Jill Becker is the CEO and Cofounder, Kebotix. The Kebotix self-driving lab combines data and AI with robotics to discover and create advanced chemicals and materials at a faster rate and at the push of a button. Kebotix offers inventive solutions that create new, disruptive chemistries and materials at a rapid pace while reducing costs. Their innovations enable discovery of new materials to help meet the world’s global challenges.
Kebotix is targeting the creation of 100 top notch molecules per week.
Kebotix has developed the world’s first and biggest AI brain for chemistry and materials based on research conducted by company founders while at Harvard. Kebotix’s self-driving lab for materials discovery — combining AI decision making with laboratory robotic platforms — dramatically condenses the research cycle from a decade to a period of months. The Kebotix system, the AI and robotics reiterative closed loop, rapidly and efficiently processes enormous amounts of complex molecular data to discover new materials or generate new formulations of particular products with desired target properties.
“The top five producers of materials have combined sales of over $210 billion with large-scale production and established supply chains, but are too slow in innovating,” said Becker. “With the world’s first and only AI-driven, fully integrated lab system for materials discovery, Kebotix aims to reinvent the $800 billion global materials market.”
Jill indicates that the material discovery that she did for her over 4 years and which was used as a basis for her first company could be performed in 1 year or less using the Kebotix system.
The AI system would help guide the molecular discovery process. The AI can scan newly created chemicals for the properties and functions. They are taking the chance out of the discovery.
They start with smart screening. The system will suggest more optimized molecules based upon a target molecule.
The AI predicts and plans and the experiments. It guides robotic systems to perform the synthesis.
They can create anti-weed molecules that are not just more potent but do not target other plants or have other pollution problems and do not harm humans.
They solve inverse design using generative machine learning.
SOURCE- Live by Brian Wang of Nextbigfuture.com at EmTech Digital 2019, Kebotix