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Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
Mit einer langfristigen Forschungsstrategie begleiten die für die Sicherheit von Mensch und Umwelt zuständigen Bundesoberbehörden (Umweltbundesamt, Bundesinstitut für Risikobe-wertung, Bundesanstalt für Arbeitsschutz und Arbeitsmedizin, Bundesanstalt für Materialfor-schung und -prüfung und Physikalisch-Technische Bundesanstalt) die rasch voranschreiten-de Entwicklung neuer Materialien unter den Gesichtspunkten des Arbeits-, Verbraucher- und Umweltschutzes. Die Strategie steht daher in enger Verbindung zu den öffentlichen Förder-programmen für Nanomaterialien und andere innovative Werkstoffe, z. B. des BMBF („Vom Material zur Innovation“) und der EU („Horizon 2020“).
Die Forschungsstrategie baut auf den bisherigen Ergebnissen der 2008 begonnenen und 2013 erstmals bilanzierten gemeinsamen Forschungsstrategie der Bundesoberbehörden „Nanotechnologie - Gesundheits- und Umweltrisiken von Nanomaterialien"1 auf und erweitert den Blickwinkel auch auf andere Materialinnovationen, bei denen vergleichbare Risiken für Mensch und Umwelt bestehen oder abgeklärt werden müssen. Darüber hinaus greift sie die Idee „anwendungssichere chemische Produkte“2 aus der Initiative „Neue Qualität der Arbeit“ (INQA) des Bundesministeriums für Arbeit und Soziales (BMAS) und das Konzept der nach-haltigen Chemie3 auf, das vom Bundesministerium für Umwelt, Naturschutz, Bau und Reak-torsicherheit (BMUB) unterstützt wird. Durch eine anwendungssichere und umweltverträgli-che Gestaltung innovativer Materialien und ihrer Folgeprodukte sollen nicht akzeptable Risi-ken für Mensch und Umwelt von Anfang an weitgehend ausgeschlossen werden. Dies kann erreicht werden durch
1.
die Verwendung sicherer Materialien ohne Gefahreneigenschaften für Mensch und Umwelt (direkte Anwendungssicherheit) oder
2.
eine Produktgestaltung, die über den gesamten Lebenszyklus emissionsarm und umweltverträglich ist (integrierte Anwendungssicherheit) oder
3.
eine Unterstützung des Anwenders (product stewardship) durch den Hersteller bei technischen, organisatorischen und persönlichen Schutzmaßnahmen zur sicheren Verwendung und Entsorgung des Produktes (unterstützte Anwendungssicherheit).
Die Fortschreibung der Forschungsstrategie soll als Bestandteil des Nanoaktionsplans 2020 der Bundesregierung Beiträge der Ressortforschung zu folgenden Schwerpunkten leisten:
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Charakterisierung und Bewertung der Risiken von Materialinnovationen
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Unterstützung von Forschungseinrichtungen und Unternehmen
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Fortschreiben von Rechtsvorschriften und Praxisempfehlungen
1
http://www.baua.de/nn_47716/de/Themen-von-A-Z/Gefahrstoffe/Nanotechnologie/pdf/Forschungsstrategie.pdf
2 http://www.baua.de/de/Themen-von-A-Z/Gefahrstoffe/Nachhaltige-Chemie/Nachhaltige-Chemie.html
3
http://www.umweltbundesamt.de/themen/chemikalien/chemikalien-management/nachhaltige-chemie
2
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Gesellschaftliche Akzeptanz
Die Forschungsstrategie soll mit Projekten und anderen forschungsnahen Aktivitäten umge-setzt werden. Dies umfasst die eigene Forschung der Häuser, die extramurale Ausschrei-bung und Vergabe von Forschungsdienstleistungen sowie die Beteiligung an vorwiegend öffentlich geförderten Drittmittelprojekten. Hinzu kommen Aktivitäten im Rahmen der Politik-beratung und der hoheitlichen Aufgaben. Mit inter- und transdisziplinären Ansätzen soll die Risiko- und Sicherheitsforschung enger mit der Innovationsforschung und Materialentwick-lung verknüpft werden. Die Forschungsstrategie ist aufgrund der raschen Entwicklungen auf diesem Gebiet für den Zeitraum bis 2020 angelegt. Die Forschungsziele adressieren die in diesem Zeitraum voraussichtlich umsetzbaren Forschungsansätze. Die Forschungsstrategie wird durch einen Arbeitskreis begleitet und spätestens mit Ablauf des Nanoaktionsplans 2020 evaluiert und angepasst.
Alkali-activated binders (AAB) can provide a clean alternative to conventional cement in terms of CO2 emissions. However, as yet there are no sufficiently accurate material models to effectively predict the AAB properties, thus making optimal mix design highly costly and reducing the attractiveness of such binders. This work adopts sequential learning (SL) in high-dimensional material spaces (consisting of composition and processing data) to find AABs that exhibit desired properties. The SL approach combines machine learning models and feedback from real experiments. For this purpose, 131 data points were collected from different publications. The data sources are described in detail, and the differences between the binders are discussed. The sought-after target property is the compressive strength of the binders after 28 days. The success is benchmarked in terms of the number of experiments required to find materials with the desired strength. The influence of some constraints was systematically analyzed, e.g., the possibility to parallelize the experiments, the influence of the chosen algorithm and the size of the training data set. The results show the advantage of SL, i.e., the amount of data required can potentially be reduced by at least one order of magnitude compared to traditional machine learning models, while at the same time exploiting highly complex information. This brings applications in laboratory practice within reach.
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.
3D structural investigations are described by X-ray laminography studies of sandwich shell segments, made of a PVC foam core, covered by non-crimp fabric glass fibre composite lay-ups processed by vacuum assisted resin infusion of epoxy. The specific scope of this study is to image transversal flaws within the foam core (joints) and of single ply overlaps. Test flaws were purposely implemented in order to simulate typical failure under cyclic load. In a dedicated test rig for shell structures, the flaw evolution/propagation is monitored by thermography and optical 3D inspection of deformation. Due to the unfavourable preconditions for classical computed tomography as of large aspect ratio, the samples were investigated by coplanar translational laminography. Its limited range of observation angles of ± 45°, results in anisotropic artefacts about the normal to the sample surface, but the typical flaws are well visualized in the as-prepared state, in a state of early damage, and in the repaired state.
The properties of nanomaterials are influenced not only by their chemical composition but also by physical properties (such as size, geometry and crystal structure). For the reliable determination and assessment of behaviour and effects of nanomaterials as well as for the determination of the exposure of humans and environment a comprehensive physical-chemical characterization of nanomaterials is essential. This is an important prerequisite to identify them as nanomaterials and to interpret and compare test results and - in future – to forecast interaction and effects of nanomaterials.
In 2006, the OECD launched a sponsorship program for the testing of nanomaterials in which 11 nanomaterials were thoroughly investigated using a variety of methods. The aim of the project was, among other things, to find out where problems occur and where there are gaps in the measurement and test procedures and where are changes required. An important outcome of the sponsorship program was the finding that the OECD Test Guidelines should in several cases be extended to the specific needs in testing of nanomaterials. The existing standardized test methods of the OECD for physical-chemical characterization have not been developed for nanomaterials in particular. A high demand for an extension of the test guidelines was identified. Germany complied with the OECD's request in 2017 and has agreed to extend the “Test Guideline on Particle Size Distribution / Fiber Length and Diameter Distributions Test Guideline” for Manufactured Nanomaterials (MN). UBA commissioned BAM and BAuA with the preparation of the Test Guideline. The aim of the project is the development of a harmonized test protocol for a valid and reproducible determination of particle size and size distribution which is one of the most relevant physical-chemical properties for MNs.
Different measuring methods provide different results for the size distribution of the particles. This is caused by the different measuring principles of the methods. Each method measures a specific parameter that ultimately determines particle size. First, the measured quantity differs for each method (Scattered light intensity, 2D image / projection, electric mobility, etc.). Second, the calculated diameters of the MN may differ (Feret Diameter, Area Projection, Mobility Diameter, Aerodynamic Diameter, Hydrodynamic Diameter). Third, a measuring method provides a size distribution which is measured either mass-based, surface-based or number-based. A conversion between the results requires additional parameters and thus possibly increases the measurement error.
In addition to the technical differences, the individual parameters are strongly influenced by the structure and material of the nanoparticles. For example, a surface functionalization can lead to very different results in the size distribution. The suitability of measurement methods differs with the material of the MN. As a result, two very different results can be measured for the particle size distribution using two different methods, which are nevertheless both correct. Several large projects in recent years therefore concluded that nanomaterials should be characterized by at least two complementary method. Imaging techniques are regarded as one of these methods for the characterization, the complementary methods are supposed to be statistical methods.
The different results for the size distribution of nanomaterials become problematic for the registration of new MN. A comparable and reproducible size distribution is a prerequisite for a standardized registration. In the future, the particle size distribution in the EU will also decide on the classification of a substance as a nanomaterial or as a non-nanomaterial. Especially in borderline cases, a standardized and comparable measurement methodology is therefore essential.
The new OECD test guideline will address the following four main steps in the determination of the length and width distributions of fibers: sample preparation, image acquisition, data evaluation and uncertainty analysis. As the sample preparation has to be optimized for each material, general quality criteria will be given in the protocol. For full visibility of a fiber the appropriate resolution has to be chosen. In the data evaluation the length and diameter of each fiber will be determined concurrently to allow for application of different regulatory definitions. The quality of the results critically depends on the sample preparation as well as the data evaluation. In this step the classification rules have to be formulated and followed accurately in order to optimize reproducibility of the method. The SOP will be validated in an international round robin test, which is planned for 2018/2019.