Deep Learning Classification of Crystal Structures Utilizing Wyckoff Positions
Crystals, 2022 In materials science, crystal lattice structures are the primary metrics used to measure the structure–property paradigm of a crystal structure. Crystal compounds are understood by the number of various atomic chemical settings, which are associated with
Nada Ali Hakami +1 more
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Prediction of Structure, Function, and Spectroscopic Properties of G-Protein-Coupled Receptors: Methods and Applications [PDF]
, 2004 G-protein-coupled receptors are of great pharmaceutical interest, comprising the majority of targets for currently marketed drugs. The theme of my thesis is the development of the structure prediction method, MembStruk, for the superfamily of G-protein ...
Trabanino, Rene Jouvanni
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npj Computational Materials, 2023 A desired prerequisite when performing a quantum mechanical calculation is to have an initial idea of the atomic positions within an approximate crystal structure. The atomic positions combined should result in a system located in, or close to, an energy
Adam Carlsson +2 more
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, 2020 We describe the implementation of a Monte Carlo basin hopping global optimization procedure for the prediction of molecular crystal structure. The basin hopping method is combined with quasi-random structure generation in a hybrid method for crystal ...
Shiyue, Yang, Graeme, Day
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Machine Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes
, 2023 A primary challenge in organic molecular crystal structure prediction (CSP) is accurately ranking the energies of potential structures. While high-level solid-state density functional theory (DFT) methods allow for mostly reliable discrimination of the ...
Graeme, Day +2 more
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Calculation of the free energy of crystalline solids [PDF]
, 2013 The prediction of the packing of molecules into crystalline phases is a key step in understanding the properties of solids. Of particular interest is the phenomenon of polymorphism, which refers to the ability of one compound to form crystals with ...
Vasileiadis, Manolis
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Accelerating Crystal Structure Prediction with Machine Learning Forcefields [PDF]
Struct DynLong-standing methods in materials simulation can now generally predict crystalline structure for near-/stable materials with high accuracy, and independently of local materials chemistry.
Aaron D Kaplan
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How crystal structure prediction can impact small-molecule pharmaceutical development: past examples, success stories, and future prospects. [PDF]
Struct DynThe impact of crystal structure prediction on the development of small-molecule pharmaceutical products cannot be overstated. The choice of a thermodynamically metastable crystal structure as the lead development form, and the unexpected appearance of a ...
Luca Iuzzolino
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A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/Property Prediction
CrystalsCrystal Property Prediction (CPP) and Crystal Structure Prediction (CSP) play an important role in accelerating the design and discovery of advanced materials across various scientific disciplines.
Mostafa Sadeghian +2 more
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Aniline–phenol recognition: from solution through supramolecular synthons to cocrystals
IUCrJ, 2014 Aniline–phenol recognition is studied in the crystal engineering context in several 1:1 cocrystals that contain a closed cyclic hydrogen-bonded [...O—H...N—H...]2 tetramer supramolecular synthon (II). Twelve cocrystals of 3,4,5- and 2,3,4-trichlorophenol
Arijit Mukherjee +3 more
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