Materials discovery through machine learning formation energy
GGC Peterson, J Brgoch - Journal of Physics: Energy, 2021 - iopscience.iop.org
The budding field of materials informatics has coincided with a shift towards artificial
intelligence to discover new solid-state compounds. The steady expansion of repositories for …
intelligence to discover new solid-state compounds. The steady expansion of repositories for …
[HTML][HTML] Computational discovery of energy materials in the era of big data and machine learning: a critical review
Z Lu - Materials Reports: Energy, 2021 - Elsevier
The discovery of novel materials with desired properties is essential to the advancements of
energy-related technologies. Despite the rapid development of computational infrastructures …
energy-related technologies. Despite the rapid development of computational infrastructures …
A critical examination of compound stability predictions from machine-learned formation energies
Abstract Machine learning has emerged as a novel tool for the efficient prediction of material
properties, and claims have been made that machine-learned models for the formation …
properties, and claims have been made that machine-learned models for the formation …
Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations
While high-throughput density functional theory (DFT) has become a prevalent tool for
materials discovery, it is limited by the relatively large computational cost. In this paper, we …
materials discovery, it is limited by the relatively large computational cost. In this paper, we …
Network analysis of synthesizable materials discovery
Assessing the synthesizability of inorganic materials is a grand challenge for accelerating
their discovery using computations. Synthesis of a material is a complex process that …
their discovery using computations. Synthesis of a material is a complex process that …
Machine-learning rationalization and prediction of solid-state synthesis conditions
There currently exist no quantitative methods to determine the appropriate conditions for
solid-state synthesis. This not only hinders the experimental realization of novel materials …
solid-state synthesis. This not only hinders the experimental realization of novel materials …
Artificial intelligence driving materials discovery? perspective on the article: Scaling deep learning for materials discovery
AK Cheetham, R Seshadri - Chemistry of Materials, 2024 - ACS Publications
The discovery of new crystalline inorganic compounds─ novel compositions of matter within
known structure types, or even compounds with completely new crystal structures─ …
known structure types, or even compounds with completely new crystal structures─ …
Free energy predictions for crystal stability and synthesisability
What is the likelihood that a hypothetical material—the combination of a composition and
crystal structure—can be formed? Underpinning the reliability of predictions for local or …
crystal structure—can be formed? Underpinning the reliability of predictions for local or …
Applying machine learning techniques to predict the properties of energetic materials
We present a proof of concept that machine learning techniques can be used to predict the
properties of CNOHF energetic molecules from their molecular structures. We focus on a …
properties of CNOHF energetic molecules from their molecular structures. We focus on a …
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 …