High-Pressure Structural Behavior of <i>para</i>-Xylene

Konar, Sumit; Hobday, Claire L.; Bull, Craig L.; Funnell, Nicholas P.; Chan, Qi Feng; Fong, Angela; Atceken, Nurunnisa; Pulham, Colin R.

doi:10.1021/acs.cgd.2c00249

Cited by 3 publications

(2 citation statements)

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“…These findings indicate that neural network-based methods could greatly advance structure solutions from powder diffraction patterns. The present methods for solving structures from such data are time consuming and frequently lead to ambiguity and errors (44)(45)(46).…”

Section: Application To Experimental X-ray Diffraction Datamentioning

confidence: 99%

PhAI: A deep-learning approach to solve the crystallographic phase problem

Larsen,

Rekis,

Madsen

2024

Science

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X-ray crystallography provides a distinctive view on the three-dimensional structure of crystals. To reconstruct the electron density map, the complex structure factors F = F exp i ϕ of a sufficiently large number of diffracted reflections must be known. In a conventional experiment, only the amplitudes F are obtained, and the phases ϕ are lost. This is the crystallographic phase problem. In this work, we show that a neural network, trained on millions of artificial structure data, can solve the phase problem at a resolution of only 2 angstroms, using only 10 to 20% of the data needed for direct methods. The network works in common space groups and for modest unit-cell dimensions and suggests that neural networks could be used to solve the phase problem in the general case for weakly scattering crystals.

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Section: Application To Experimental X-ray Diffraction Datamentioning

confidence: 99%

PhAI: A deep-learning approach to solve the crystallographic phase problem

Larsen,

Rekis,

Madsen

2024

Science

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“…High-pressure research of molecular crystals is an important direction of modern crystallography [1][2][3][4][5][6][7][8][9][10][11]. A lot of organic systems have polymorphic modifications at ambient pressure, while other organic crystals undergo phase transitions at extreme conditions [12,13].…”

Section: Introductionmentioning

confidence: 99%

Computational Investigation of the Stability of Di-p-Tolyl Disulfide “Hidden” and “Conventional” Polymorphs at High Pressures

2022

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The investigation of molecular crystals at high pressure is a sought-after trend in crystallography, pharmaceutics, solid state chemistry, and materials sciences. The di-p-tolyl disulfide (CH3−C6H4−S−)2 system is a bright example of high-pressure polymorphism. It contains “conventional” solid–solid transition and a “hidden” form which may be obtained only from solution at elevated pressure. In this work, we apply force field and periodic DFT computational techniques to evaluate the thermodynamic stability of three di-p-tolyl disulfide polymorphs as a function of pressure. Theoretical pressures and driving forces for polymorphic transitions are defined, showing that the compressibility of the γ phase is the key point for higher stability at elevated pressures. Transition state energies are also estimated for α → β and α → γ transitions from thermodynamic characteristics of crystal structures, not exceeding 5 kJ/mol. The β → γ transition does not occur experimentally in the 0.0–2.8 GPa pressure range because transition state energy is greater than 18 kJ/mol. Relations between free Gibbs energy (in assumption of enthalpy) of phases α, β, and γ, as a function of pressure, are suggested to supplement and refine experimental data. A brief discussion of the computational techniques used for high-pressure phase transitions is provided.

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