The PrISMa platform’s modular structure allows us to consider various stakeholders’ perspectives. For any combination of source, sink, technology, utilities and region, we compute a list of 50 key performance indicators (KPIs; Supplementary Table 5). A Spearman analysis (Extended Data Fig. 1) helped us identify six reference KPIs that capture the most important trends (Extended Data Table 2).Let us first focus on one case study: capturing CO2 using a temperature vacuum swing adsorption (TVSA) process (with a vacuum pressure of 0.6 bar) from a cement plant located in the UK. The captured CO2 is compressed and sent for geological storage. In Fig. 2, we compare the performance of the materials with the monoethanolamine (MEA) benchmark22 (Supplementary Information Section 4); many materials outperform the benchmark for the different process, TEA and LCA KPIs.Fig. 2: Materials performance for a TVSA carbon-capture process at 0.6 bar added to a cement plant in the UK.a, The nCAC versus recovery, with colour coding the specific electrical energy consumption. b, The nCAC versus purity, with colour coding the specific thermal energy consumption. c, Specific thermal energy consumption for heating versus productivity, with colour coding the recovery. d, MR:MM versus climate change, with colour coding the nCAC. Sbe, antimony equivalent. Our visualization tool25 gives an interactive version of this graph. The dotted lines in a, c and d show the MEA benchmark (Supplementary Information Section 4). In b, the vertical orange dotted line gives the purity required for geological storage (>96%) and in a, the blue shaded area gives the uncertainty. Each dot represents the corresponding KPI of a material.The net carbon avoidance cost (nCAC) is the KPI that quantifies the cost of avoiding CO2 emissions into the atmosphere over the plant’s life cycle. The nCAC is not the only criterion, and evaluating materials across all KPIs and from all stakeholders’ perspectives is important. Figure 3 highlights the top-performing materials for a given KPI and their ranking on the other KPIs across the platform. The comparison of the material rankings in Fig. 3 illustrates the complexity of selecting an optimal material; the top ten for a given KPI do not necessarily perform well for the other KPIs.Fig. 3: Comparison of materials ranking for a TVSA carbon-capture process at 0.6 bar added to a cement plant in the UK.Rankings according to Henry selectivity (S), purity (Pu), productivity (P), nCAC, climate change (CC) and MR:MM for a TVSA carbon-capture process added to a cement plant in the UK. In these graphs, the top-performing material is ranked number one. Coloured lines represent the top ten performers for the six reference KPIs. The same colour is used to highlight the KPI of interest. Every line illustrates how the ranking of a specific material (y axis) changes across all other KPIs (x axis). Our visualization tool25 gives an interactive version of this graph.From an engineering perspective, we are interested in identifying the best technology. Figure 4a compares the nCAC of the 20 top-performing materials for the 3 process configurations and 3 CO2 sources. For all three technologies, we find materials that outperform the benchmark for coal and cement. For cases with a low CO2 concentration in the feed stream (for example, natural gas combined cycle (NGCC) power plants), the vacuum step in the process configuration reduces the cost, but no materials are identified with a lower nCAC than the MEA benchmark.Fig. 4: Comparison of process configurations and regions.This analysis of the stakeholders’ perspectives focuses on the 20 materials with the lowest nCAC. In these violin plots, the white circle gives the median, which we use as a (conservative) estimate of the performance. The bottom of the violin represents a few materials with an even better performance. The width indicates the number of structures with a particular y value and the thick black bar contains 50% of the structures. a, The nCAC jointly with the MEA benchmark (black dashed lines). b, The purity for three CO2 sources depending on the technologies (TSA and TVSA with two vacuum levels 0.2 bar and 0.6 bar) jointly with the required purity of the CO2 sink (red dashed line). c,d, The climate change (c) and effective recovery (d) for the five regions. e,f, The CCC (e) and the nCAC (f) for the five regions. CN-GD, China Guangdong region; CN-SD, China Shandong region; CH, Switzerland. See Supplementary Information Section 8 for the data.The vacuum step increases the purity of the product stream. This increase is achieved by rapidly purging the weakly adsorbed components from the column’s gas phase after the adsorption step but at the expense of a lower recovery than a temperature swing adsorption (TSA) process. Figure 4b shows that with the vacuum step, most materials exceed 96% purity, whereas for TSA, only a few materials meet this requirement for geological storage. Therefore, we focus on operating TVSA with 0.6 bar for the cement and coal and the TVSA with 0.2 bar for the NGCC.After optimization, many more materials meet the purity requirement (Supplementary Information Section 10.3.3). Optimization lowers the nCAC by about €7 tCO2−1 (about 12%) for a TVSA process (cement in the UK) and about €9 tCO2−1 (about 14%) for a TSA process and reduces the differences between the various process configurations. Importantly, we see that the ranking of the top-performing materials has not been impacted significantly.Running a carbon-capture plant inherently produces emissions of CO2 and other greenhouse gases owing to an increased demand for energy and materials. The environmental manager’s perspective focuses on maximizing the captured CO2 while simultaneously minimizing these associated CO2e emissions and other possible environmental impacts.The effective recovery (Fig. 4d) adjusts the process recovery for the CO2e emissions associated with building and operating the carbon-capture plant, using the climate change KPI (Fig. 4c). For some materials, we find that the climate change KPI is >1 kgCO2e per kgCO2 captured (Extended Data Fig. 2a). This indicates that the capture process with these materials emits more CO2e over the plant’s lifetime than the total amount of CO2 captured. Several factors can contribute to this result. For example, some materials have a very low CO2 working capacity, resulting in high material and energy demands. Some others, with relatively good working capacities and moderate heat demands, contain metals such as gold or rhodium. The climate change impact of synthesizing such materials is so significant that it leads to a climate change KPI >1 kgCO2e per kgCO2 captured. An important environmental KPI is the material resources:metals/minerals (MR:MM), which relates to the use of minerals and metals resources. In Extended Data Fig. 3, we compare the ranking of materials based on their constituent metals, focusing on some abundant metals (magnesium, zinc and manganese) and rare metals (copper, lutetium and silver). The MR:MM ranking will be poorer if a greater amount of the corresponding MOF is required to remove a unit of CO2 or if the total energy demand is higher. The abundant metals rank better, whereas the rank drops for the rather rare metals. If a MOF scores poorly on MR:MM, it may inspire chemists to explore similar structures with more abundant metals.Another important factor in MOF synthesis is solvent selection. The PrISMa platform identifies the greenest solvent from a list of frequently used ones. Supplementary Information Section 8.2.3 pinpoints anticipated environmental hotspots related to solvent selection.The platform provides additional KPIs related to the process’s environmental impacts (Extended Data Fig. 2b), for example, impact on ecosystem quality, human health and the use of resources (land, water, materials and non-renewable energy), and allows us to flag materials that impact the environment.The CO2-producer perspective seeks the most cost-effective capture technology. For instance, a cement producer can select different utilities based on their impact on the plant’s environmental footprint and cost. In Switzerland, CO2e emissions can be reduced using electric boilers instead of natural-gas-fired ones. This change significantly lowers the climate change KPI owing to the low carbon intensity of Switzerland’s electricity grid, resulting in nearly 100% effective recovery. However, this improvement comes with a cost increase of approximately €16 tCO2−1 (about 20%) owing to the high operating costs in Switzerland (Supplementary Information Section 8.2.2).If one needs to perform large-scale carbon capture tomorrow, the default choice is often the well-established MEA technology. However, from an investor’s perspective, our platform shows that solid-sorbent-based capture processes can outperform the MEA benchmark. The cost reductions increase with CO2 concentration; for cement, the nCAC is about a factor of two lower than the benchmark (Fig. 4a). Investors are also interested in understanding the economics of deploying carbon-capture plants in different parts of the globe. The large cost differences and electricity grid characteristics will make specific regions economically more beneficial than others. Figure 4e highlights this region’s dependence on the carbon-capture cost (CCC). For the cement case, electricity and natural-gas costs are low in the USA, which makes it favourable in CCC, whereas it is highest in Switzerland. The region dependency of coal costs is rather small, whereas for natural gas, it is more substantial.However, the CCC does not account for the CO2e emissions associated with operating the carbon-capture plant and the product loss (for example, electricity). The nCAC corrects the system-based CCC by the climate change KPI (Fig. 4f). The largest impact is observed in the NGCC case. The high CO2e emissions of the electricity grid owing to the many coal power plants in China, particularly in Shandong province, lead to the highest nCAC. In contrast, Switzerland has the lowest because its grid is dominated by hydroelectricity. The low energy cost and CO2e emissions of the electricity grid mix make the USA beneficial for coal and cement.The route from the first synthesis of a new material to its implementation into a commercial process can take many years. It is, therefore, important, from a chemist’s perspective, to provide some guidance on how molecular characteristics impact the material’s performance at the very early material’s design stage. An interesting practical question is whether one can synthesize materials that work well for any CO2 source. Extended Data Fig. 4a compares the nCAC ranking for NGCC, coal-fired power plants and cement plants. We observe a significant change in ranking when we go from the NGCC to coal. The changes are smaller but still considerable when we move from coal to cement. This indicates the need for tailored materials for different capture applications (see Supplementary Information Section 8.5.1 for more details).Extended Data Fig. 4b shows the increase in nCAC with wet versus dry flue gases. As the value of α, indicating water penetration in the bed, increases, costs rise substantially, following exponential trends after a certain threshold. For cement, the increase in nCAC is at least €5.0 tCO2−1 (8%), and €26.7 tCO2−1 (22%) for NGCC. This underscores the necessity of managing moisture at lower feed-CO2 partial pressures to maintain cost competitiveness. In Supplementary Information Section 9, we discuss the limits of our (ideal) model. Under non-ideal mass-transfer conditions, about 60–70% of the materials remain top performers. However, for materials with high water affinity (for example, zeolite 13X), moisture slippage into the dry part of the bed can significantly undermine their capacity and shift their ranking.Screening more than a thousand materials enables us to use data-driven methods to identify the molecular characteristics of the top-performing materials. For cement, we demonstrate that by retaining the descriptor related to pore geometry (that is, persistence images), we can accurately predict whether a material has a lower nCAC than the MEA benchmark (Supplementary Information Section 8.5.3). These persistence images also rank the importance of each atom in these predictions, with the collection of these atoms characterizing the molecular features that define the adsorbaphore23. A common feature among materials outperforming MEA is a geometrical rod of metal atoms (highlighted in Extended Data Fig. 5). These features are often associated with stacked delocalized systems (aromatic rings) separated by 6 Å to 11 Å (see also Supplementary Fig. 62).
