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.
get to know us
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
SCHOTT AF, Germany
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.