Optimizing molecular properties using a relative index of thermodynamic stability and global optimization techniques

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

Are zinc clusters really amorphous? A detailed protocol for locating global minimum structures of clusters

We report the results of a conjoint experimental/theoretical effort to assess the structures of free-standing zinc clusters with up to 73 atoms. Experiment provides photoemission spectra for ZnN- cluster anions, to be used as fingerprints in structural assessment, as well as mass spectra for both anion and cation clusters. Theory provides both a detailed description of a novel protocol to locate global minimum structures of clusters in an efficient and reliable way, and its specific application to neutral and charged zinc clusters. Our methodology is based on the well-known hybrid EP-DFT (empirical potential-density functional theory) approach, in which the approximate potential energy surface generated by an empirical Gupta potential is first sampled with unbiased basin hopping simulations, and then a selection of the isomers so identified is re-optimized at a first-principles DFT level. The novelty introduced in our paper is a simple but efficient new recipe to obtain the best possible EP parameters for a given cluster system, with which the first step of the EP-DFT method is to be performed. Our method is able to reproduce experimental measurements at an excellent level for most cluster sizes, implying its ability to locate the true global minimum structures; meanwhile, if exactly the same method is applied based on the existing Gupta potential (fitted to bulk properties), it leads to wrong predicted structures with energies between 1 and 2 eV above the correct ones. Opposite to what was claimed in the past, our work unequivocally demonstrates that Zn clusters are not amorphous, and they rather adopt high symmetry structures for most sizes. We show that Zn clusters have a number of exotic, unprecedented structural and electronic properties which are not expected for clusters of a metallic element, and describe them in detail.

Exploring experimental fitness landscapes for chemical synthesis and property optimization

The topology of experimental fitness landscapes for chemical optimization objectives is assessed through svr-based HDMR modeling.