About Atomic Data Sciences

AtomCloud provides an AI-native platform that automates the extraction and analysis of data from various characterization techniques, enabling the generation of high-fidelity datasets for advanced materials. This technology reduces the lead time for materials development by facilitating real-time insights and improving decision-making in R&D and manufacturing processes.

```xml <problem> Advanced materials development is hindered by the complexity of analyzing data from various characterization techniques. Manual data extraction and analysis are time-consuming, prone to errors, and limit the ability to generate high-fidelity datasets needed for materials innovation. This slows down R&D cycles and delays the commercialization of new materials. </problem> <solution> AtomCloud offers an AI-native platform that automates the extraction and analysis of data from diverse materials characterization instruments. The platform unifies data streams, enabling the creation of comprehensive digital fingerprints of materials, processing environments, and experimental targets. By applying AI-powered workflows, AtomCloud facilitates pattern discovery across multiple data sources, uncovering valuable insights and accelerating materials engineering. The platform provides interactive visualizations and charts, empowering users to understand their data better, iterate faster, and make data-driven decisions in R&D and manufacturing. </solution> <features> - Automated data extraction from various characterization techniques, including RHEED, XPS, and instrument logs. - AI-powered workflows for scalable data analysis and pattern discovery. - Unified data model for integrating experimental information from diverse sources. - Interactive visualizations and charts for enhanced data understanding. - Automated RHEED pattern analysis, including detection of kinetic transitions and analysis of rotating growths. - One-click extraction of atomic concentrations from XPS spectra without manual peak fitting. - Integration of instrument logs for contextualizing characterization data. - Custom data source integration for incorporating any type of tabular information. </features> <target_audience> AtomCloud's primary users are scientists and engineers in materials science R&D and manufacturing who need to accelerate materials development and optimize synthesis processes. </target_audience> ```

What does Atomic Data Sciences do?

Where is Atomic Data Sciences located?

Atomic Data Sciences is based in Berkeley, United States.

When was Atomic Data Sciences founded?

Atomic Data Sciences was founded in 2022.

Location

Berkeley, United States

Founded

2022

Employees

6 employees

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Atomic Data Sciences

Score: 41/100

AI-Generated Company Overview (experimental) – could contain errors

Executive Summary

atomicdatasciences.com 300+

Founded 2022 – Berkeley, United States

Funding

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Team (5+)

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Company Description

Problem

Advanced materials development is hindered by the complexity of analyzing data from various characterization techniques. Manual data extraction and analysis are time-consuming, prone to errors, and limit the ability to generate high-fidelity datasets needed for materials innovation. This slows down R&D cycles and delays the commercialization of new materials.

Solution

AtomCloud offers an AI-native platform that automates the extraction and analysis of data from diverse materials characterization instruments. The platform unifies data streams, enabling the creation of comprehensive digital fingerprints of materials, processing environments, and experimental targets. By applying AI-powered workflows, AtomCloud facilitates pattern discovery across multiple data sources, uncovering valuable insights and accelerating materials engineering. The platform provides interactive visualizations and charts, empowering users to understand their data better, iterate faster, and make data-driven decisions in R&D and manufacturing.

Features

Automated data extraction from various characterization techniques, including RHEED, XPS, and instrument logs.

AI-powered workflows for scalable data analysis and pattern discovery.

Unified data model for integrating experimental information from diverse sources.

Interactive visualizations and charts for enhanced data understanding.

Automated RHEED pattern analysis, including detection of kinetic transitions and analysis of rotating growths.

One-click extraction of atomic concentrations from XPS spectra without manual peak fitting.

Integration of instrument logs for contextualizing characterization data.

Custom data source integration for incorporating any type of tabular information.

Target Audience

AtomCloud's primary users are scientists and engineers in materials science R&D and manufacturing who need to accelerate materials development and optimize synthesis processes.