The role of computational materials science in achieving sustainable development goals: A review

Computational materials science advances progress toward the United Nations’ Sustainable Development Goals by applying the rational design of sustainable materials and technologies. However, a systematic assessment of how computational materials science helps bridge the gap between theoretical predictions and real-world applications is still underexplored. This review aims to critically assess the role of computational materials science techniques such as density functional theory, molecular dynamics, and machine learning for relevant challenges in clean energy (SDG 7), water purification (SDG 6), climate-resilient infrastructure (SDG 9 and 13), and sustainable material cycles (SDG 12). This review presents evidence on how these approaches provide means to overcome the limitations of conventional experiments in terms of high throughput screening, reduced water, and manpower consumption, as well as increased pace of innovation. The review also discusses the model validation through experimental benchmarking and multi scale simulation for guaranteeing the reliability of the predictive design of materials. This leads to implications for realising advancements with CMS, such as tying for energy efficient catalysis and polymeric recyclable materials, indeed contributing toward sustainability goals. However, some issues persist, mainly revolving around data availability, computational costs, and small mismatches between simulations and experimental results. To enhance further, interdisciplinary collaborations, open-data initiatives, and experimental research alongside CMS should be promoted. These approaches will help the materials science consortium to convert computational knowledge into scalable solutions to foster development that is equitable and sustainable.