シュレーディンガー Official site

[Case Study] Machine Learning Enabling Accurate Prediction of Precursor Volatility

Predict the evaporation or sublimation temperature with an accuracy of ±9°C on average, calculating hundreds of complexes per second.

A New Path to Precursor Development: Schrödinger's Machine Learning This predictive model opens a new avenue for designing new precursors with improved performance, optimizing not only the deposition and chemistry but also the temperature at which they can evaporate or sublime to be supplied as vapor. This advancement allows for a much broader range of structural changes to be screened computationally than before, enabling the generation of candidate precursors for experimental synthesis and testing that are less risky and more innovative. With this volatility model and the computational workflow for reactivity and decomposition based on Schrödinger's quantum mechanics, a complete design kit for vapor phase deposition and etching is provided, accelerating research on materials and processes for new technologies. *For 50 common metal and metalloid complexes, the evaporation or sublimation temperature at a given vapor pressure is predicted with an accuracy of ±9°C (about 3% of absolute temperature). *It can compute hundreds of complexes per second, resulting in a fast turnaround time. *For more details, please refer to the PDF document or feel free to contact us.

Schrödinger Materials Science

basic information

【Published Items】 ■Challenges in Volatility Prediction ■Approaches Using Machine Learning ■Volatility Prediction Results of Inorganic and Organic Metal Complexes *For more details, please refer to the PDF document or feel free to contact us.

Price information

Please contact us.

Delivery Time

Applications/Examples of results

For more details, please refer to the PDF document or feel free to contact us.

Detailed information

Related Videos

[Case Study] Machine Learning Enabling Accurate Prediction of Precursor Volatility

TECHNICAL

Schrödinger's platform for materials science

PRODUCT

Product Comprehensive Guide

PRODUCT

[Data] Machine Learning and Material Property Prediction

OTHER

[Case Studies] Machine Learning for Materials Research

TECHNICAL

This is a Japanese brochure that clearly introduces Schrödinger's materials development support products.

PRODUCT

Japanese Brochure: Polymer and Resin Property Value Prediction Support Tool

PRODUCT

[Data - Simplified Version] AI Platform for Materials Informatics: LiveDesign Presentation Materials

PRODUCT

News about this product(2)

Recommended products

Distributors