Patrick Huck
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View article: Accelerated data-driven materials science with the Materials Project
Accelerated data-driven materials science with the Materials Project Open
The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than…
View article: The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity
The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity Open
Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-dri…
View article: The ab initio amorphous materials database: Empowering machine learning to decode diffusivity
The ab initio amorphous materials database: Empowering machine learning to decode diffusivity Open
Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven ex…
View article: Gold-Thiolate Nanocluster Dynamics Dataset and Supplementary Files
Gold-Thiolate Nanocluster Dynamics Dataset and Supplementary Files Open
Supplementary information accompanying the article “Gold-Thiolate Nanocluster Dynamics and Intercluster Reactions Enabled by a Machine Learned Interatomic Potential” including the training/testing dataset, potential files, and simulation r…
View article: The ab initio amorphous materials database: Empowering machine learning to decode diffusivity
The ab initio amorphous materials database: Empowering machine learning to decode diffusivity Open
Amorphous materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven ex…
View article: An equivariant graph neural network for the elasticity tensors of all seven crystal systems
An equivariant graph neural network for the elasticity tensors of all seven crystal systems Open
An equivariant graph neural network model enables the rapid and accurate prediction of complete fourth-rank elasticity tensors of inorganic materials, facilitating the discovery of materials with exceptional mechanical properties.
View article: A database of molecular properties integrated in the Materials Project
A database of molecular properties integrated in the Materials Project Open
Advanced chemical research is increasingly reliant on large computed datasets of molecules and reactions to discover new functional molecules, understand chemical trends, train machine learning models, and more. To be of greatest use to th…
View article: A database of molecular properties integrated in the Materials Project
A database of molecular properties integrated in the Materials Project Open
Advanced chemical research is increasingly reliant on large computed datasets of molecules and reactions to discover new functional molecules, understand chemical trends, train machine learning models, and more. To be of greatest use to th…
View article: A database of molecular properties integrated in the Materials Project
A database of molecular properties integrated in the Materials Project Open
Advanced chemical research is increasingly reliant on large computed datasets of molecules and reactions to discover new functional molecules, understand chemical trends, train machine learning models, and more. To be of greatest use to th…
View article: An equivariant graph neural network for the elasticity tensors of all seven crystal systems
An equivariant graph neural network for the elasticity tensors of all seven crystal systems Open
The elasticity tensor that describes the elastic response of a material to external forces is among the most fundamental properties of materials. The availability of full elasticity tensors for inorganic crystalline compounds, however, is …
View article: Elasticity tensors of 10276 crystals from DFT computations
Elasticity tensors of 10276 crystals from DFT computations Open
Paper introducing this dataset Wen, M., Horton, M., Munro, J., Huck, P., & Persson, K. (2024). An equivariant graph neural network for the elasticity tensors of all seven crystal systems. Digital Discovery. DOI:https://doi.org/10.1039/D3DD…
View article: Elasticity tensors of 10276 crystals from DFT computations
Elasticity tensors of 10276 crystals from DFT computations Open
Paper introducing this dataset Wen, M., Horton, M., Munro, J., Huck, P., & Persson, K. (2024). An equivariant graph neural network for the elasticity tensors of all seven crystal systems. Digital Discovery. DOI:https://doi.org/10.1039/D3DD…
View article: Crystal Toolkit: A Web App Framework to Improve Usability and Accessibility of Materials Science Research Algorithms
Crystal Toolkit: A Web App Framework to Improve Usability and Accessibility of Materials Science Research Algorithms Open
Crystal Toolkit is an open source tool for viewing, analyzing and transforming crystal structures, molecules and other common forms of materials science data in an interactive way. It is intended to help beginners rapidly develop web-based…
View article: A database of molecular properties integrated in the Materials Project
A database of molecular properties integrated in the Materials Project Open
A new infrastructure for computed molecular properties, including a web app and API, is incorporated into the Materials Project, enabling the creation of a database currently containing over 170 000 molecules.
View article: A representation-independent electronic charge density database for crystalline materials
A representation-independent electronic charge density database for crystalline materials Open
In addition to being the core quantity in density-functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since i…
View article: High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration
High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration Open
With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of M…
View article: Data for the MOF Explorer on the Materials Project
Data for the MOF Explorer on the Materials Project Open
This is the data backend for the MOF Explorer application on the Materials Project and is built around the QMOF Database.
View article: High-Throughput Predictions of Metal–Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration
High-Throughput Predictions of Metal–Organic Framework Electronic Properties: Theoretical Challenges, Graph Neural Networks, and Data Exploration Open
With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for (opto)electronic and energy storage applications, we present a new dataset of predicted electronic structure properties for thousands of MOFs car…
View article: Science Capsule: Towards Sharing and Reproducibility of Scientific Workflows
Science Capsule: Towards Sharing and Reproducibility of Scientific Workflows Open
Workflows are increasingly processing large volumes of data from scientific instruments, experiments and sensors. These workflows often consist of complex data processing and analysis steps that might include a diverse ecosystem of tools a…
View article: A representation-independent electronic charge density database for crystalline materials
A representation-independent electronic charge density database for crystalline materials Open
In addition to being the core quantity in density functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since i…
View article: OPTIMADE, an API for exchanging materials data
OPTIMADE, an API for exchanging materials data Open
The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the…
View article: High-throughput Computational Study of Halide Double Perovskite Inorganic Compounds
High-throughput Computational Study of Halide Double Perovskite Inorganic Compounds Open
Double perovskite halides are a class of materials with diverse chemistries that are amenable to solution-based synthesis routes, and display a range of properties for a variety of potential applications. Here, starting from a consideratio…
View article: 2DMatPedia: An open computational database of two-dimensional materials from top-down and bottom-up approaches
2DMatPedia: An open computational database of two-dimensional materials from top-down and bottom-up approaches Open
Two-dimensional (2D) materials have been a hot research topic in the last decade, due to novel fundamental physics in the reduced dimension and appealing applications. Systematic discovery of functional 2D materials has been the focus of m…
View article: Materials design of perovskite solid solutions for thermochemical applications
Materials design of perovskite solid solutions for thermochemical applications Open
Perovskite solid solutions are screened both experimentally and through DFT to determine their redox properties for thermochemical applications.
View article: 2DMatPedia, An open computational database of two-dimensional materials from top-down and bottom-up approaches
2DMatPedia, An open computational database of two-dimensional materials from top-down and bottom-up approaches Open
We present a large dataset of 2D materials, with more than 6,000 monolayer structures, obtained from both top-down and bottom-up discovery procedures. First, we screened all bulk materials in the database of Materials Project for layered s…
View article: 2DMatPedia — An open computational database of two-dimensional materials from top-down and bottom-up approaches
2DMatPedia — An open computational database of two-dimensional materials from top-down and bottom-up approaches Open
We present a large dataset of 2D materials, with more than 6,000 monolayer structures, obtained from both top-down and bottom-up discovery procedures. First, we screened all bulk materials in the database of Materials Project for layered s…
View article: 2DMatPedia: An open computational database of two-dimensional materials from top-down and bottom-up approaches
2DMatPedia: An open computational database of two-dimensional materials from top-down and bottom-up approaches Open
We present a large dataset of 2D materials, with more than 6,000 monolayer structures, obtained from both top-down and bottom-up discovery procedures. First, we screened all bulk materials in the database of Materials Project for layered s…