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What is L7|ESP?

The L7 Enterprise Science Platform (L7|ESP®) offers a holistic solution aimed at contextualizing data and eliminating business silos through effective process orchestration. This integrated platform facilitates the digital transformation of data and scientific workflows in life sciences organizations. It comprises essential applications such as L7 LIMS, L7 Notebooks, L7 MES, and L7 Scheduling. With the ability to integrate effortlessly with third-party applications, lab instruments, and various devices, L7|ESP consolidates all data into a single cohesive model. Its low-code/no-code workflow designer, along with a variety of pre-built connectors, allows for swift deployment and comprehensive automation. By leveraging a unified data model, L7|ESP advances bioinformatics, artificial intelligence, and machine learning, thereby delivering valuable scientific and operational insights. This robust platform is specifically designed to meet the data and laboratory management challenges faced by the life sciences industry, focusing on areas such as: ● Research and Diagnostics ● Pharma and CDMO ● Clinical Sample Management For further resources, including on-demand recordings, case studies, and datasheets, visit the L7 Resource Center at l7informatics dot com/resource-center, where you can find a wealth of information to help you maximize the benefits of the platform.

What is Hydrogen-1.0?

Hydrogen-1.0 is an innovative scientific language model featuring open weights, developed by Deep Principle through the post-training of GLM-5.3, utilizing a wide variety of data sources such as chemistry, materials science, life sciences, laboratory notes, and research methodologies. The primary goal of this model is to aid researchers in addressing scientific questions, clarifying derivations, analyzing reaction pathways, interpreting spectra, designing experiments, and formulating hypotheses regarding materials. Its capabilities encompass scientific computing workflows, exploration of reaction networks, NMR structure analysis, experiment planning, and material discovery. Users can easily obtain the model by downloading its weights from platforms like Hugging Face and ModelScope, accessing it via API through NeuroSpark, or utilizing Deep Principle's Mira AI Scientist. Additionally, Hydrogen-1.0 can be run locally with proper hardware and software setups. It is crucial for researchers to critically evaluate the model's outputs in context with relevant evidence and scientific constraints, promoting responsible usage in their research endeavors. Consequently, Hydrogen-1.0 signifies a major leap forward in enhancing the efficiency and accessibility of scientific research, ultimately fostering innovation and collaboration in various fields. This advancement opens new avenues for interdisciplinary studies, bridging gaps between different scientific domains.

Media

Media

No images available

Integrations Supported

Integrations Supported

Hugging Face
ModelScope

API Availability

API Availability

Has API

Pricing Information

Please contact us at info at L7informatics dot com
or fill out a contact form on our website at L7 Informatics dot com

Pricing Information

Pricing not provided
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

L7 Informatics

Date Founded

2012

Company Location

United States

Company Website

l7informatics.com/esp/

Company Facts

Organization Name

Deep Principle

Date Founded

2024

Company Location

China

Company Website

blog.deepprinciple.com/introducing-hydrogen-1.0/

Categories and Features

AI Science

Not specified

Bioinformatics

Not specified

ELN

Not specified

Lab Automation

Not specified

Life Sciences

Not specified

LIMS

Not specified

Categories and Features

AI Models

Not specified

AI Science

Not specified

Open-Weight AI Models

Not specified

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