Ma, CALYPSO: a method for crystal structure prediction, Comput. Phys. Commun. 183 (2012) 2063-2070.Wang, Y.; Lv, J.; Zhu, L.; Ma, Y. CALYPSO : A Method for Crystal Structure Prediction. Comput. Phys. Commun. 2012, 183, 2063-2070....
We have developed a software package CALYPSO (Crystal structure AnaLYsis by Particle Swarm Optimization) to predict the energetically stable/metastable crystal structures of materials at given chemical compositions and external conditions (e.g., pressure). The CALYPSO method is based on several major te...
Challenges of crystal structure prediction of diastereomeric salt pairs. A methodology for the computational prediction of the crystal structures and resolution efficiency for diastereomeric salt pairs is developed by considering the polymorphic system of the diastereomeric salt pair (R)-1-phenylethylammonium...
We present a method that measures the accuracy of NMR protein structures. It compares random coil index [RCI] against local rigidity predicted by mathematical rigidity theory, calculated from NMR structures [FIRST], using a correlation score (which asses
We present a machine learning approach for high-throughput crystal orientation mapping, which relies on the optical technique called directional reflectance microscopy. We successfully apply our method on Inconel 718 specimens produced by additive manufacturing, which exhibit complex, spatially-varying ...
crystal structure predictionevolutionary algorithmsinorganic solidspotential energy landscapevibrational normal modesSummary The computational prediction of new materials for specific applications hinges on the ability to prophesize their crystal structures prior to their synthesis. This is just one of the ...
The Cys-57°FMN models were constructed on the basis of the crystal structures. Geometry optimization of the models and electronic property calculations were performed with ab initio complete active space self-consistent field method (CASSCF; Roos, 1987). The energies of the optimized structures were...
et al. Multi-Scale Response Analysis and Displacement Prediction of Landslides Using Deep Learning with JTFA: A Case Study in the Three Gorges Reservoir, China. Remote Sensing, 2023, 15(16): 3995. DOI:10.3390/rs15163995 99. Zou, Z., Luo, T., Zhang, S. et al. A novel method to ...
Machine learning, trained on quantum mechanics (QM) calculations, is a powerful tool for modeling potential energy surfaces. A critical factor is the quality and diversity of the training dataset. Here we present a highly automated approach to dataset construction and demonstrate the method by buildi...
it is now possible to build models that learn complex functions of protein sequence and structure, including models for protein backbone generation18,19,20 and protein structure prediction21,22; as a result, we were curious as to whether an entirely learned method could be used to design protein...