DEMOCRATIZING MATERIAL DISCOVERY

we enable you to

 

Substantial AI is aimed at accelerating material discovery by using data-driven tools to develop new, sustainable and innovative materials faster.

Predict Material Properties

Use data-driven techniques to predict properties of a material exhibiting non-linear behaviour as a function of composition

Discover Novel Compositions

Reduce the number of experiments needed to discover new compositions based on target properties and compositional constraints

Select Raw Materials

Make informed decisions for selecting raw materials based on availability without affecting the end product

Optimize Processes

Optimise a process for minimal cost, best quality, performance and energy consumption

Retrieve Knowledge

Retrieve specific data from publications and datasets using natural language processing

THE AGE OF ML

Since the dawn of mankind, materials science has shaped the development of civilizations. Materials transform society and culture.

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OBJECTIVE

Substantial AI is aimed at accelerating material discovery by using data-driven tools to develop new, sustainable and innovative materials faster. We use experimental data and deep learning based computational modelling to develop algorithms for material discovery.

TEAM

We have a unique group of scientists with expertise both in the field of materials science and computer science. Our team is capable of profoundly understanding the situation and building upon our existing ideals and algorithms to come up with a tailored solution.

OUR CLIENTS

ABSTC, India

UFSCar, Brazil

SERVICES WE PROVIDE

Technical Advice

Our technical advice services help steer your projects to completion smoothly. We help you to get the most out of your existing resources and guide you through the entire transformative journey.

Software as a Service

We offer customised software solutions based on the problem you’re trying to solve. We learn the clients’ present structure, understand their need and provide solutions that assimilate seamlessly with current business environment.

Machine Learning based Solutions

We tailor the best-in-class solutions based on machine learning and other data-driven techniques to meet the needs of various industries. These solutions can help you enhance your current algorithms or build new ones.

PYTHON FOR GLASS GENOMICS

Python for Glass Genomics (PyGGi) is a package for accelerating innovations in the field of glasses and ceramics. It uses state-of-the-art machine learning algorithms and computational techniques to assist users in designing novel glasses and understanding the complex composition-property relationships.

PACKAGES

PyGGi has various packages that allow discovery of novel materials. PyGGi Seer predicts various glass properties as a function of just the glass composition, using machine learning algorithms. PyGGi Zen is an optimization package that allows the discovery of novel glasses with targeted properties and compositions, at the tap of a button. PyGGi Bank is a large database of glass properties, which allows users to contribute their data as well.