Related papers: Evolutionary crystal structure prediction and nove…
Determining crystal structures from powder X-ray diffraction (PXRD) has been a significant challenge in materials science, particularly when experimental data contain noise or the target structure has a high complexity. While recent AI…
Efficiently and accurately determining the symmetry is a crucial step in the structural analysis of crystalline materials. Existing methods usually mindlessly apply deep learning models while ignoring the underlying chemical rules. More…
The discovery of new materials using crystal structure prediction (CSP) based on generative machine learning models has become a significant research topic in recent years. In this paper, we study invariance and continuity in the generative…
The 2D layered Ruddlesden-Popper crystal structure can host a broad range of functionally important behaviors. Here we establish extraordinary configurational disorder in a two dimensional layered Ruddlesden-Popper (RP) structure using…
Predicting phase stabilities of crystal polymorphs is central to computational materials science and chemistry. Such predictions are challenging because they first require searching for potential energy minima and then performing arduous…
Entropy alone can self-assemble hard particles into colloidal crystals of remarkable complexity whose structures are the same as atomic and molecular crystals, but with larger lattice spacings. Although particle-based molecular simulation…
Silver-based chalcohalide anti-perovskites (CAP), Ag$_{3}$BC (B = S, Se; C = Cl, Br, I), represent an emerging family of energy materials with intriguing optoelectronic, vibrational and ionic transport properties. However, the structural…
In this work, we employed \textit{ab initio} methods combined with evolutionary algorithms for searching stable structures for fluorine in the terapascal (TPa) regime. We performed several structural searches using the USPEX code, at…
Crystallization pressure drives deformation and damage in monuments, buildings and the Earth's crust. Even though the phenomenon has been known for 170 years there is no agreement between theoretical calculations of the maximum attainable…
The discovery of new multicomponent inorganic compounds can provide direct solutions to many scientific and engineering challenges, yet the vast size of the uncharted material space dwarfs current synthesis throughput. While the…
Molecular crystals often exist in multiple competing polymorphs, showing significantly different physico-chemical properties. Computational crystal structure prediction is key to interpret and guide the search for the most stable or useful…
Predicting crystal nucleation is among the most significant long--standing challenges in condensed matter. In the system most studied (hard sphere colloids), the comparison between experiments performed using static light scattering and…
We present an evolutionary construction technique of formation energy convex hull to search for thermodynamically stable compounds. In this technique, candidates with a wide variety of chemical compositions and crystal structures are…
Finding an optimal match between two different crystal structures underpins many important materials science problems, including describing solid-solid phase transitions, developing models for interface and grain boundary structures. In…
Over the past decade, evolutionary algorithms, data mining, and other methods showed great success in solving the main problem of theoretical crystallography: finding the stable structure for a given chemical composition. Here we develop a…
Using evolutionary crystal structure prediction algorithm USPEX, we showed that at pressures of the Earth's lower mantle CaAl2O4 is the only stable calcium aluminate. At pressures above 7.0 GPa it has the CaFe2O4-type structure and space…
Crystallization of the amorphous phases into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to synthesis and development of new materials in the laboratory.…
Physical systems are frequently modeled as sets of points in space, each representing the position of an atom, molecule, or mesoscale particle. As many properties of such systems depend on the underlying ordering of their constituent…
The discovery of new functional and stable materials is a big challenge due to its complexity. This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical…
Crystals are the foundation of numerous scientific and industrial applications. While various learning-based approaches have been proposed for crystal generation, existing methods seldom consider the space group constraint which is crucial…