Matmerize

Dating back to the ancient times, humanity has developed beautiful mathematical descriptions of our physical world. Yet, most AI approaches today largely disregard known physics. As a result, while AI models excel at interpolation, they struggle with extrapolation—unless massive datasets are involved.   This is why embedding physics into machine learning models is essential. 𝗠𝗮𝘁𝗺𝗲𝗿𝗶𝘇𝗲 𝗶𝘀 𝗲𝘅𝗰𝗶𝘁𝗲𝗱 𝘁𝗼 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲 𝘁𝗵𝗮𝘁 𝗗𝗲𝗲𝗽𝗣𝗵𝘆𝘀𝗶𝗰𝘀 𝗶𝘀 𝗻𝗼𝘄 𝗹𝗶𝘃𝗲 𝗼𝗻 𝗣𝗼𝗹𝘆𝗺𝗥𝗶𝘇𝗲—a no-code tool that makes it easier than ever to develop these models. Just bring your equations of interest and your data, and we'll take care of the rest.   To learn more, contact us at info@matmerize.com.

Dr. Chiho Kim, CTO of Matmerize, will present a talk titled "𝗣𝗼𝗹𝘆𝗺𝗲𝗿𝘀 & 𝗙𝗼𝗿𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗰𝘀: 𝗥𝗲𝗰𝗲𝗻𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 & 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗡𝗲𝘅𝘁 𝗦𝘁𝗲𝗽𝘀" at the 2024 KRICT R&D Forum on November 5th.   Dr. Kim’s presentation will cover how Matmerize’s Polymer Informatics platform PolymRize is driving advancements in polymer discovery and design, with a focus on:   - AI and machine learning models for polymer property prediction - Data management and overcoming data bottlenecks - Informatics-based strategies to achieve targeted property specifications - Developing sustainable polymers and improving processing with AI

Listen to our Director of Software and Algorithms, Dr. Rishi Gurnani, on It's a Material World podcast, as he explores the revolutionary potential of AI-assisted polymer discovery.   He discusses:   - Real-world case studies of AI in polymer discovery. - The complexities of predicting polymer properties. - Challenges in AI, including the importance of high-quality datasets.   Watch the episode here: https://lnkd.in/eZiAZqj9

Congratulations to the authors, Jessica LaLonde, Dr. Ghanshyam Pilania, and Dr. Babetta (Babs) Marrone, on their impactful review article on polyhydroxyalkanoates (PHAs)! Their work sheds light on the opportunities and challenges facing this promising class of biopolymers, highlighting PHAs' unique potential in advancing a circular plastic economy. With further research to unlock PHAs’ full range of applications, their contribution brings us closer to sustainable alternatives in materials science.

Thank you to Collaborative Real Estate and Tech Square ATL for hosting the Matmerize team at this year’s annual pumpkin carving event!   Stop by the Centergy One Building lobby until October 30th to cast your vote for the Matmerize pumpkin!

We are excited to share that Dr. Rishi Gurnani and Dr. Rampi Ramprasad have co-authored a recent groundbreaking paper, published in Nature Communications. The research showcases AI-assisted discovery of high-temperature polymer dielectrics, which are vital for energy storage in hybrid vehicles, aerospace, and electric power systems due to their high energy density and thermal stability.   Rishi, Director of Software Engineering at Matmerize, highlights the broader impact as follows: "This work demonstrates AI’s potential to develop materials that meet performance, cost, and sustainability goals. While our focus was on polymer dielectrics for electrostatic capacitors, the scope for applying AI in materials discovery is limitless. Our society is racing against the clock to meet global challenges—elimination of PFAs and microplastics, creation of a circular plastics economy, and more—AI can propel us forward."     Read the full paper here: https://lnkd.in/dc8yPJVX

Matmerize is enhancing its AI-powered platform, PolymRize, by adding new stock models that predict a wide range of molecular properties, including: •𝗥𝗼𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁 •𝗗𝗶𝗽𝗼𝗹𝗲 𝗺𝗼𝗺𝗲𝗻𝘁 •𝗜𝘀𝗼𝘁𝗿𝗼𝗽𝗶𝗰 𝗽𝗼𝗹𝗮𝗿𝗶𝘇𝗮𝗯𝗶𝗹𝗶𝘁𝘆 •𝗘𝗹𝗲𝗰𝘁𝗿𝗼𝗻𝗶𝗰 𝘀𝗽𝗮𝘁𝗶𝗮𝗹 𝗲𝘅𝘁𝗲𝗻𝘁 With the integration of these models and the powerful ASKPOLY tool, users can now quickly and intuitively predict properties in seconds, streamlining the discovery of new eco-friendly, energy-efficient polymers tailored for specific applications.

Dr. Rampi Ramprasad delivered an insightful plenary talk on building versatile, interpretable, and scalable AI models using their ‘‘multi-task learning’’ approach. His approach addresses both “forward materials problems” (predicting polymer properties) and “inverse materials problems” (identifying polymers that meet specific property criteria). With the near-infinite chemical space of polymers, finding the ideal combination of properties is a significant challenge. Dr. Ramprasad showcased how multi-task learning—where models simultaneously learn and predict multiple polymer properties—can streamline this process efficiently and effectively. This AI-driven methodology is transforming polymer design, helping to accelerate the discovery of high-performance, tailored materials.

Artificial Intelligence takes center stage once again! The 2024 Nobel Prize in Physics has been awarded to John J. Hopfield of Princeton University and Geoffrey E. Hinton of the University of Toronto for their contributions to artificial neural networks! Their innovative methods, rooted in physics, has far-reaching impacts, including in areas like material design, where they are instrumental in developing new materials with specific properties. At Matmerize, we’re excited by this recognition, as it underscores the transformative potential of AI in revolutionizing materials science, directly aligning with and expanding the possibilities of our own work. https://lnkd.in/dyUfXAtg

Dr. Rishi Gurnani recently delivered a standout keynote at the RISE 2024 conference. As our Director of Software and Algorithms, Dr. Gurnani shared insights on "Advancing Nonwovens Technology Using Artificial Intelligence," showcasing how AI is reshaping the industry and unlocking new material innovations. Thank you Bryan Haynes and Behnam Pourdeyhimi for recommending Matmerize to give the keynote talk at RISE 2024!