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In recent years, many protocols in computational materials science have been automated and made available within software packages (primarily Python-based). This ranges from the automation of simple heuristics (oxidation states, coordination environments) to the automation of protocols, including multiple DFT and post-processing tools such as (an)harmonic phonon computations or bonding analysis. Such developments also shorten the time frames of projects after such developments have been made available and open new possibilities. For example, we can now easily make data-driven tests of well-known rules and heuristics or develop quantum chemistry-based materials descriptors for machine learning approaches. These tests and descriptors can have applications related to magnetic ground state predictions of materials relevant for spintronic applications or for predicting thermal properties relevant for thermal management in electronics. Combining high-throughput ab initio computations with fitting, fine-tuning machine learning models and predictions of such models within complex workflows is also possible and promises further acceleration in the field. In this talk, I will show our latest efforts to link automation with data-driven chemistry and materials science.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying 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. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Zirconium chloro fluoride as catalyst for C-F bond activation and HF transfer of fluoroalkanes
(2024)
In this work1, we have successfully synthesised amorphous zirconium chloro fluoride (ZCF), which exhibits medium lewis acidity. In addition to investigating the local coordination sphere around the Zr atoms and the material properties, we were able to establish a catalytic behavior of ZCF in C-F bond activation reactions. We present the first heterogeneous catalyst that performs dehydrofluorination of a fluoroalkane and consecutive hydrofluorination of an alkyne at room temperature.
The LOBSTER (Deringer et al., 2011;Maintz et al., 2013 ,2016 ;Nelson et al., 2020 ) software aids in extracting quantum-chemical bonding information from materials by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. LobsterEnv, a module implemented in pymatgen (Ong et al., 2013) by some of the authors of this package, facilitates the use of quantum-chemical bonding information obtained from LOBSTER calculations to identify neighbors and coordination environments. LobsterPy is a Python package that offers a set of convenient tools to further analyze and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both LobsterPy and LobsterEnv capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs) (Dronskowski & Blöchl, 1993), both can now also analyze Crystal Orbital Overlap Populations (COOP) (Hughbanks & Hoffmann, 1983) and Crystal Orbital Bond Index (COBI) (Müller et al., 2021). Extracting the information about the most important orbitals contributing to the bonds is optional, and users can enable it as needed. Additionally, bonding-based features for machinelearning (ML) studies can be engineered via the sub-packages “featurize” and “structuregraphs”. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. LobsterPy has been used to produce the results in Ngo et al. (2023), Chen et al. (2024), Naik et al. (2023), and it is also part of Atomate2 (2023) bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project (Jain et al., 2013) API.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying 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. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Crystalline materials that exhibit reversible mechanical responses upon exposure to external stimuli have garnered significant attention owing to their potential applications in various fields. Herein, we report a crystal of (2Z,2′Z)-2,2′-(1,4-phenylene)bis(3-(4-bromophenyl)acrylonitrile) (DSBr), which displays simultaneous elasto-plastic behaviour and reversible thermosalient effects. While elasto-plastic behaviour is attributed to underlying packing features, reversible thermosalient expansion is attributed to uniaxial expansion mediated by heat. Exceptional length increase and contraction upon cooling is due to the restorative nature of weak interactions through a cooperative effect. The cooperative movement of molecules is reflected in the unidirectional expansion of the habit plane. Thermosalient reversible expansion–contraction in elasto-plastic crystals have not been discussed in the literature so far. Detailed analysis reported herein provides a comprehensive understanding of the underlying mechanism of flexibility and thermosalient responses. This crystal's unique blend of reversible thermal expansion with flexibility holds substantial promise for applications in flexible thermal actuators.
This study investigates the mechanochemical reactions between AgBr 3-picolylamine and 4-picolylamine. The use of different stoichiometry ratios of the reagents allows [(AgBr)(n-pica)]n and [(AgBr)2(n-pica)]n to be obtained, and we report the new structures of [(AgBr)2(3-pica)]n and [(AgBr)2(4-pica)]n which are characterized by the presence of the following: (a) infinite inorganic chains, (b) silver atom coordinated only by bromide atoms and (c) argentophilic interactions. Furthermore, we studied the interconversion of [(AgBr)(n-pica)]n/[(AgBr)2(n-pica)]n by mechanochemical and thermal properties. The in situ experiments suggest that [(AgBr)(3-pica)]n is kinetically favoured while [(AgBr)2(3-pica)]n is converted into [(AgBr)(3-pica)]n only with a high excess of the ligand. Finally, the liquid nature of the ligands is not sufficient to assist the grinding process, and the complete reaction is observed with the addition of a small quantity of acetonitrile.
Jobflow is a free, open-source library for writing and executing workflows. Complex workflows can be defined using simple python functions and executed locally or on arbitrary computing resources using the FireWorks workflow manager.
Some features that distinguish jobflow are dynamic workflows, easy compositing and connecting of workflows, and the ability to store workflow outputs across multiple databases.