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22.08.2024
In our "Three questions for..." series, we interview selected personalities on various topics. Among other things, the questions address topics that are discussed by the interviewees in the form of keynote speeches or other activities at the "Circular Materials - Digitalization and Sustainability" congress organized by Bayern Innovativ.
Today we are interviewing Daniel Trost, Business Development Lead / Territory Manager DACH at Total Materia AG.
Mr. Trost, how can digital technologies drive sustainable materials and resource-efficient processes?
Digital technologies enable more efficient use of materials by optimizing design, production and supply chain processes. Tools such as digital twins and simulations allow engineers to virtually model and test materials, reducing waste in the creation of physical prototypes. In addition, digital platforms facilitate the tracking and management of materials throughout their lifecycle, from sourcing to recycling, promoting sustainable and circular practices. Importantly, these technologies also enable the selection of materials with a lower carbon footprint by incorporating environmental impact assessments directly into the material selection process. This ensures that sustainability is considered alongside performance and cost, helping companies make more responsible decisions that align with environmental goals.
What role does artificial intelligence play in material development and what benefits does it bring to sustainable solutions?
Artificial intelligence plays a crucial role in material development by first predicting material properties based on extensive data sets, ensuring that these materials meet all the necessary technical requirements. Once the material properties are validated, it is also possible to assess sustainability indicators such as carbon footprint to ensure that the selected materials not only perform well but also align with environmental goals. This approach allows for a more efficient and targeted selection process, where materials are chosen based on both their technical suitability and their environmental impact, ultimately supporting the development of sustainable solutions.
How has AI accelerated the development of new materials compared to traditional methods, what is your assessment of this?
AI has dramatically accelerated the development of new materials by changing the traditional research and development process. Traditional methods rely on time-consuming experiments and iterative testing that often take years to develop new materials. In contrast, artificial intelligence can quickly analyze and model potential materials, including their carbon footprint, identifying viable candidates much faster. Machine learning algorithms can predict how new materials will behave under different conditions, allowing developers to focus on the most promising and sustainable options from the outset. This not only shortens the time to market for new materials, but also ensures that sustainability, including a lower carbon footprint, is an important consideration throughout the development process, leading to greener innovations.