The elimination of global CO2 emissions will require significant advances in the discovery and design of advanced materials that serve as the basis behind many decarbonisation technologies. These materials will require optimised efficiencies and exceptional lifetimes combined with a low environmental impact and economic cost for their production. Success in this regard will improve the practicality of the global implementation of many important technologies such as those related to clean energy production, carbon capture and utilisation, and energy storage. For example, in 2021, researchers designed a solar cell having a record efficiency of 29.5% in the lab by adding an inexpensive, thin film of synthetic perovskite to a standard silicon cell. Moreover, scientists are working feverishly around the globe to provide new materials that can passively extract CO2 from exhaust gas at power plants as well as ambient air, and others are actively designing new catalysts that might enable the efficient reuse of CO2 after its collection for the production of synthetic fuels.
Despite all the effort, the rate at which advanced materials are being discovered, assessed, and actively implemented into decarbonisation technologies remains too slow. Thus, there is still plenty to do. For instance, creating aggressive carbon capture technology for use at point sources such as power plants remains a challenge. As things stand, applying the most mature capture technology to a coal-fired power plant would slash the net output of the facility by an estimated 30 per cent.16 Moreover, all too often, when a new material is successfully made in the lab, there are few guarantees, aside from chemical intuition and empirical trends, that it can be subsequently used for the application that motivated its development. Thus, research aimed at the discovery of new advanced materials that can reduce the aforementioned cost of the carbon capture process is now making use of cutting-edge computer methods to screen, in silico, hundreds of thousands of materials that might make the separation process more efficient, before physically testing the best candidates.17 This type of approach to the design of advanced materials via computational tools enables scientists to screen hypothetical and existing materials, identify those with the highest potential for a given application, propose possible synthetic pathway to expedite the discovery process, and assess the CO2 footprint and environmental impact of its implementation in a given decarbonisation technology on large scales. New tools related to data mining, machine learning, and artificial intelligence will hopefully push past the era of largely serendipitous discoveries of advanced materials to those that are engineered to solve a specific problem, and at some point, such work will be done in automated laboratories that require limited to no human intervention.