Understanding Structure-Function Relationships of Polyoxovanadate-Alkoxide Clusters from a Bottom-Up Perspective
Our group is interested in the computational exploration of the chemical space of molecular metal oxides towards the identification of key structure-function relationships relevant to their speciation, nucleation roadmaps, and overall physicochemical properties. In 2022, our group was awarded NSF's Faculty Early Career Development Program (CAREER) to expand our understanding of structure-function relationships in polyoxovanadate-alkoxide (POV-alkoxide) clusters from a bottom-up perspective. This project uses advanced computational approaches to understand the nucleation and growth of functionalized POV-alkoxide clusters, as well as the ability of these clusters to activate small molecules. We use high-level quantum chemical calculations and neural network algorithms to examine the nucleation mechanism of first-row functionalized POV-alkoxide clusters as well as to better understand the impact of dynamic experimental conditions on the structure-redox relationships and multi-electron reactivity of the clusters. Specifically, a combination of density functional theory calculations benchmarked against domain-based local pair natural orbital coupled cluster and second-order complete active space methodologies are being used to characterize nucleation intermediates and derive neural network potentials to streamline the exploration of the nucleation space. The scientific impacts of this work include transforming the way the organometallic chemistry community views function-oriented synthesis of these species using computational methodologies and to guide the discovery of new functionalities. Furthermore, this project provides advanced training opportunities for graduate and undergraduate students, including directed training opportunities for students from groups that are underrepresented in the physical sciences (hands-on workshops at neighboring tribal colleges, the engagement of tribal undergraduate students in STEM research, and National Chemistry Week activities for students at K-12 schools).
Engineering mechanisms of proton-coupled electron transfer to a titanium-substituted polyoxovanadate–alkoxide
Shannon E. Cooney, S. Genevieve Duggan, M. Rebecca A. Walls, Noah J. Gibson, James M. Mayer, Pere Miró, and Ellen M. Matson
Chemical Science, 2025, 16, 2886-2897
Computational Insights into the Nucleation of Mixed-Valent Polyoxovanadate Alkoxide Clusters
S. M. Gulam Rabbani, Andreas J. Achazi, and Pere Miró
Inorganic Chemistry 2021, 60, 10, 7262–7268
Nucleation Roadmap of Reduced Polyoxovanadate-Alkoxide Clusters
S. Genevieve Duggan, S. M. Gulam Rabbani, and Pere Miró
Inorganic Chemistry, 2025, ASAP
Computational Insights into Iron Heterometal Installation in Polyoxovanadate–Alkoxide Clusters
S. M. Gulam Rabbani and Pere Miró
Inorganic Chemistry 2023, 62, 5, 1797–1803
Lanthanide and Actinide Molecular Metal Oxides
Our group is interested in heavy element chemistry specifically the nucleation and speciation of lanthanide and low valent An(IV) metal oxides in aqueous and non-aqueous media. In this aspect, we are part of a collaborative DOE proposal aimed to transform critical materials separations through metal-oxo cluster chemistry.
Electronic Structure of Molybdenum Chalcogenide Clusters as Supports for Low-Valent Actinides
Pere Miró and S. Genevieve Duggan
Dalton Transactions, 2025, Accepted
Aqueous Speciation of Tetravalent Actinides in the Presence of Chloride and Nitrate Ligands
Rameswar Bhattacharjee and Pere Miró
Inorganic Chemistry, 2022, 61, 37, 14718-14725.
Machine Learning and Dynamic Chemical Speciation Networks
Another area of interest in our group is applying machine learning models to molecular and materials science problems. We recently used neural network potentials (NNPs) trained against density functional theory (DFT) to simultaneously predict the potential energy surface and multiple quantum-mechanical properties relevant to the optoeletronic properties of Cu2O. In this study, we also evaluated the uncertainty of our NNP since it is common to encounter regions far the training data during molecular dynamics simulations. We used the standard deviation of a NNP snapshot-ensemble as an uncertainty measure. We developed a TensorFlow code that interfaces with the Atomic Simulation Environment that automatically switches to using DFT during molecular dynamics simulations when the explored region of the potential energy surface is not properly described by the NNP. Furthermore, the newly calculated high-level data points can be used to retrain the NNP and incorporate poorly described potential energy surface regions. We also explored the application of symmetry functions to large chemical spaces using a new universal chemical descriptor developed in our group. This descriptor combines features from Behler-Parrinello’s symmetry functions with a periodic table representation in conjunction with convolutional neural network to reduce the number of neural network parameters to be trained and is being in various ongoing projects involving molecular metal oxides and alkoxides.
Application of Symmetry Functions to Large Chemical Spaces Using a Convolutional Neural Network
Balaranjan Selvaratnam, Ranjit T. Koodali, and Pere Miró
Journal of Chemical Information and Modeling 2020, 60, 4, 1928–1935
Prediction of optoelectronic properties of Cu2O using neural network potential
Balaranjan Selvaratnam, Ranjit T. Koodali, and Pere Miró
Physical Chemistry Chemical Physics 2020,22, 14910-14917