Assessed

NiCAT

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'Team points' are awarded according to the adequacy of the solution with the type of innovations sought by Team for the Planet. They correspond to the analysis of several factors :

  • impact potential: impact average score > 4 => 0,5 point / if > 4,15 => 1 point.
  • global consistency: all average score of the 6 selection criteria > 2,5 => 1 point.
  • the favourite: % of assessments judge the innovation as a top one to act on a global scale against greenhouse gases > 20% => 1 point
  • the targeting: validation of Team for the Planet scope of action higher than 90% => 0,5 point + innovation level of maturity is enough => 0,5 point
  • social acceptability: semantic analysis score of comments > 0 => 0,5 point/ if > 3500 => 1 point
26 assessments

Submitted for assessment : After checking that an innovation is within the scope of the 20 issues Team for the Planet targets and that it has reached a sufficient maturity, the innovation is being assessed.

We use A.I. to increase the material science development speed of EV Cars.

Sector
Other
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Submission date August 01, 2021 Founders Engin Karabudak Development location Türkiye

Detailed project

NB: this form is filled entirely by the ones submitting the innovation.

What is the issue addressed?

EV adoption in transportation speed is not enough, we use A.I. development material discovery novelty to increase the speed of EV adoption. So EV adoption speed will increase and there will be no carbon based fuel usage in transportation, because with our A.I. developed materials.

How is the problem solved?

Next generation battery materials as the key to the transition to battery electric vehicles We work on AI assisted research and development of next generation battery materials. With our expertise in application of machine learning in material science, we are developing low cobalt/high n

What is the customer target?

EV battery manufacturers are our clients. We will provide them cutting edge, A.I. developed battery materials for EV transition.

How is this solution different?

Rivals use classical chemistry research, but we are using A.I. assistant material science development. So we are digital reactor farm, A.I. developed materials, so we can speed up EV transition 10 fold.