Literature DB >> 25899476

Conceptual inorganic materials discovery - a road map.

Martin Jansen1.   

Abstract

Synthesis of novel solids, which is a pivotal starting point in innovative materials research, is markedly impeded by the lack of predictability. A conception is presented that enables syntheses of new materials to be rationally planned. The approach is based on the atomic configuration space, and the potential energies associated to the atomic arrangements. Each minimum of the respective hyperspace of potential energy corresponds to a chemical compound capable of existence. Thus the whole realm of known and not-yet-known chemical compounds is represented in virtuo on that energy landscape. From this view it follows further that the full sets of the corresponding materials' properties are pre-determined. Within the scope of the "Energy Landscape Concept of Chemical Matter" presented, targets for synthesis are identified in a rational manner by searching the underlying potential energy landscapes for (meta)stable candidates computationally. Subsequently, the gained information are transferred to finite temperatures, which enables phase diagrams to be calculated, including metastable manifestations of matter, from first principles. The subsequent steps in materials discovery, e.g., assessing the properties and the impact of defects on the performance of the solids predicted are addressed briefly. The approach presented is complete and physically consistent; its feasibility has been proven and validated experimentally.
© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  calculation of phase diagrams; energy landscape concept; inorganic materials discovery; metastable materials; structure prediction

Year:  2015        PMID: 25899476     DOI: 10.1002/adma.201500143

Source DB:  PubMed          Journal:  Adv Mater        ISSN: 0935-9648            Impact factor:   30.849


  2 in total

1.  Extracting Crystal Chemistry from Amorphous Carbon Structures.

Authors:  Volker L Deringer; Gábor Csányi; Davide M Proserpio
Journal:  Chemphyschem       Date:  2017-03-08       Impact factor: 3.102

2.  Machine-learned and codified synthesis parameters of oxide materials.

Authors:  Edward Kim; Kevin Huang; Alex Tomala; Sara Matthews; Emma Strubell; Adam Saunders; Andrew McCallum; Elsa Olivetti
Journal:  Sci Data       Date:  2017-09-12       Impact factor: 6.444

  2 in total

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