Best Materials Science Software - Page 2

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.

Connect the virtual and real worlds with the Dassault Systèmes 3DEXPERIENCE® platform to Collaborate, Model, Optimize and Perform operations. Define the plant layout, flow, assets and resources needed to produce products efficiently and in a safe environment. Enrich the product and resource definition; define and validate a process plan and create work instructions to meet production goals. Supply Chain Planning & Optimization across all planning horizons; gain visibility with planning and scheduling to minimize disruptions. Transform global production operations to achieve and sustain operational excellence with Manufacturing Operations Management. Create, manage, and govern operational processes on a global scale.

DIGIMU® generates digital polycrystalline microstructures representative of the material's heterogeneities (compliance with the topological characteristics of the microstructure). The boundary conditions applied to the REV are representative of that experienced by a material point at the macroscopic scale (thermomechanical cycle of the considered point). Based on a Finite Elements formulation, the various physical phenomena involved during metal forming processes are simulated (recrystallization, grain growth, Zener pinning due to second phase particles, etc.). In order to improve digital precision and to reduce computation times, the software is capable of providing a precise description of the interfaces (grain boundaries) while using an appropriate number of elements thanks to a fully automated anisotropic meshing and remeshing adaptation technology.

Microsoft Discovery is a new agentic platform designed to revolutionize research and development (R&D) by empowering scientists and engineers with AI-driven collaboration and high-performance computing (HPC). Built on Azure, this platform enables researchers to work alongside specialized AI agents that help accelerate the discovery process through advanced knowledge reasoning, hypothesis formulation, and experimental simulations. The platform's graph-based knowledge engine facilitates complex, contextual reasoning over vast amounts of scientific data, promoting transparency and accountability while speeding up the discovery cycle. By automating and enhancing research tasks, Microsoft Discovery offers an extensible, enterprise-ready solution that integrates seamlessly with existing tools and datasets.

Transform drug discovery and materials research with advanced molecular modeling. Our physics-based computational platform integrates differentiated solutions for predictive modeling, data analytics, and collaboration to enable rapid exploration of chemical space. Our platform is deployed by industry leaders worldwide for drug discovery, as well as for materials science in fields as diverse as aerospace, energy, semiconductors, and electronics displays. The platform powers our own drug discovery efforts, from target identification to hit discovery to lead optimization. It also drives our research collaborations to develop novel medicines for critical public health needs. With more than 150 Ph.D. scientists on our team, we invest heavily in R&D. We’ve published over 400 peer-reviewed papers that demonstrate the strength of our physics-based approaches, and we’re continually pushing the limits of computer modeling.