Best Materials Science Software for Amazon Web Services (AWS)

Compare the Top Materials Science Software that integrates with Amazon Web Services (AWS) as of October 2025

This a list of Materials Science software that integrates with Amazon Web Services (AWS). Use the filters on the left to add additional filters for products that have integrations with Amazon Web Services (AWS). View the products that work with Amazon Web Services (AWS) in the table below.

What is Materials Science Software for Amazon Web Services (AWS)?

Materials science software is designed to help scientists, engineers, and researchers analyze, model, and simulate the properties and behaviors of different materials. These tools are used to explore the relationship between a material's structure, its properties, and its performance under various conditions. Materials science software typically includes capabilities for molecular dynamics simulations, finite element analysis (FEA), and materials property databases, enabling users to predict how materials will behave in real-world applications. It is widely used in industries such as aerospace, automotive, electronics, and energy, where the development of new materials with specific characteristics is critical. By offering insights into the design and behavior of materials, these tools accelerate innovation, improve product quality, and reduce the risk of material failure. Compare and read user reviews of the best Materials Science software for Amazon Web Services (AWS) currently available using the table below. This list is updated regularly.

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    AQChemSim

    AQChemSim

    SandboxAQ

    AQChemSim is a cloud-native platform developed by SandboxAQ that leverages Large Quantitative Models (LQMs) grounded in physics and chemistry to revolutionize materials discovery and optimization. By integrating Density Functional Theory (DFT), Iterative Full Configuration Interaction (iFCI), Generative AI, Bayesian Optimization, and Chemical Foundation Models, AQChemSim enables high-fidelity simulations of molecular and material behaviors under real-world conditions. AQChemSim's capabilities include predicting performance under various stresses, accelerating formulation through in silico testing, and exploring sustainable chemical processes. Notably, AQChemSim has demonstrated significant advancements in battery technology by reducing lithium-ion battery end-of-life prediction time by 95%, achieving 35x greater accuracy with 50x less data.
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