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What is TWAICE?

Our cutting-edge battery analytics platform provides innovative solutions for monitoring, analyzing, and enhancing battery performance. By employing an array of machine learning techniques and specialized battery algorithms, we guarantee precise outcomes across various scenarios. The platform is equipped with an API that seamlessly integrates with third-party applications, such as fleet management systems, simplifying the user experience. Rather than depending on guesswork, laboratory experiments, or tedious manual data management, users can perform virtual tests and benefit from automated lab data handling. With the model library, users can efficiently evaluate battery cells in a virtual setting, further streamlining the development process. Additionally, our data management software solutions promote the effective organization of lab data, leading to improved decision-making. By harnessing field data analytics, you can refine your development strategies. The TWAICE battery model library provides access to a diverse range of high-fidelity electrical, thermal, and aging models of the latest lithium-ion battery cells, ensuring that you have the most precise and current resources for your projects. This all-encompassing approach not only simplifies the research and development workflow but also significantly boosts innovation in battery technology. In this way, our platform empowers users to stay ahead in the rapidly evolving energy landscape.

What is AQBioSim?

AQBioSim is a cutting-edge cloud platform developed by SandboxAQ that employs Large Quantitative Models (LQMs) grounded in the principles of physics and chemistry to revolutionize the processes of discovering and optimizing materials. By integrating methodologies like Density Functional Theory (DFT), Iterative Full Configuration Interaction (iFCI), Generative AI, Bayesian Optimization, and Chemical Foundation Models, AQBioSim enables exceptionally precise simulations of molecular and material behaviors in practical applications. Its diverse functionalities allow for the prediction of performance under various stressors, refinement of formulation processes through in silico testing, and exploration of environmentally friendly chemical strategies. A remarkable highlight of AQBioSim is its significant advancements in battery technology, achieving a staggering 95% reduction in the time required for lithium-ion battery end-of-life predictions, along with an impressive 35 times increase in accuracy while utilizing only 50 times less data. This exceptional platform not only accelerates the pace of material innovation but also plays a vital role in fostering advancements in sustainable energy solutions, paving the way for a greener future. Furthermore, the implications of these innovations extend into various industries, demonstrating AQBioSim's potential to influence a wide range of applications.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

TWAICE Technologies

Company Location

Germany

Company Website

twaice.com

Company Facts

Organization Name

SandboxAQ

Date Founded

2021

Company Location

United States

Company Website

www.sandboxaq.com/solutions/aqbiosim

Categories and Features

Predictive Analytics

AI / Machine Learning
Benchmarking
Data Blending
Data Mining
Demand Forecasting
For Education
For Healthcare
Modeling & Simulation
Sentiment Analysis

Categories and Features

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