Solutions & Case Studies
Our AI-Discovery Engine is a globally proven platform. We partner with the world’s leading innovators in science and technology to transform the most complex challenges into breakthrough solutions. Explore our case studies to see how.
Case Study: Collaboration with Microsoft Research
The Challenge: Traditional materials discovery and optimization can take decades and often leads to products that can’t be manufactured at a suitable scale. AI-driven quantum chamical simulations with predictive capability significantly accelerate material discoveries and optimizations. However, Existing computational tools compromise between the speed of the simulations and their predictive power. This tradeoff directly hindered the development and optimization of new chemical technologies and processes.
The Solution: Our collaboration with Microsoft Research AI for Science is redefining what's possible. By constructing highly accurate and diverse chemical databases, we are creating next-generation deep-learning quantum chemical methods that deliver benchmark accuracy at an affordable cost, making high-throughput predictive simulations a reality.
The Impact: The newly developed Skala deep-learning DFT method achieves "chemical accuracy" at a fraction of the cost of traditional DFT methods. This breakthrough makes it possible to reliably predict experimental outcomes, de-risk R&D, and dramatically accelerate materials discovery.
Supporting Evidence:
Case Study: A Global Clean Water Solution
The Challenge: Extreme weather conditions such as droughts and floods introduce new challenges to clean water availability in Australia. At any given time, the atmosphere contains six times more water than all the available fresh water on the planet. However, condensing atmosphric humidity into water is an energy intensive process.
The Solution: To develop new materials capable of efficiently capturing atmospheric water. Using our predictive simulation capabilities, we identified a novel mechanism for enhancing water capture. We discovered that a synergistic hydrogen-bond network between functionalized graphene and cations could create a highly effective water-harvesting material.
The Impact: This discovery, published in the prestigious journal PNAS in collaboration with Nobel Laureate Sir Kostya Novoselov, provides a new, viable pathway for developing high-performance materials for atmospheric water generation.