Materials discovery via CALYPSO methodology

  title={Materials discovery via CALYPSO methodology},
  author={Yanchao Wang and Jian Lv and Li Zhu and Shaohua Lu and Ketao Yin and Quan Li and Hui Wang and Lijun Zhang and Yanming Ma},
  journal={Journal of Physics: Condensed Matter},
The structure prediction at the atomic level is emerging as a state-of-the-art approach to accelerate the functionality-driven discovery of materials. By combining the global swarm optimization algorithm with first-principles thermodynamic calculations, it exploits the power of current supercomputer architectures to robustly predict the ground state and metastable structures of materials with only the given knowledge of chemical composition. In this Review, we provide an overview of the basic… 

Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface.

By combining a state-of-art machine learning (ML) potential with the in-house developed CALYPSO structure prediction method, two acceleration schemes for the structure prediction of large systems are developed, in which a ML potential is pre-constructed to fully replace DFT calculations or trained in an on-the-fly manner from scratch during the structure searches.

Crystal Structure Prediction via Efficient Sampling of the Potential Energy Surface.

ConspectusThe crystal structure prediction (CSP) has emerged in recent years as a major theme in research across many scientific disciplines in physics, chemistry, materials science, and geoscience,

Accelerated discovery of two crystal structure types in a complex inorganic phase field

This approach will accelerate the systematic discovery of new materials in complex compositional spaces by efficiently guiding synthesis and enhancing the predictive power of the computational tools through expansion of the knowledge base underpinning them.


Crystal structure prediction is now playing an increasingly important role in discovery of new materials. Global optimization methods such as genetic algorithms (GA) and particle swarm optimization

Materials discovery through machine learning formation energy

This Review discusses the specific design choices for developing a machine learning model capable of predicting formation energy, including the thermodynamic quantities governing material stability, and investigates several models presented in the literature that have succeeded in uncovering new DFT-stable compounds and directing materials synthesis.

Crystal Structure Prediction Using an Age-Fitness Multiobjective Genetic Algorithm and Coordination Number Constraints.

Results show that compared to the previous CMCrystal algorithm, the multiobjective crystal structure prediction algorithm (C MCrystalMOO) can reconstruct the crystal structure with higher quality and alleviate the problem of premature convergence.

Contact map based crystal structure prediction using global optimization

It is shown that it is viable to reconstruct the crystal structure given the atomic contact map for some crystal materials but more constraints are needed for other target materials to achieve successful reconstruction, implying that atomic interaction information learned from existing materials can be used to improve crystal structure prediction.

TCSP: a Template-Based Crystal Structure Prediction Algorithm for Materials Discovery.

This work develops a template-based crystal structure prediction (TCSP) algorithm and its companion web server, which makes this tool accessible to all materials researchers.



Computational Alchemy: The Search for New Superhard Materials

A central challenge to modern materials science is the rational design and synthesis of new materials possessing exceptional properties. Recent advances in first-principles modeling methods and the

Crystal structure prediction using ab initio evolutionary techniques: principles and applications.

We have developed an efficient and reliable methodology for crystal structure prediction, merging ab initio total-energy calculations and a specifically devised evolutionary algorithm. This method

CALYPSO: A method for crystal structure prediction

Crystal structure prediction from first principles.

The current state of the art in this field is illustrated with topical applications to inorganic, especially microporous solids, and to molecular crystals; the field also looks at applications to nanoparticulate structures.

First Step Towards Planning of Syntheses in Solid‐State Chemistry: Determination of Promising Structure Candidates by Global Optimization

A method is presented here that allows, in principle, the prediction of the existence and structure of (meta)stable solid compounds. It is based on a set of adjustable modules that are applied to the

Ab initio random structure searching

  • C. PickardR. Needs
  • Chemistry
    Journal of physics. Condensed matter : an Institute of Physics journal
  • 2011
This work describes a simple, elegant and powerful approach to searching for structures with DFT, which it calls ab initio random structure searching (AIRSS).

Crystal structure prediction via particle-swarm optimization

A method for crystal structure prediction from ``scratch'' through particle-swarm optimization (PSO) algorithm within the evolutionary scheme and illustrates the promise of PSO as a major technique on crystal structure determination.

Big bang methodology applied to atomic clusters

An implementation of a novel strategy for cluster geometry optimization, using a stochastic method, is applied. This algorithm is based on the spirit of Big Bang theory. The strategy consists on a

XtalOpt: An open-source evolutionary algorithm for crystal structure prediction

Opportunities and challenges for first-principles materials design and applications to Li battery materials

The idea of first-principles methods is to determine the properties of materials by solving the basic equations of quantum mechanics and statistical mechanics. With such an approach, one can, in