Here, we propose a suite of ‘top-down’ design and ‘bottom-up’ discovery of porous metal oxides metal oxides and anticipate the formation of extended materials based upon these molecular building blocks, see Figure 4. Leveraging the decades-long expertise of the broad computational material science community and the large amount of data available at Density Functional Theory (DFT) level will be beneficial to obtain reliable description of POMs electronic structure, which can be used to  generate the database obtained by exploring the chemical space of POMzite materials.[25] This initial database will be explored and refined using principal component analysis that will be used to establish a new bottom-up method. Top-down methods will rely on analysis of the structural database, and the development of deep neural network produce new synthetic routes by developing theory from aggregated observations using Reinforced Learning (RL) algorithms.
Figure 4. The estimated accessible chemical space generated by expanding the POMzite family.
The general objective is to develop a new conceptual approach for the fabrication of functional nano-molecules and adaptive materials. Proposing to explore the hypothesis of first predicting and then synthesizing new POMzite materials. The ideal outcome of the research proposed here is to predict new POM all-inorganic framework materials, followed by experimental realization by our collaborators. By theoretically expanding the acessible chemical space generated by the derivatization of polyoxometalate (POM) clusters, which enables their assembly into a range of frameworks by use of inorganic linkers.[2] These robust all-inorganic frameworks are made using metal-ion linkers (Co, Mn, Ni, Ag), which combine molecular synthetic control without the need for organic components.[1] Later generating the heterogeneous database of POMzite materials, 1.6 107 estimated structures, see Figure 4. For instance, so far we have just explored 4 transition metals as linkers, out of 30 possibilites
We have the tools to understand POMzites at a molecular level and understand the interactions between the electrons of atoms bound in molecules using ‘first principle’ techniques DFT algorithms. By mapping their electronic structure by modelling, using an ab init o packages with basis sets and functionals for transition metal clusters. The cyclic heteropolyanion [P8W48O184]40−(abbreviated as {P8W48}) is the current building block for the construction of porous framework materials. Preliminary gas-phase calculations confirmed the stability of {P8W48} as an initial structural motif, [26] derivatives such as the Se equivalent are also stable. Theoretically derivate other cyclic POMs with different heteroatoms (As, Sb, S, Te) and asses their potential to aggregate into porous structures. We are also interested in understanding the binding sites of the POMzite materials, this will only be possible by describing their molecular structure. This basic computational setup will provide with data (e.g. molecular charge, optimized geometry, HLgaps, etc.) to start the initial layer of a neural network.
Given the size and the characteristics of their unique porous environment, we will need macroscale modelling of the POMzite architectures. To advance this area, we need to combine experimental input with computational modelling to imagine a wide range of different rearrangements, and to create a blueprint for building these materials.[27] Aiming to understand the connectivity and the stability of {P8W48} rings in POMzite structures and predict new motifs from the results, as well as being able to anticipate new favourable structures with different linker modes. Given the fact that some POMzites have just been observed with Co or Ni, e.g. POMzite-5, by modelling we will be able to substitute those linkers with another TM. We use simple topological methods such as stochastic modelling to make predictions of the different binding sites.[28] We will be able to predict new bulk physical properties from assemblies of these building blocks e.g. electropotential, acidity, optical properties, electronic storage. Once we have fully understood the chemical system and created a database of all the molecular architectures, we would like to predict their physical properties.
The data generated in this project will provide a greater insight in the binding site and the resulting in the crude heteregoneous framework materials database, with an estimate of 1.6 107structures. The curation of this database will require initial data pre-processing of the POMzite geometries, checking its format and presenting them in an understandable format. This will be the ideal training set to use an end-to-end Reniforcement Learning (RL) approach to investigate whether is possible to predict new and stable POMzite materials. We propose to use a modular behaviour-based reinforcement architecture that will start training neural networks with a known solution,[29] leveraging on knowledge of known, and/or predicted to be stable, POMzite materials. Classically a learning algorithm training will split the dataset into 3 sets: training, validation, and testing datasets. The first one is used in the learning process, where the model parameters are obtained. Once we obtain an optimal set of parameters, the test set is used to assess the performance of the model. If the obtained model is unsuccessful, the previous steps are repeated with improved data selection, representation, transformation, sampling, and removing outliers, or by changing the algorithm altogether.[10]