Machine Learning Fights Global Warming

Among all greenhouse gasses, carbon dioxide is the highest contributor to global warming. Without action by 2100, according to the Intergovernmental Panel on Climate Change, the average temperature of the world will increase by about 1.5 degrees Celsius. Finding effective ways to capture and store carbon dioxide has been a challenge for researchers and industries focused on combating global warming - Amir Barati Farimani has been working to change that. "Machine-learning models bear the promise for discovering new chemical compounds or materials to fight against global warming," explained Barati Farimani, an assistant professor of mechanical engineering at Carnegie Mellon University. "Machine-learning models can achieve accurate and efficient virtual screening of CO₂ storage candidates and may even generate preferable compounds that never existed before." Barati Farimani has made a breakthrough using machine learning to identify ionic liquid molecules. Ionic liquids (ILs) are families of molten salt that remain in a liquid state at room temperature, have high chemical stability and high CO₂ solubility, making them ideal candidates for CO₂ storage. The combination of ions largely determines the properties of ILs.