Machine-enabled inverse design of inorganic solid materials: promises and challenges
Developing high-performance advanced materials requires a deeper insight and search into
the chemical space. Until recently, exploration of materials space using chemical intuitions …
the chemical space. Until recently, exploration of materials space using chemical intuitions …
Design and exploration of semiconductors from first principles: A review of recent advances
Recent first-principles approaches to semiconductors are reviewed, with an emphasis on
theoretical insight into emerging materials and in silico exploration of as-yet-unreported …
theoretical insight into emerging materials and in silico exploration of as-yet-unreported …
Machine learning for materials scientists: an introductory guide toward best practices
This Methods/Protocols article is intended for materials scientists interested in performing
machine learning-centered research. We cover broad guidelines and best practices …
machine learning-centered research. We cover broad guidelines and best practices …
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition
Conventional machine learning approaches for predicting material properties from
elemental compositions have emphasized the importance of leveraging domain knowledge …
elemental compositions have emphasized the importance of leveraging domain knowledge …
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
The current predictive modeling techniques applied to Density Functional Theory (DFT)
computations have helped accelerate the process of materials discovery by providing …
computations have helped accelerate the process of materials discovery by providing …
Machine learning in materials discovery: confirmed predictions and their underlying approaches
The rapidly growing interest in machine learning (ML) for materials discovery has resulted in
a large body of published work. However, only a small fraction of these publications includes …
a large body of published work. However, only a small fraction of these publications includes …
Stability and equilibrium structures of unknown ternary metal oxides explored by machine-learned potentials
Ternary metal oxides are crucial components in a wide range of applications and have been
extensively cataloged in experimental materials databases. However, there still exist cation …
extensively cataloged in experimental materials databases. However, there still exist cation …
Deep dive into machine learning density functional theory for materials science and chemistry
With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
Crystalgan: learning to discover crystallographic structures with generative adversarial networks
A Nouira, N Sokolovska, JC Crivello - arXiv preprint arXiv:1810.11203, 2018 - arxiv.org
Our main motivation is to propose an efficient approach to generate novel multi-element
stable chemical compounds that can be used in real world applications. This task can be …
stable chemical compounds that can be used in real world applications. This task can be …
Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds
Discovering novel, multicomponent crystalline materials is a complex task owing to the large
space of feasible structures. Here we demonstrate a method to significantly accelerate …
space of feasible structures. Here we demonstrate a method to significantly accelerate …