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Understanding the chemistry and nature of individual chemical bonds is essential for materials design. Bonding analysis via the LOBSTER software package has provided valuable insights into the properties of materials for thermoelectric and catalysis applications. Thus, the data generated from
bonding analysis becomes an invaluable asset that could be utilized as features in large-scale data analysis and machine learning of material properties. However, no systematic studies exist that conducted high-throughput materials simulations to curate and validate bonding data obtained from LOBSTER. Here we present an approach to constructing such a large database consisting of quantum-chemical bonding information.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics for materials. More and more frequently, they are used as features in machine learning. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying these local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale and for machine-learning approaches. The latter relates to a general trend toward automation in density functional-based materials science. The lecture will demonstrate how our tools, that assess local atomic environments, helped to test and develop heuristics and design rules and an intuitive understanding of materials.
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database.
Here we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output files data stored as a dictionary.
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of json files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database.
Here we provide also the larger computational data json files for 700 compounds. This files consists of all important LOBSTER computation output files data stored as dictionary.
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders, part of the energy-intensive clinker production process can be dispensed. However, as numerous raw materials are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced binder formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of alkali-activated binder formulations can significantly accelerate research.
The "Sequential Learning App for Materials Discovery" (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings.
Our material database already includes more than 120,000 data points of alternative binders and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials.
Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.
High greenhouse gas emissions from the production of building materials are a major contributor to the current climate crisis. However, developing alternative building materials is complex. Traditional laboratory methods are reaching their limits. Artificial intelligence, on the other hand, can give research a new dynamic.
Novel materials are usually developed manually in the laboratory rather than on a computer. This makes the processes time-consuming, difficult and expensive. With the app SLAMD (Sequential Learning App for Materials Discovery), materials researchers can explore the potential of artificial intelligence to speed up materials research and easily apply AI in the lab. The app was developed by our team at the Federal Institute for Materials Research and Testing (BAM) led by Prof. Sabine Kruschwitz together with a team in the Department of Building Materials and Construction Chemistry at TU Berlin led by Prof. Dietmar Stephan.
It uses material composition and characterization data to predict ideal material candidates. It can be used to optimize many material properties simultaneously and even incorporates database information such as carbon footprint, material cost or resource availability. Unlike the usual data-intensive AI methods, SLAMD optimally integrates existing knowledge and human feedback, and provides numerous decision support tools to precisely navigate complex scientific knowledge processes towards success.
In this talk, we will present some case studies where we were able to find suitable advanced materials in a few months instead of several years. We will talk about the challenges we overcame and the future potential we see for this approach to developing the green materials of the future.
Many PhD students are interested in applying machine learning, AI, data science, etc., and there are many good reasons for this. However, there is a disconnect between mainstream data science and materials science, for example, when it comes to the sheer size of the data. This talk will highlight some of the unique challenges in materials informatics and present some interesting approaches to overcome them. Although the field is large, this talk will focus on cases that have some practical relevance to PhD students at BAM.