Machine Learning in Inorganic Materials Chemistry
A special issue of Inorganics (ISSN 2304-6740). This special issue belongs to the section "Inorganic Solid-State Chemistry".
Deadline for manuscript submissions: closed (1 December 2019) | Viewed by 229
Special Issue Editor
Special Issue Information
Dear Colleagues,
Machine learning (ML) algorithms, which aim to extract knowledge from large amounts of data, are increasingly used to solve basic problems in physics and chemistry. This is largely driven by the recent availability of raw supercomputing power, but it also enables new ways of using the structural knowledge that chemists have collected for decades—in the Inorganic Crystal Structure Database (ICSD), for example. In the recent years, ML-based techniques have begun to identify promising synthesis targets from the wide space of candidate compositions, leading to possible new superhard materials, for example. Atomic, molecular, and materials properties have been successfully “machine-learned”; this includes, but is not limited to, atomization energies, band gaps, or NMR chemical shifts. Finally, ML-based interatomic potentials (force fields) are becoming increasingly popular: by fitting to accurate quantum-mechanical reference data, these ML-driven simulation methods can create structural models of materials with an unprecedented combination of accuracy and speed. Today, these methods are beginning to be applicable to questions in solid-state and materials chemistry, such as the chemical reactivity at surfaces and interfaces, or the search for new crystal structures. This Special Issue is meant to capture a snapshot of this exciting and emerging field. It is hoped that it will inspire new work at the intersection of ML-driven techniques, solid-state and materials chemistry, and neighboring research fields.
Dr. Volker Deringer
Guest Editor
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Keywords
- machine learning (ML)
- data mining and Open Data
- artificial neural network (NN) models
- kernel methods
- ML-based interatomic potentials (force fields)
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