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What is Electric Twin?

Electric Twin stands out as a groundbreaking platform that harnesses artificial intelligence to create synthetic audiences by building virtual demographics grounded in real data, which allows teams to rapidly predict the thoughts, behaviors, and responses of targeted consumers toward products, messages, campaigns, and strategic questions without relying on traditional methods like surveys or focus groups. By combining sophisticated language models, machine learning methodologies, and insights derived from social sciences, it fabricates complex synthetic personas that genuinely mirror actual demographic profiles, thus enabling quick inquiries that provide insights with a statistical precision comparable to conventional research techniques, often delivering results in just seconds instead of the weeks that are usually needed. This remarkable capability empowers organizations to assess marketing materials, product ideas, campaigns, and market assumptions, allowing for swift iterations across various segments and facilitating the exploration of responses from different demographic groups, thereby speeding up the acquisition of insights that would otherwise require costly and lengthy field studies. As a result, Electric Twin not only improves the efficiency of decision-making processes but also significantly cuts research expenses, establishing itself as an essential resource for businesses striving to maintain a competitive edge. Moreover, by streamlining the research process, it opens up new avenues for innovation and adaptability in an ever-evolving market landscape.

What is BIOVIA TURBOMOLE?

BIOVIA TURBOMOLE is a cutting-edge software suite designed for quantum chemistry, focusing on ab initio electronic structure calculations applicable to diverse systems like molecules, clusters, periodic structures, and solutions. Initially created at the University of Karlsruhe and Forschungszentrum Karlsruhe GmbH, it is now overseen by TURBOMOLE GmbH, which has broadened its functionality to encompass a variety of computational techniques, including density functional theory (DFT), Møller–Plesset perturbation theory, coupled-cluster methods, and GW-Bethe–Salpeter approaches. The software is particularly notable for its accuracy in predicting outcomes related to chemical reactions, spectroscopy, and the simulation of optical devices. Among its standout features are the optimization of reaction pathways, identification of transition states, and the modeling of solvation effects using COSMO-RS. Furthermore, its spectroscopic capabilities span a multitude of methods such as IR, Raman, VCD, UV-Vis, and vibronic spectra, while its optical property simulations are bolstered by advanced techniques for spin-orbit coupling and relativistic all-electron calculations. As a result, BIOVIA TURBOMOLE proves to be an invaluable tool for researchers delving into intricate chemical systems and their underlying properties, paving the way for innovative discoveries in the field of quantum chemistry. With its comprehensive range of features, it continues to be a cornerstone for academics and industry professionals alike.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

Windows
Mac
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Electric Twin

Date Founded

2023

Company Location

United Kingdom

Company Website

www.electrictwin.com

Company Facts

Organization Name

Dassault Systèmes

Date Founded

1981

Company Location

France

Company Website

www.3ds.com/products/biovia/turbomole

Categories and Features

Synthetic User Tools

Not specified

User Research

Not specified

Categories and Features

Chemical

Not specified

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