Best Materials Science Software - Page 2

The CrowdChem Data Platform is a knowledge-based platform for the chemistry field, built on independently collected data. It enables users to smoothly select raw materials and search for customers through data analysis functions and text mining. Examples include discovering combinations of new raw materials, conducting more accurate usage investigations of chemical products, and creating lists of potential customers who can use each company's products. Information can be searched from highly comprehensive data collected from patents, papers, catalogs, news articles, etc., eliminating the need to dig around for necessary data. Users can select raw materials and customers on the platform by combining machine learning and natural language processing technology, allowing for raw material selection, customer search, competitive analysis, and more.

Atinary's Self-Driving Labs (SDLabs) platform is an AI and machine learning solution designed to digitize and automate R&D workflows, enabling traditional laboratories to transition from manual experiments to autonomous experimentation. It facilitates the design and optimization of experiments through a closed-loop system that integrates AI-driven hypotheses, predictions, and decisions. Key features include multi-objective optimization, database management, workflow orchestration, and real-time data analytics. Users can define experiments with constraints, allow the ML algorithms to decide on subsequent iterations, run experiments (with or without robotic assistance), analyze data, and retrain models with new data, thereby accelerating the discovery of better, cheaper, and greener products. Atinary's proprietary algorithms, such as Emmental for non-linear constrained optimization, SeMOpt for transfer learning in Bayesian optimization, and Falcon.

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.

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.