What is a Design Space?
Design spaces define the search area for AI models. This blog explains what they are, and how they can be used in AI powered materials research.
Utilization of machine learning to accelerate colloidal synthesis and discovery
Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work […]
Addressing challenges in high-temperature alloy design
High-temperature alloy design is challenging, but crucial to aeronautical applications. Citrine is working on a project to improve the representation of alloys data and develop techniques to to accurately predict properties outside of the range of input data.
Aggregating data to accelerate alloy design
Extracting, cleaning and analyzing and visualizing MPEA data.
Five High-Impact Research Areas in Machine Learning for Materials Science
Over the past several years, the field of materials informatics has grown dramatically. Applications of machine learning (ML) and artificial intelligence (AI) to materials science are now commonplace. As materials informatics has matured from a niche area of research into an established discipline, distinct frontiers of this discipline have come into focus, and best practices […]
Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures
Single-crystal diffraction is one of the most common experimental techniques in chemistry for determining a crystal structure. However, the process of crystal structure determination and refinement is not always straightforward. Methods for simplifying and rationalizing the path to the most optimal crystal structure model have been incorporated into various data processing and crystal structure solution […]
Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities
Data‐driven science and technology have helped achieve meaningful technological advancements in areas such as materials/drug discovery and health care, but efforts to apply high‐end data science algorithms to the areas of glass and ceramics are still limited. Many glass and ceramic researchers are interested in enhancing their work by using more data and data analytics […]
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 confirmation of ML predictions, either via experiment or via physics-based simulations. In this review, we first identify the core components common to materials informatics discovery pipelines, […]
Machine-Learning Guided Quantum Chemical and Molecular Dynamics Calculations to Design Novel Hole-Conducting Organic Materials
Materials exhibiting higher mobilities than conventional organic semiconducting materials such as fullerenes and fused thiophenes are in high demand for applications such as printed electronics, organic solar cells, and image sensors. In order to discover new molecules that might show improved charge mobility, combined density functional theory (DFT) and molecular dynamics (MD) calculations were performed, […]
Expanded dataset of mechanical properties and observed phases of multi-principal element alloys
This data article presents a compilation of mechanical properties of 630 multi-principal element alloys (MPEAs). Built upon recently published MPEA databases, this article includes updated records from previous reviews (with minor error corrections) along with new data from articles that were published since 2019. The extracted properties include reported composition, processing method, microstructure, density, hardness, yield […]