Coevolutionary search for optimal materials in the space of all possible compounds

Allahyari, Zahed; Oganov, Artem R.

doi:10.1038/s41524-020-0322-9

Cited by 24 publications

(22 citation statements)

References 74 publications

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“…Very recently, a coevolutionary method, called Mendelevian search, was developed [76]. It can be viewed as evolution of a population of evolutionary searches, each of which focuses on a particular chemical system; these systems compete and exchange information with each other, leading to progressively better systems being sampled.…”

Section: [H1] Introductionmentioning

confidence: 99%

Structure prediction drives materials discovery

et al. 2019

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Progress in the discovery of new materials has been accelerated by the development of reliable quantum-mechanical approaches to crystal structure prediction. The properties of a material depend very sensitively on its structure, therefore structure prediction is the key to computational materials discovery. Structure prediction was considered to be a formidable problem, but the development of new computational tools has allowed the structures of many new and increasingly complex materials to be anticipated. These widely applicable methods, based on global optimisation and relying on little or no empirical knowledge, have been used to study crystalline structures, point defects, surfaces and interfaces. In this Review we discuss structure prediction methods, examining their potential for the study of different materials systems, and present examples of computationally-driven discoveries of new materials -including superhard materials, superconductors and organic materials -that will enable new technologies. Advances in firstprinciple structure predictions also lead to a better understanding of physical and chemical phenomena in materials.

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Section: [H1] Introductionmentioning

confidence: 99%

Structure prediction drives materials discovery

et al. 2019

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“…Materials discovery − is a promising ML application. Atomic- or molecular-scale calculations are performed over a wide range of compounds to map atomic/molecular variations to macroscopically relevant properties.…”

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confidence: 99%

How Machine Learning Will Revolutionize Electrochemical Sciences

et al. 2021

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Electrochemical systems function via interconversion of electric charge and chemical species and represent promising technologies for our cleaner, more sustainable future. However, their development time is fundamentally limited by our ability to identify new materials and understand their electrochemical response. To shorten this time frame, we need to switch from the trial-and-error approach of finding useful materials to a more selective process by leveraging model predictions. Machine learning (ML) offers data-driven predictions and can be helpful. Herein we ask if ML can revolutionize the development cycle from decades to a few years. We outline the necessary characteristics of such ML implementations. Instead of enumerating various ML algorithms, we discuss scientific questions about the electrochemical systems to which ML can contribute.

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“…Moreover, a new non-empirical method for the prediction of material(s) among all possible combinations of all elements has been reported recently [ 115 ]. It was stated that this method possesses the best combination of target properties because it combines a new coevolutionary approach with the carefully restructured “Mendelevian” chemical space, energy filtering, and Pareto optimization to ensure that the predicted materials have optimal properties and a high chance to be synthesizable.…”

Section: Importance Of High-pressure For Synthesis Of New Oxide Sumentioning

confidence: 99%

Layered Cuprates Containing Flat Fragments: High-Pressure Synthesis, Crystal Structures and Superconducting Properties

Kopnin

2021

Molecules

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High-pressure synthesis and crystal structures of the homologous series AuBa2(Ca,Ln)n−1CunO2n+3 (n = 1–4; Ln = rare-earth cations) are described. Their crystal structures and superconducting properties are compared with the corresponding members of the Hg-homologous series. Numerous cuprates containing flat structural fragments (CuO4, CO3 and BO3) synthesized mainly at high pressure are compared in terms of structural peculiarities and superconducting properties. Importance and future prospects of high-pressure application for the preparation of new superconducting oxides are discussed.

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Coevolutionary search for optimal materials in the space of all possible compounds

Cited by 24 publications

References 74 publications

Structure prediction drives materials discovery

Structure prediction drives materials discovery

How Machine Learning Will Revolutionize Electrochemical Sciences

Layered Cuprates Containing Flat Fragments: High-Pressure Synthesis, Crystal Structures and Superconducting Properties

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