Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Artificial intelligence and machine learning in design of mechanical materials
Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …
is becoming an important tool in the fields of materials and mechanical engineering …
Unsupervised word embeddings capture latent knowledge from materials science literature
The overwhelming majority of scientific knowledge is published as text, which is difficult to
analyse by either traditional statistical analysis or modern machine learning methods. By …
analyse by either traditional statistical analysis or modern machine learning methods. By …
Opportunities and challenges for machine learning in materials science
Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …
the discovery of novel materials and the improvement of molecular simulations, with likely …
Machine learning for catalysis informatics: recent applications and prospects
The discovery and development of catalysts and catalytic processes are essential
components to maintaining an ecological balance in the future. Recent revolutions made in …
components to maintaining an ecological balance in the future. Recent revolutions made in …
The role of machine learning in the understanding and design of materials
Develo** algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …
enable us to systematically find novel materials, which can have huge technological and …
Chemistry-informed machine learning for polymer electrolyte discovery
Solid polymer electrolytes (SPEs) have the potential to improve lithium-ion batteries by
enhancing safety and enabling higher energy densities. However, SPEs suffer from …
enhancing safety and enabling higher energy densities. However, SPEs suffer from …
Data-driven materials research enabled by natural language processing and information extraction
Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …
society, but particularly in the scientific domain, there is increased importance placed on …
A strategy to apply machine learning to small datasets in materials science
There is growing interest in applying machine learning techniques in the research of
materials science. However, although it is recognized that materials datasets are typically …
materials science. However, although it is recognized that materials datasets are typically …
A review of the recent progress in battery informatics
Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …
transportation in the current and future society. Recently machine learning (ML) has …