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Mechanochemistry is increasingly used for synthesizing soft matter materials including metal organic compounds and cocrystals.1 The ever-increasing interest in this method is contrasted by a limited mechanistic understanding of the mechanochemical reactivity and selectivity. Time-resolved in situ investigations of milling reactions provide direct insights in the underlying mechanisms.2-4 We recently introduced different setups enabling in situ investigation of mechanochemical reactions using synchrotron XRD combined with Raman spectroscopy and thermography. The presented setup allows the detection of crystalline, amorphous, eutectic, and liquid intermediates. Furthermore, the chemical composition of the reaction mixture was found to be directly correlated with changes in the temperature profile of the reaction. The resulting deeper kinetic and thermodynamic understanding of milling processes is the key for future optimization of mechanochemical syntheses. In this contribution, we will discuss our recent results investigating the formation of (polymorphic) cocrystals and coordination polymers.2,3,5 Our results indicate that in situ investigation of milling reactions offer a new approach to tune and optimize mechanochemical syntheses.
Mechanochemistry has emerged as one of the most interesting synthetic protocols to produce new materials. The development of mechanochemistry as a synthetic method is supported by excellent research by many groups worldwide in a wide range of applications. The potential of mechanochemistry is also reflected in the inclusion in IUPAC’s 10 chemical innovations that will change our world’.[1] Solvent-free methodologies lead to unique chemical processes during synthesis with the consequent formation of martials with new properties.2 In this contribution, we will discuss our recent results investigating the formation of (polymorphic) cocrystals, coordination polymers, metal oxides and metal nanoparticles.[3-8] We introduced different setups enabling in situ investigation of mechanochemical reactions using synchrotron XRD combined with Raman spectroscopy and thermography.
Kristalle sind in der Regel spröde und neigen dazu, bei mechanischer Belastung zu brechen. Dieses Verhalten schränkt ihre Anwendungsmöglichkeiten stark ein. Durch die Entdeckung von mechanischer Flexibilität in Kristallen wurde dieses Problem gelöst. Je nach Art der Verformung können Kristalle in plastisch (irreversibel) oder elastisch (reversibel) biegbar unterteilt werden. Die Verbindung 4-Brom-6-[(6-chlorpyridin-2-ylimino)methyl]phenol (BCMPMP) ist ein vielversprechender Kandidat für die Anwendung als flexibler Waveguide. BCMPMP hat zwei verschiedene Polymorphe mit unterschiedlichen optischen und mechanischen Eigenschaften. Die spröden Kristalle der Form I zeigen eine sehr schwache Emission bei 605 nm (λex = 425 nm; Photolumineszenz-Quantenausbeute Φ = 0,4 %). Im Gegensatz dazu zeigt die plastische Form II eine starke Emission bei 585 nm (λex = 425 nm; Φ = 8,7 %). Aufgrund der mechanischen Flexibilität wurde die Eignung der Form II als Waveguide untersucht.
High-throughput computations are nowadays an established way to suggest new candidate materials for applications to experimentalists. Due to new packages for automation and access to databases of computed materials properties, these studies became more and more complex over the last years. Besides suggesting new candidate materials for applications, they also offer a way to understanding the materials properties based on chemical bonds. For example, we have recently used orbital-based bonding analysis to understand the results of high-throughput studies for spintronic materials, ferroelectric materials and photovoltaic materials in detail. To do so, we have developed Python tools for high-throughput bonding analysis with the programs VASP and Lobster (see www.cohp.de). They are based on the Python packages pymatgen, atomate, and custodian. This implementation will be discussed within the talk. We also expect that these tools offer possibilities to arrive at new descriptors based on chemical bonds for materials properties.
The stability of materials is a very important property for materials discovery. In recent years, we have explored several approaches for advancing the prediction of the stability of crystals (e.g., classical chemical heuristics and machine-learned interatomic potentials). For example, we have assessed a famous chemical heuristic – the Pauling rules – regarding their usefulness for structure prediction. They are only of limited predictive power, i.e. the rules 2 to 5 only work for 13 % of all tested oxides. Furthermore, we have shown that machine-learned interatomic potentials can be used to predict phonon properties of a range of silicon allotropes accurately and, therefore, they can also be used to assess the dynamic stability of materials. To do so, we have developed new schemes to build reference databases for machine learned interatomic potentials.
The talk „Automation in computational materials science“ deals with the current state of automation in the field of computational materials science. It illustrates how automation can, for example, be used to speed up the search for new ferroelectric materials and spintronic materials. Furthermore, it lists current tools for automation and challenges in the field.
Developments in density functional theory (DFT) calculations, their automation and therefore easier access to materials data have enabled ab initio high-throughput searches for new materials for numerous applications. These studies open up exciting opportunities to find new materials in a much faster way than based on experimental work alone. However, performing density functional theory calculations for several thousand materials can still be very time consuming. The use of, for example, faster chemical heuristics and machine-learned interatomic potentials would allow to consider a much larger number of candidate materials. In addition to DFT based high-throughput searches, the seminar will discuss two possible ways to accelerate high-throughput searches.
Using data analysis on the structures and coordination environments of 5000 oxides, we were able to investigate a chemical heuristic – the famous Pauling rules – regarding its usefulness for the fast prediction of stable materials.
We have also investigated how machine-learned interatomic potentials can be used to accelerate the prediction of (dynamically) stable materials. The use of these potentials makes vibrational properties accessible in a much faster way than based on DFT. Our results based on a newly developed potential for silicon allotropes showed excellent agreement with DFT reference data (agreement of the frequencies within 0.1-0.2 THz).
In addition, we have successfully used high-throughput calculations in the search for new candidate materials for spintronic applications and ferroelectrics