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Abstract In recent years, substantial progress has been made in the modeling of organic solids. Computer simulation has been increasingly shaping the area of new organic materials by design. It is possible to discover new organic crystals by computational structure prediction, based on the combination of powerful exploratory algorithms and accurate energy modeling. In this review, we begin with several key early concepts in describing crystal packing, and then introduce the recent state-of-the-art computational techniques for organic crystal structure prediction. Perspectives on the remaining technical challenges, functional materials screening and software development are also discussed in the end. It is reasonable to expect that, in the near future, accurate predictive computational modeling can be accomplished within a time frame that is appreciably shorter than that needed for the laboratory synthesis and characterization. Graphical abstract 

Abstract We present PyXtal_FF—a package based on Python programming language—for developing machine learning potentials (MLPs). The aim of PyXtal_FF is to promote the application of atomistic simulations through providing several choices of atom-centered descriptors and machine learning regressions in one platform. Based on the given choice of descriptors (including the atom-centered symmetry functions, embedded atom density, SO4 bispectrum, and smooth SO3 power spectrum), PyXtal_FF can train MLPs with either generalized linear regression or neural network models, by simultaneously minimizing the errors of energy/forces/stress tensors in comparison with the data fromab-initiosimulations. The trained MLP model from PyXtal_FF is interfaced with the Atomic Simulation Environment (ASE) package, which allows different types of light-weight simulations such as geometry optimization, molecular dynamics simulation, and physical properties prediction. Finally, we will illustrate the performance of PyXtal_FF by applying it to investigate several material systems, including the bulk SiO₂, high entropy alloy NbMoTaW, and elemental Pt for general purposes. Full documentation of PyXtal_FF is available athttps://pyxtal-ff.readthedocs.io. 

Abstract In this work, we employ density functional theory simulations to investigate possible spin polarization of CeO 2 -(111) surface and its impact on the interactions between a ceria support and Pt nanoparticles. With a Gaussian type orbital basis, our simulations suggest that the CeO 2 -(111) surface exhibits a robust surface spin polarization due to the internal charge transfer between atomic Ce and O layers. In turn, it can lower the surface oxygen vacancy formation energy and enhance the oxide reducibility. We show that the inclusion of spin polarization can significantly reduce the major activation barrier in the proposed reaction pathway of CO oxidation on ceria-supported Pt nanoparticles. For metal-support interactions, surface spin polarization enhances the bonding between Pt nanoparticles and ceria surface oxygen, while CO adsorption on Pt nanoparticles weakens the interfacial interaction regardless of spin polarization. However, the stable surface spin polarization can only be found in the simulations based on the Gaussian type orbital basis. Given the potential importance in the design of future high-performance catalysts, our present study suggests a pressing need to examine the surface ferromagnetism of transition metal oxides in both experiment and theory.