discovery of high-performance, sustainable materials. In this webinar, we’ll hear from Dr Ioannis Zampetakis of Modern Synthesis how machine learning was deployed and applied, share outcomes from this process, demonstrate the Alchemite platform, and answer your questions in a live Q&A
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.
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 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.
See how machine learning is powering innovative 3D printing technology to enable an autonomous manufacturing process. In partnership with Photocentric.
An overview of Alchemite™ Suite – a series of easy-to-use apps that slash development times and break through R&D bottlenecks. We share case studies, show the software in action, and recap big developments from the last year like integration with Excel and generative AI.
Learn how Alchemite™ Designer enables AI-led Design of Experiments that has delivered 5-fold speed-ups in experimental programs without requiring detailed data science or statistical knowledge.
See how tools such as the Alchemite™ Explorer app are enabling teams that develop formulated products to explore design space with maximum efficiency, applying innovative machine learning in ‘what if?’ studies that find new solutions and reduce time-to-market.
Discover how encoding the chemistry of formulation ingredients directly into models enables better generalisation across ingredients and more intelligent exploration of formulation space. Attendees will see how combining chemistry with data-driven design can reduce experimental effort, accelerate development, and unlock improved formulations across a range of industries.
The comprehensive ML tools and analytics of Alchemite™ Innovator maximize the value of research data, uncovering vital relationships and guiding research decision-making. Such insights delivered 40% cost savings in manufacturing and testing to one customer. Join us hear about this and other examples.
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.
We present practical use cases in battery and materials development, including property prediction, experiment prioritization, and candidate selection. The session also includes a live demo of Alchemite, showing how it can be used to explore data, generate predictions, and support real-time decision-making. This webinar recording will be of interest to researchers and engineers working on batteries, advanced materials, and sustainable technology.
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.
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.
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.
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.
