Digital materials chemistry is a multidisciplinary field that combines traditional materials science methods with modern digital tools to enable more efficient, sustainable, and targeted material development. While traditional techniques often rely on resource-intensive cycles of synthesis and testing, digital approaches allow for less resource-intensive prediction and optimization of material properties. Central to this approach are quantum chemical calculations, which analyze the electronic structures and properties of materials at the atomic level, and molecular dynamics simulations, which model how atoms and molecules behave over time. Combined with experimental materials data, the data from these simulations feed into machine learning algorithms that identify patterns and enable faster predictions of material behavior and synthesis pathways. High-throughput screening speeds up the materials design process by simultaneously testing thousands of material combinations, quickly identifying promising candidates. An important advantage of digital methods is their ability to incorporate sustainability and safety considerations from the start—such as emphasizing environmentally friendly materials or including life cycle assessments in the design process. These methods are increasingly supported by open, reproducible software tools, which help accelerate innovation across the materials science community.
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