Big-data science in porous materials: materials genomics and machine learning
By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
Toward autonomous design and synthesis of novel inorganic materials
Autonomous experimentation driven by artificial intelligence (AI) provides an exciting
opportunity to revolutionize inorganic materials discovery and development. Herein, we …
opportunity to revolutionize inorganic materials discovery and development. Herein, we …
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming
steps in the development cycle of novel thin-film materials. We propose a machine learning …
steps in the development cycle of novel thin-film materials. We propose a machine learning …
Symmetry prediction and knowledge discovery from X-ray diffraction patterns using an interpretable machine learning approach
Determination of crystal system and space group in the initial stages of crystal structure
analysis forms a bottleneck in material science workflow that often requires manual tuning …
analysis forms a bottleneck in material science workflow that often requires manual tuning …
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design
J Benavides-Hernández, F Dumeignil - ACS Catalysis, 2024 - ACS Publications
This review paper delves into synergistic integration of artificial intelligence (AI) and
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
machine learning (ML) with high-throughput experimentation (HTE) in the field of …
Probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra
Autonomous synthesis and characterization of inorganic materials require the automatic and
accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep …
accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep …
Perovskite or Not Perovskite? A Deep‐Learning Approach to Automatically Identify New Hybrid Perovskites from X‐ray Diffraction Patterns
F Massuyeau, T Broux, F Coulet… - Advanced …, 2022 - Wiley Online Library
Determining the crystal structure is a critical step in the discovery of new functional materials.
This process is time consuming and requires extensive human expertise in crystallography …
This process is time consuming and requires extensive human expertise in crystallography …
Rapid identification of X-ray diffraction patterns based on very limited data by interpretable convolutional neural networks
Large volumes of data from material characterizations call for rapid and automatic data
analysis to accelerate materials discovery. Herein, we report a convolutional neural network …
analysis to accelerate materials discovery. Herein, we report a convolutional neural network …
Automated classification of big X-ray diffraction data using deep learning models
In current in situ X-ray diffraction (XRD) techniques, data generation surpasses human
analytical capabilities, potentially leading to the loss of insights. Automated techniques …
analytical capabilities, potentially leading to the loss of insights. Automated techniques …
X-ray diffraction data analysis by machine learning methods—a review
X-ray diffraction (XRD) is a proven, powerful technique for determining the phase
composition, structure, and microstructural features of crystalline materials. The use of …
composition, structure, and microstructural features of crystalline materials. The use of …