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Composite resin formulation with AI and machine learning in action

Posted on May 15, 2026May 18, 2026 by Anonymous
Webinar

Composite resin formulation with AI and machine learning in action

CalendarDate & Time
    • Jun 17, 2026,   8:00 AM PDT | 11:00 AM EDT
    • Jun 23, 2026,   15:00 CEST | 14:00 BST | 18:30 IST
LocationLocation
  • Virtual

If you have trouble registering, please email marketing@schrodinger.com

Composite performance depends heavily on matrix properties that govern processability and operational stability. Increased digitization is providing clear value across industries, but successful application in composite resin formulations requires a clear understanding of the key questions and insight into the critical design factors. Combining expert know-how and atomic-level detail with powerful AI and ML tools enables resin formulation teams to maximize successful design initiatives.

This webinar with demos will demonstrate how integrating ML with molecular simulation enables faster, more informed development of next-generation resin formulations.

Key Highlights

  • Discover how AI/ML aids in designing polymer and ceramic matrix composites, focusing on critical matrix properties
  • Learn why successful resin formulation requires increased digitization for both experimentation and simulation
  • See how integrating chemistry expertise with AI/ML tools leads to better decision-making and outcomes
  • Explore how ML and molecular simulation accelerate the development of new resin formulations
Register – JUN 17, 11:00 AM EDT (AMER)
Register – JUN 23, 14:00 BST (EMEA)

Our Speaker

Andrea Browning

Senior Director of Polymers and Soft Matter, Schrödinger

Andrea leads initiatives in polymer and soft matter simulations. Prior to Schrödinger, Andrea was a lead research engineer and project manager at Boeing, where she focused on translating engineering problems into fundamental materials insights. She brings over a decade of experience in connecting industrial and engineering problems to root materials issues and how simulations can be used to inform industrial decisions. Andrea earned her Ph.D. in Chemical Engineering from the University of California, Santa Barbara, where she was a National Science Foundation Graduate Research Fellow.

Integrating AI and Machine Learning to Accelerate Composite Resin Formulation

Posted on April 2, 2026May 11, 2026 by Anonymous
Webinar

Integrating AI and Machine Learning to Accelerate Composite Resin Formulation

CalendarDate & Time
  • May 13th, 2026
  • 8:00 AM PDT | 11:00 AM EDT | 4:00 PM BST | 5:00 PM CEST
LocationLocation
  • Virtual
Register

Schrödinger is excited to be hosting a webinar in collaboration with Composites World, taking place on May 13th at 11:00AM EDT.

Artificial intelligence and machine learning have entered into everyday usage, but what impact can they have on polymer and ceramic matrix composites development?

Composite performance depends heavily on matrix properties that govern processability and operational stability. Increased digitization is providing clear value across industries, but successful application in composite resin formulations requires a clear understanding of the key questions and insight into the critical design factors. Combining expert know-how and atomic-level detail with powerful artificial intelligence and machine learning tools enables resin formulation teams to maximize successful design initiatives.

This webinar will demonstrate how integrating machine learning with molecular simulation enables faster, more informed development of next-generation resin formulations.

Agenda:

  • Where AI and machine learning add value: Discover how these technologies aid in designing polymer and ceramic matrix composites, focusing on critical matrix properties.
  • Digitization: Learn why successful resin formulation requires increased digitization for both experimentation and simulation.
  • Integration: See how combining chemistry expertise with AI and machine learning tools leads to better decision-making and outcomes.
  • Acceleration: Explore how machine learning and molecular simulation accelerate the development of new resin formulations.

Our Speaker

Andrea Browning

Senior Director of Polymers and Soft Matter, Schrödinger

Andrea Browning, senior director of polymers and soft matter at Schrödinger, leads initiatives in polymer and soft matter simulations. Before joining Schrödinger, Browning was a lead research engineer and project manager at Boeing, where she focused on translating engineering problems into fundamental materials insights. She brings more than a decade of experience in connecting industrial and engineering problems to root materials issues and how simulations can be used to inform industrial decisions. Browning earned her doctorate in chemical engineering from the University of California, Santa Barbara, where she was a National Science Foundation Graduate Research Fellow.

Register

Formulation machine learning and optimization for accelerated materials discovery

Posted on April 1, 2026May 20, 2026 by Anonymous
Webinar

Formulation machine learning and optimization for accelerated materials discovery

CalendarDate & Time
  • June 25th, 2026
  • 8:00 AM PDT | 11:00 AM EDT | 4:00 PM BST | 5:00 PM CEST | 8:30 PM IST
LocationLocation
  • Virtual

If you have trouble registering, please email marketing@schrodinger.com

Register

Finding new chemistries and materials, or reformulating existing chemicals, with good properties necessitate extensive trial-and-error experimentation that is costly and inefficient when faced with expansive possibilities. Schrödinger’s Materials Science Suite is a physics-informed, data-driven platform that offers an alternative, cost-effective solution by helping to guide the next round of experiments with scalable, automated digital tools.

Join our upcoming webinar to learn how your R&D team can leverage automated data driven solutions to guide the design of versatile chemical solutions. In this webinar, we will focus on how materials informatics, formulation machine learning, and optimization tools can be applied to create accurate chemical prediction models, screen large libraries, and provide optimized solutions based on desired target properties. We will demonstrate how easy it is to apply these tools using experimental datasets across broad material science applications, including polymers, consumer goods, batteries, and beyond.

Join us and see demos on:

  • Training accurate drug solubility ML models for pure or binary liquids as a function of temperature
  • Applying optimization tools to tailor formulation design for desired target properties, which provides rapid suggestions for the next best experiments
  • Transferability of these tools to be applicable to diverse chemical domains, such as  polymers, consumer goods, batteries, and more, which demonstrates their broad usefulness for diverse applications

Who should attend:

  • R&D leaders
  • Innovation managers
  • Digitization managers
  • Computational materials scientists
  • Materials scientists
  • Pharmaceutical scientists
  • Formulation scientists
  • Synthetic chemists

Our Speaker

Alex K. Chew

Principal Scientist II, Schrödinger

Alex K. Chew is currently a Principal Scientist II at Schrödinger, Inc., and he is passionate about accelerating materials design by integrating physics-based modeling and machine learning algorithms. Alex earned his B.S./M.S. from NYU Tandon School of Engineering in 2016, followed by his Ph.D. in Chemical Engineering from the University of Wisconsin-Madison in 2021. During his graduate studies working with Prof. Reid C. Van Lehn at UW-Madison, Alex focused on integrating molecular dynamics simulation and machine learning tools to engineer new nanomaterials for biomedical applications and new solvent-mediated processes to improve the conversion of biomass to fuel. Since joining Schrödinger after UW-Madison, Alex has been involved in leading physics-informed machine learning solutions for industrial applications, designing tutorials to help customers leverage our machine learning tools, and engineering new machine learning workflows to expand our scientific software capabilities for a wide range of materials applications, with an emphasis in computer-aided formulations design. Alex has expertise in molecular dynamics simulations and machine learning algorithms, has co-authored more than 16 peer-reviewed publications, and has been cited more than 1000 times.

Register

Advancing battery materials innovation using charge-aware machine learning force fields

Posted on September 25, 2025November 5, 2025 by Anonymous

OCT 29, 2025

Advancing battery materials innovation using charge-aware machine learning force fields

Batteries are fundamental technology – powering everything from our personal electronics to electric vehicles, as well as large-scale grid storage systems for renewable energy integration. However, current battery technologies, primarily lithium-ion batteries, face significant limitations in performance, safety, cost, and reliance on scarce materials like cobalt. Therefore, innovation in battery materials is the key to unlocking the next generation of energy storage.

In this webinar, we will demonstrate how Schrödinger is utilizing an integrated computational approach combining physics-based molecular modeling with machine learning force fields (MLFFs) to address key challenges in battery materials design. We will introduce Schrödinger’s latest advancements in MLFFs, featuring charge recursive neural networks (QRNN) and the recently released Message Passing Network with Iterative Charge Equilibration (MPNICE) architectures, which incorporate explicit electrostatics for accurate charge representations.

Moreover, we will showcase several industry-relevant case studies highlighting the application of MLFFs to precisely model the structure and properties of electrolyte materials (liquid, polymer, and inorganic solid-state electrolytes), cathode coatings, and electrode materials. We will also explore how MLFFs facilitate large-scale simulations, allowing scientists to investigate the impact of defects and heterogeneities on crucial properties like Li-ion transport, paving the way for the efficient design of next-generation battery materials and chemistries.

Webinar Highlights:

  • How Schrödinger combines physics-based modeling with machine learning force fields to drive battery materials discovery
  • Schrödinger’s latest MLFF technologies, including QRNN and MPNICE
  • Real-world case studies modeling electrolytes, cathode coatings, and electrode materials
  • How MLFFs facilitate large-scale simulations, such as the investigation of Li-ion transport

Our Speaker

Garvit Agarwal

Principal Scientist, Schrödinger

Garvit Agarwal, Principal Scientist and Scientific Lead for Energy Storage at Schrödinger, works to extend and apply molecular modeling tools for the accelerated discovery of next-generation clean energy technologies. Garvit obtained his Ph.D. in Materials Science and Engineering from the University of Connecticut. He worked as a post-doctoral researcher in the Materials Science Division at Argonne National Laboratory prior to joining the Materials Science team at Schrödinger.