Explore our latest events and insightful content to stay ahead in the world of materials science.
Why is your R&D laboratory's productivity down? Missing reagents, expired chemicals, and wasted hours searching for supplies are common reasons. These issues not only slow down research but also drive up operational costs. However many labs continue to rely on outdated tracking methods for managing inventory, such as spreadsheets and manual logs, that make it difficult to maintain accurate records and keep up with growing material demands.
Scientific research is more data-driven than ever, and laboratories worldwide produce vast amounts of information daily. Yet, many still rely on outdated record-keeping systems or fragmented digital tools, which leads to inefficiencies, mistakes, and compliance risks. In high-stakes environments, disorganized records can slow innovation and inflate costs—and even put workers at risk.
Selecting the right metal for R&D and manufacturing is more complex than ever. Choosing the wrong material can cause costly delays, failed prototypes, and compromised product performance. In industries where precision is crucial—such as aerospace, pharmaceuticals, and advanced materials—understanding a metal's mechanical properties is fundamental to ensuring durability, compliance, and reliability.
Sometimes, small things have major consequences: A failed drug batch or a structural flaw in an advanced material can often be traced back to a seemingly minor deviation in the production process, like a slight shift in formulation or an overlooked equipment calibration.
Today’s laboratories face growing challenges navigating the complexities of materials research. Even minor missteps during the R&D process can lead to compromised results, regulatory delays, or substantial financial and reputational risks. It’s more critical than ever for laboratories to ensure precision, reliability, and operational consistency. That’s where the Good Laboratory Practice (GLP) framework comes in.
Lab automation software solutions are specialized platforms designed to automate and streamline various laboratory processes. Automation plays a vital role in managing laboratory workflows, data collection, and analysis, and is crucial for staying competitive.
R&D processes today are more demanding than ever. Laboratories must balance increasing workloads, tighter deadlines, and growing complexity in managing data and workflows. At the same time, ensuring accuracy and regulatory compliance remains non-negotiable. Traditional manual processes introduce inefficiencies that delay progress and hinder innovation. The solution? Lab automation.
The chemical industry is evolving rapidly, and there have been recent breakthroughs in sustainable plastics, advanced composites, and other innovative materials. Testing these materials for strength, flexibility, and durability is essential for meeting rigorous safety and performance standards. In laboratories around the world, Universal Tensile Machines (UTMs) provide the precise measurements necessary for these evaluations.
What if your manufacturing process didn't just meet demand but gave you a competitive advantage? The masterbatch manufacturing process is essential for producing high-performance materials with consistent quality, efficient production, and sustainability compliance. Optimizing this process allows manufacturers to unlock innovation and adapt to the growing demands of modern industries.
Maven is a new agentic AI capability that searches, visualizes, and analyzes internal and external data through a user-friendly interface, accelerating the way organizations act on knowledge at scale.
Discover how AI-Guided R&D accelerates food innovation.
Let's discuss how we can help you accelerate your packaging R&D.
Explore how AI is accelerating discovery in specialty chemicals – join us at Chemspec Europe
Discover how AI-Guided R&D is reshaping coatings innovation – meet us at ACS 2026.
Join us for a hands-on discussion on using AI in real cosmetics R&D processes.
Join us for a practical discussion on embedding AI into real plastics R&D workflows.
Watch the webinar on-demand.
Join Jeff and Omri to discover how AI-powered materials informatics tools are transforming Advanced Materials R&D by reducing experimental cycles and accelerating formulation discovery.
See how AI-Guided R&D is transforming plastics and advanced materials – meet us at K 2025.
We are delighted to be exhibiting at the Advanced Materials Show in Columbus, Ohio (USA) on September 30 - October 1. Visit us at Booth 428!
Discover how AI-powered materials informatics tools are transforming Advanced Materials R&D by reducing experimental cycles and accelerating formulation discovery. Learn how smart data and smarter tools are reshaping materials innovation.
The webinar is now available on demand.
The gathering of the coatings and paint industry from 25 – 27 March 2025
The annual conference for coatings formulators and manufacturers to discuss innovations in coatings technology
Materials Zone, a scientific AI/ML-based collaborative platform, harnesses data, technological tools, and years' worth of extensive know-how and experience to accelerate R&D and manufacturing processes and reduce costs across industries. Learn more about how the platform can help you achieve meaningful AI/ML insights and drive better decision-making.
Digitizing and automating the lab space
How to achieve meaningful AI/ML insights to accelerate R&D and manufacturing processes. A presentation by Assaf Anderson from Materials Zone.
This symposium invites contributions on new challenges to enhance the performance of perovskite-based photovoltaic cells, light-emitting devices, and related optoelectronic devices for energy harvesting & production and optical information processing. It will further consider theoretical modelling of new materials or analytic approach to material and device degradation. Perovskite-like materials inspired from halide perovskite crystals will also be discussed in the scope of the symposium.
Digital solutions for additive manufacturing
From Materials Data to AI Accelerated Results, Fast! Materials Informatics Platform (MIP) is an organizational platform that accelerates materials/products innovation.
From Materials Data to AI Accelerated Results Faster! AI/ML Acceleration for the Plastics Industry.
Designing the Nanomaterials of the Future - Opportunities from Artificial Intelligence. We are proud to host keynote speakers such as Prof. Edward Sargent, Prof. Marjolein Djistra, Dr. Emory Chan, and Prof. Liberato Manna. Don’t miss out! Register in the link below for free participation-
DIGITAL. AUTOMATED. CONNECTED.
Modern semiconductor and display development is constrained by complex materials challenges and lengthy experimental cycles. R&D teams face intense pressure to optimize processes and materials faster, et traditional trial-and-error approaches struggle to keep pace. Materials informatics – the application of AI/ML to materials data – offers a breakthrough. It leverages vast materials and process data to predict outcomes and guide experiments, reducing development time.
As the push for sustainability grows, semiconductor and photovoltaic (PV) industries are under increasing pressure to incorporate recycled materials into their products. Embracing recycled feedstocks (like reclaimed silicon, metals, or plastics) can reduce waste and carbon footprint. However, doing so in high-performance electronics is non-trivial – recycled materials often exhibit variability and unknown impacts on product quality. This white paper examines how an AI-driven materials informatics approach can de-risk and optimize the integration of recycled resources.
The concept of Lean, originating from Toyota's manufacturing processes, has revolutionized industries worldwide by optimizing efficiency and minimizing waste. In this white paper, we explore the journey of Lean principles from their inception to their application in materials research and development (R&D).
Materials informatics plays a critical role in accelerating materials discovery, development, and optimization. In the age of digital transformation, ensuring the security of data and information is a top priority. Our company recognizes the importance of robust security measures to protect intellectual property, sensitive research data, and client information and hereby earns the trust of our customers by making data security our top priority.
This white paper advocates for the integration of AI with active learning in materials science and chemistry research. By addressing the limitations of traditional trial-and-error methods, AI offers a transformative approach that can greatly enhance the material product development process. It accelerates the materials discovery process, leading to faster and more cost-effective innovations across various industries. As technology progresses, the fusion of AI and materials science promises a more efficient and promising future for materials research.
AI materials discovery uses artificial intelligence, machine learning, and materials data to help R&D teams discover, design, predict, and optimize materials with desired properties. It can support innovation in property prediction, formulation optimization, inverse design, next-best-experiment recommendations, and AI-guided product development.
AI in materials science applies machine learning, data science, and automation to materials R&D so teams can predict outcomes, prioritize experiments, and improve decision-making across the R&D lifecycle.
Material performance issues rarely start in production, but that is often where they are discovered. A packaging film that looked stable in early testing may warp during temperature cycling, or a capsule coating that passed initial quality checks may become brittle during low-temperature transit. These failures are expensive and disruptive, and they are often avoidable.
The Cost of Poor Quality (COPQ) is the total financial loss a company incurs when materials, processes, or end products fail to meet quality standards. The metric represents significant hidden costs for manufacturers in complex, data-heavy manufacturing and R&D industries such as chemicals, advanced materials, or FMCG products. AI-guided R&D platforms like MaterialsZone can effectively address the data and process issues that lead to excessive COPQ.
Beauty has a hidden cost. The products people use every day can raise difficult questions about what lies behind their performance and presentation, especially when green claims are used to reassure consumers. Conventional cosmetics can have an impact in many ways, from hard-to-recycle packaging to manufacturing choices that generate more waste and emissions.
For companies selling consumer products, making an environmental claim is common. Ensuring that those claims stand up against scrutiny, however, is not. Regulators across the European Union are clamping down on sustainability claims used in consumer marketing. A lack of transparency has weakened consumer trust and made it harder for responsible companies to differentiate genuine environmental improvements from the misleading claims known as greenwashing.
Your company is developing a commercial product that contains multiple chemicals and materials for sale in the European Union. One day, your procurement manager receives an email from a supplier explaining that a critical raw material is being discontinued. Everything seems manageable, as the replacement they offer “should be equivalent.” The R&D team swaps out the materials to keep the project moving. Two weeks later, QA flags an issue during pre-launch checks. The updated formulation now contains a restricted substance above a legal threshold, meaning your team cannot immediately demonstrate what is in the product.
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