Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …
traditional research paradigms in the era of artificial intelligence and automation. An …
Advances in decomposing complex metabolite mixtures using substructure-and network-based computational metabolomics approaches
Covering: up to the end of 2020 Recently introduced computational metabolome mining
tools have started to positively impact the chemical and biological interpretation of …
tools have started to positively impact the chemical and biological interpretation of …
[HTML][HTML] Perspective on integrating machine learning into computational chemistry and materials science
Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …
electronic structure theory and molecular simulation. In particular, ML has become firmly …
Fully automated unconstrained analysis of high-resolution mass spectrometry data with machine learning
Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the
analysis of complex mixtures, which is vital for materials science, life sciences fields such as …
analysis of complex mixtures, which is vital for materials science, life sciences fields such as …
Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis
Artificial intelligence (AI) is an emerging technology with great potential, and its robust
calculation and analysis capabilities are unmatched by traditional calculation tools. With the …
calculation and analysis capabilities are unmatched by traditional calculation tools. With the …
Functional group identification for FTIR spectra using image-based machine learning models
Fourier transform infrared spectroscopy (FTIR) is a ubiquitous spectroscopic technique.
Spectral interpretation is a time-consuming process, but it yields important information about …
Spectral interpretation is a time-consuming process, but it yields important information about …
Chemputation and the standardization of chemical informatics
The explosion in the use of machine learning for automated chemical reaction optimization
is gathering pace. However, the lack of a standard architecture that connects the concept of …
is gathering pace. However, the lack of a standard architecture that connects the concept of …
[HTML][HTML] Deep metabolome: Applications of deep learning in metabolomics
In the past few years, deep learning has been successfully applied to various omics data.
However, the applications of deep learning in metabolomics are still relatively low compared …
However, the applications of deep learning in metabolomics are still relatively low compared …
A universal and accurate method for easily identifying components in Raman spectroscopy based on deep learning
X Fan, Y Wang, C Yu, Y Lv, H Zhang, Q Yang… - Analytical …, 2023 - ACS Publications
Raman spectroscopy has been widely used to provide the structural fingerprint for molecular
identification. Due to interference from coexisting components, noise, baseline, and …
identification. Due to interference from coexisting components, noise, baseline, and …
Recent developments in machine learning for mass spectrometry
Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich
history with several modern MS-based applications using statistical and chemometric …
history with several modern MS-based applications using statistical and chemometric …