Themed collection Machine learning and artificial neural networks: Celebrating the 2024 Nobel Prize in Physics

26 items
Open Access Perspective

Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials

With increased awareness of artificial intelligence-based algorithms coupled with the non-stop creation of material databases, artificial intelligence (AI) can facilitate fast development of high-performance electrochemical energy storage systems (EESSs).

Graphical abstract: Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials
From the themed collection: Energy Advances Recent Review Articles
Open Access Perspective

Neural network potentials for chemistry: concepts, applications and prospects

Artificial Neural Networks (NN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions.

Graphical abstract: Neural network potentials for chemistry: concepts, applications and prospects
Perspective

A review of recent advances and applications of machine learning in tribology

This review summarises recent advances in the use of machine learning for predicting friction and wear in tribological systems, material discovery, lubricant design and composite formulation. Potential future applications and areas for further research are also discussed.

Graphical abstract: A review of recent advances and applications of machine learning in tribology
Open Access Perspective

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets.

Graphical abstract: Natural product drug discovery in the artificial intelligence era
Open Access Review Article

Leveraging machine learning in porous media

Evaluating the advantages and limitations of applying machine learning for prediction and optimization in porous media, with applications in energy, environment, and subsurface studies.

Graphical abstract: Leveraging machine learning in porous media
Review Article

Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer

AI enabled imaging technology advances the precision, early detection, and personalizes treatment through analysis and interpretation of medical images.

Graphical abstract: Employing nano-enabled artificial intelligence (AI)-based smart technologies for prediction, screening, and detection of cancer
From the themed collection: Recent Review Articles
Open Access Review Article

Computing of neuromorphic materials: an emerging approach for bioengineering solutions

Machine learning techniques for the development of neuromorphic materials for bioengineering solutions by developing energy-efficient hardware, enhancing neuron models, and learning algorithms.

Graphical abstract: Computing of neuromorphic materials: an emerging approach for bioengineering solutions
Review Article

Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review

This review offers a guideline for selecting the ML-based inverse design method, considering data characteristics and design space size. It categorizes challenges and underscores the proper methods, with a focus on composites and its manufacturing.

Graphical abstract: Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review
Review Article

A review on the application of molecular descriptors and machine learning in polymer design

Molecular descriptors and machine learning are useful tools for extracting structure–property relationships from large, complex polymer data, and accelerating the design of novel polymers with tailored functionalities.

Graphical abstract: A review on the application of molecular descriptors and machine learning in polymer design
Open Access Review Article

Machine learning for soft and liquid molecular materials

This review discusses three types of soft matter and liquid molecular materials, namely hydrogels, liquid crystals and gas bubbles in liquids, which are explored with an emergent machine learning approach.

Graphical abstract: Machine learning for soft and liquid molecular materials
Review Article

Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges

This review compares machine learning approaches for property prediction of materials, optimization, and energy storage device health estimation. Current challenges and prospects for high-impact areas in machine learning research are highlighted.

Graphical abstract: Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges
Open Access Review Article

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

The cross-fertilisation between the listed disciplines with a long standing knowledge on the application of artificial intelligence protocols and electron microscopy for materials science can entail the next breakthroughs in the field.

Graphical abstract: Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
Review Article

Machine learning for design principles for single atom catalysts towards electrochemical reactions

Machine learning (ML) integrated density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of heterogeneous catalysts such as single atom catalysts (SACs) through the establishment of deep structure–activity relationships.

Graphical abstract: Machine learning for design principles for single atom catalysts towards electrochemical reactions
Open Access Review Article

Industrial data science – a review of machine learning applications for chemical and process industries

Understand and optimize industrial processes via machine learning and chemical engineering principles.

Graphical abstract: Industrial data science – a review of machine learning applications for chemical and process industries
Open Access Edge Article

Low-cost machine learning prediction of excited state properties of iridium-centered phosphors

Neural networks are used to predict iridium phosphor excited state properties at accuracy competitive with TDDFT, enabling high-throughput screening.

Graphical abstract: Low-cost machine learning prediction of excited state properties of iridium-centered phosphors
Open Access Edge Article

Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning

Recurrent neural networks as a machine learning tools are gaining popularity in chemical, physical and materials applications searching for viable methods in the structure and energetics analyses of systems ranging from crystals to soft matter.

Graphical abstract: Forecasting molecular dynamics energetics of polymers in solution from supervised machine learning
Open Access Paper

Using a supervised machine learning approach to predict water quality at the Gaza wastewater treatment plant

This paper presents the use of a machine learning approach to predict the performance of a Gaza wastewater treatment plant.

Graphical abstract: Using a supervised machine learning approach to predict water quality at the Gaza wastewater treatment plant
Open Access Paper

Accelerating materials discovery using integrated deep machine learning approaches

Our work introduces an innovative deep machine learning framework to significantly accelerate novel materials discovery, as demonstrated by its application to the La–Si–P system where new ternary and quaternary compounds were successfully identified.

Graphical abstract: Accelerating materials discovery using integrated deep machine learning approaches
Paper

Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network

We report the development of machine learning model for the calculation of carbon dioxide solubilities in deep solvent solvents. This model helps to predict and accelerate the development of carbon capture solvents with ideal experimental conditions.

Graphical abstract: Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network
Open Access Paper

Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models

Artificial neural networks trained on 23 density functional approximations (DFAs) from multiple rungs of “Jacob's ladder” enable the prediction of where each DFA has zero curvature for chemical discovery.

Graphical abstract: Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models
Open Access Paper

Prediction of total organic carbon and E. coli in rivers within the Milwaukee River basin using machine learning methods

Ensemble-hybrid ML models can explain and predict the variability in water quality parameters and living microorganism behavior in natural streams with satisfactory prediction accuracies based on specific physicochemical parameters.

Graphical abstract: Prediction of total organic carbon and E. coli in rivers within the Milwaukee River basin using machine learning methods
Open Access Paper

Data-driven approach for the prediction of mechanical properties of carbon fiber reinforced composites

Supervised machine learning models are trained on experimental data to predict the mechanical properties of composite materials. Results show that these techniques are reasonably accurate and generalizable.

Graphical abstract: Data-driven approach for the prediction of mechanical properties of carbon fiber reinforced composites
Open Access Paper

Sensitive rGO/MOF based electrochemical sensor for penta-chlorophenol detection: a novel artificial neural network (ANN) application

Reduced graphene oxide/metal organic framework based electrochemical sensor coupled with machine learning for sensitive detection of penta-chlorophenol.

Graphical abstract: Sensitive rGO/MOF based electrochemical sensor for penta-chlorophenol detection: a novel artificial neural network (ANN) application
Open Access Paper

A smartphone-interfaced, low-cost colorimetry biosensor for selective detection of bronchiectasis via an artificial neural network

Detection of bronchiectasis from exhaled breath.

Graphical abstract: A smartphone-interfaced, low-cost colorimetry biosensor for selective detection of bronchiectasis via an artificial neural network
Paper

Advanced artificial synaptic thin-film transistor based on doped potassium ions for neuromorphic computing via third-generation neural network

A novel technology of doping potassium ions to enhance the synaptic characteristics of synaptic thin-film transistors. The classifier of Spiking Neural Network with significant energy efficiency was successfully operated based on the proposed device.

Graphical abstract: Advanced artificial synaptic thin-film transistor based on doped potassium ions for neuromorphic computing via third-generation neural network
Open Access Paper

Machine learning enhanced spectroscopic analysis: towards autonomous chemical mixture characterization for rapid process optimization

A supervised machine learning algorithm is developed to determine the concentrations of chemical species in multicomponent solutions from their Fourier transform infrared (FTIR) spectra.

Graphical abstract: Machine learning enhanced spectroscopic analysis: towards autonomous chemical mixture characterization for rapid process optimization
26 items

About this collection

This cross-journal collection celebrates the 2024 Nobel Prize in Physics by bringing together research published on the use of machine learning and artificial neural networks to facilitate new and important discoveries. Through this important work, machines can now mimic functions such as memory and learning, enabling them to provide crucial assistance to humans for purposes such as uncovering new functional materials. This collection highlights work on the importance of artificial neural networks in materials science, nanoscience, physical chemistry, and more.

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