Benefits of using a materials discovery platform

A UK-based deep-tech company, Materials Nexus, has unveiled MagNex—a new rare-earth-free permanent magnet—developed with the help of AI. And they didn’t just save time—they accelerated discovery by a staggering 200 times compared to traditional methods. The demand for permanent magnets is booming, especially as electric vehicles, wind turbines, robots, and HVAC systems grow more popular. These magnets are typically made with rare earths like neodymium and dysprosium—materials that are expensive to extract and dominated by China, which processes nearly 90% of the world’s supply. This creates major supply chain vulnerabilities. That’s where AI steps in. Materials Nexus’ platform screened over 100 million rare-earth-free combinations to find a viable alternative, balancing performance, cost, availability, and environmental impact. In just three months—and with support from the University of Sheffield—they moved from discovery to real-world testing of MagNex. According to the company, MagNex offers a 70% cut in material-related carbon emissions and costs just 20% of what traditional rare-earth magnets require in raw materials. The breakthrough could offer industries a way out of rare-earth dependence—and much faster than ever before. Materials Nexus believes its AI can speed up the search for sustainable materials across sectors, from magnets to semiconductors. #RMScienceTechInvest https://lnkd.in/de4pr3hS

Accelerating computational materials discovery with machine learning and cloud high-performance computing. In materials science, accelerating the discovery processes is a pivotal advancement toward meeting global technological demands. The latest study by Chi Chen and colleagues utilizes state-of-the-art ML models and traditional physics-based models, processing over 32 million candidates to predict approximately half a million potentially stable materials with targeted properties. The team's focus on solid-state electrolytes for battery applications led to the identification of 18 promising candidates with new compositions, effectively rediscovering a decade's worth of collective knowledge in the field as a byproduct. Key to their method is a two-stage screening workflow: initial ML-based screening predicts stable candidates, followed by density functional theory (DFT) calculations for high-accuracy validation. This blend of ML and physical simulations on cloud platforms marks a significant shift toward more dynamic, scalable, and accessible materials discovery processes. This approach not only highlights the integration of digital technologies in scientific research but also sets new standards for how rapidly and efficiently materials can be screened and validated in the lab. Link to the paper: https://lnkd.in/dAwF33t4 #MaterialsScience #ComputationalMaterialsDiscovery  #ComputationalChemistry #MachineLearning #HighPerformanceComputing #CloudComputing #AIforMaterials #PhysicsBasedModels #MaterialDiscovery #BatteryTechnology #SolidStateElectrolytes #ElectrolyteResearch #DFT #ScalableSolutions #AdvancedMaterials #MaterialsEngineering #ComputationalScience

The future of scientific research just shifted into overdrive Researchers at North Carolina State University have developed an AI-powered autonomous laboratory that accelerates materials discovery by 1,000%, fundamentally changing how we approach some of humanity's most pressing challenges The breakthrough lies in replacing traditional steady-state experiments with dynamic flow experiments, where chemical mixtures are continuously varied through the system and monitored in real time Instead of capturing a single snapshot, this approach creates "a full movie of the reaction as it happens," generating 20 data points where conventional methods would produce just one This isn't just an incremental improvement, it's a paradigm shift The system's streaming-data approach enables machine learning algorithms to "make smarter, faster decisions, honing in on optimal materials and processes in a fraction of the time" The implications ripple across industries critical for our future: - Faster battery development for electric vehicles - Accelerated solar panel efficiency improvements - Rapid advancement in sustainable manufacturing materials. Professor Milad Abolhasani, who led this research, envisions a future where "scientists could discover breakthrough materials for clean energy, new electronics, or sustainable chemicals in days instead of years, using just a fraction of the materials and generating far less waste" We're witnessing AI evolve from analyzing existing data to actively conducting scientific research itself This autonomous lab represents the convergence of artificial intelligence with physical experimentation, a combination that could redefine the pace of innovation across multiple sectors The research, published in Nature Magazine Chemical Engineering, demonstrates that AI's greatest impact may not be in replacing human tasks, but in amplifying human capability to solve complex problems at unprecedented speed Read more about this breakthrough: https://lnkd.in/dkQKaD-d

From energy storage to carbon capture and catalysis, the ability to create novel materials with precise, functional properties is critical for addressing some of the world’s most pressing challenges.  Microsoft has unveiled MatterGen, a groundbreaking generative AI model that redefines how we can design and discover new physical materials—potentially paving the way for transformative advancements in #sustainability. Some applications for MatterGen are: ▶️ Energy Storage: Design materials with high lithium-ion conductivity, essential for developing next-generation batteries with improved efficiency and capacity. ▶️ Carbon Capture: Aid in developing efficient catalysts for carbon capture technologies, helping reduce greenhouse gas emissions. ▶️ Catalysis: Generate materials with specific catalytic properties, enhancing processes like water splitting for hydrogen production, contributing to cleaner energy solutions. ▶️ Mechanical Properties: Design materials with high bulk modulus, leading to the development of durable and lightweight materials for sustainable construction and transportation. For those working in and interested in #deeptech and #materialscience, check out the new platform and paper published in Nature Magazine: MatterGen Platform ➡️ https://lnkd.in/gahjMBrS Academic Paper ➡️ https://lnkd.in/gXXPeBWb

"...the randomized introduction of a new materials discovery technology to 1,018 scientists in the R&D lab of a large U.S. firm. AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product in-novation. These compounds possess more novel chemical structures and lead to more radical inventions. However, the technology has strikingly disparate effects across the productivity distribution: while the bottom third of scientists see little benefit, the output of top researchers nearly doubles. Investigating the mechanisms behind these results, I show that AI automates 57% of “idea-generation” tasks, reallocating researchers to the new task of evaluating model-produced candidate materials. Top scientists leverage their domain knowledge to prioritize promising AI suggestions, while others waste significant resources testing false positives." https://lnkd.in/eGpmkMF3 https://lnkd.in/efcWP2if

Unlocking Innovation with AI: A Breakthrough in Materials Discovery A recent study from MIT demonstrates how artificial intelligence is revolutionizing scientific discovery and product innovation. By deploying an AI-powered materials discovery tool to over 1,000 scientists in a leading R&D lab, the results were game-changing: • 44% increase in new materials discovered • 39% more patent filings • 17% growth in product prototypes Beyond accelerating the pace of innovation, the AI tool helped scientists push boundaries. It enabled the discovery of bold, novel materials—particularly in polymers—rather than iterative improvements on existing solutions. This marks a shift toward radical innovation, empowering scientists to explore entirely new design spaces. AI also demonstrated its potential to scale R&D processes efficiently, by automating a majority of idea-generation tasks and reallocating researchers to higher-value activities like evaluating model-generated candidates. The result? A significant leap in productivity without additional resources. This study underscores the transformative role AI is playing in chemistry and materials science. It’s not just about speeding up what we already do—it’s about rethinking what’s possible. Research paper in comments below #AI #Innovation #MaterialsScience #Polymers #FutureOfR&D

Microsoft Research continues to lead groundbreaking innovation in materials discovery. With MatterGen, a generative AI model for inorganic materials design, the team has successfully created new compounds with unparalleled precision and efficiency. Unlike traditional screening methods, MatterGen generates novel materials with prompts tailored to specific chemical, mechanical, electronic, and magnetic properties, enabling scientists to explore a vast range of previously unknown materials. This expanded access will massively impact the discovery and design of new materials - from pharmaceuticals to batteries, magnets, and fuel cells. Another exciting example of how AI is flipping the script on scientific discovery! https://lnkd.in/gKcwvz2S

𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐌𝐚𝐭𝐞𝐫𝐢𝐚𝐥 𝐃𝐞𝐬𝐢𝐠𝐧: 𝐁𝐞𝐲𝐨𝐧𝐝 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐰𝐢𝐭𝐡 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭’𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐆𝐞𝐧 For years, Material Design has been synonymous with innovation—driven by quantum simulations and advanced computational models. But Microsoft’s latest breakthrough, MatterGen, is redefining what’s possible. MatterGen leverages Generative AI to design entirely new materials, achieving results that surpass even what quantum computing aimed to deliver. Instead of relying solely on physics-based simulations, it uses deep learning models to predict, generate, and optimize materials with unprecedented speed and accuracy. This advancement accelerates the discovery process, allowing new materials to be designed and tested virtually in a fraction of the time required by traditional methods. It also opens the door to prioritizing sustainability, enabling the creation of eco-friendly and energy-efficient materials that drive greener innovation. The impact spans across industries—from stronger, lighter materials in aerospace to more efficient batteries for electric vehicles—unlocking possibilities that were once unimaginable. This shift marks a pivotal moment where AI doesn’t just optimize existing processes—it creates entirely new possibilities. We’re moving beyond the constraints of quantum mechanics into a future where generative models redefine how we build, manufacture, and innovate. What industries do you think will benefit most from AI-driven material design? #ai #materialdesign #productivity