Themed collection Machine Learning and Artificial Intelligence: A cross-journal collection

105 items - Showing page 1 of 2
Highlight

Machine learning integrated photocatalysis: progress and challenges

By integrating machine learning with automation and robots, accelerated discovery of photocatalysts in the future could be envisioned.

Graphical abstract: Machine learning integrated photocatalysis: progress and challenges
Highlight

Modern machine learning for tackling inverse problems in chemistry: molecular design to realization

Many of the tasks in the molecular design pipeline can be modelled as inverse problems. This highlight focuses on recent developments in modern machine learning methods which can be used to tackle those inverse problems.

Graphical abstract: Modern machine learning for tackling inverse problems in chemistry: molecular design to realization
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

Machine learning potential era of zeolite simulation

The machine learning atomic simulation will usher the research of zeolite, as other complex materials, into a new era featuring the easy access to zeolite functionalities predicted from theory.

Graphical abstract: Machine learning potential era of zeolite simulation
Perspective

Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective

This article provides a vision on how to accelerate the production of chemicals and fuels from biomass feedstocks using an integrated framework of data mining, retrosynthesis, lab automation, and process systems engineering.

Graphical abstract: Accelerating manufacturing for biomass conversion via integrated process and bench digitalization: a perspective
Open Access Feature Article

High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering

Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conversion and biopharma.

Graphical abstract: High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering
Open Access Review Article

Machine learning in energy chemistry: introduction, challenges and perspectives

This review explores machine learning's role in energy chemistry, spanning organic photovoltaics, perovskites, catalysis, and batteries, highlighting its potential to accelerate eco-friendly, sustainable energy development.

Graphical abstract: Machine learning in energy chemistry: introduction, challenges and perspectives
From the themed collection: Energy Advances Recent Review Articles
Review Article

Memristor-based neural networks: a bridge from device to artificial intelligence

This paper reviews the research progress in memristor-based neural networks and puts forward future development trends.

Graphical abstract: Memristor-based neural networks: a bridge from device to artificial intelligence
From the themed collection: Nanoscale and Nanoscale Horizons: Nanodevices
Open Access Review Article

Machine learning-inspired battery material innovation

Data-driven machine learning is a proven technique for battery material discovery and enables the development of sustainable next-generation batteries.

Graphical abstract: Machine learning-inspired battery material innovation
From the themed collection: Energy Advances Recent Review Articles
Review Article

Data-driven design of electrocatalysts: principle, progress, and perspective

In this review, we focus on the systematic construction of the data-driven electrocatalyst design framework and discuss its principles, current challenges, and opportunities.

Graphical abstract: Data-driven design of electrocatalysts: principle, progress, and perspective
Open Access Review Article

How machine learning can accelerate electrocatalysis discovery and optimization

Machine learning can accelerate the process of electrocatalyst discovery and optimization, especially when incorporated into a closed-loop approach with autonomous laboratories. This review highlights the recent progress and challenges in this field.

Graphical abstract: How machine learning can accelerate electrocatalysis discovery and optimization
Open Access Review Article

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

This review outlines several organic chemistry tasks for which predictive machine learning models have been and can be applied.

Graphical abstract: Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery
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
Open Access Review Article

Quantum machine learning for chemistry and physics

Quantum variants of machine learning algorithms are discussed with emphasis on methodology, learning techniques and applications in broad and distinct domains of chemical physics.

Graphical abstract: Quantum machine learning for chemistry and physics
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 Review Article

Recent trends in computational tools and data-driven modeling for advanced materials

The paradigm of advanced materials has grown exponentially over the last decade, with their new dimensions including digital design, dynamics, and functions.

Graphical abstract: Recent trends in computational tools and data-driven modeling for advanced materials
Review Article

Intelligent control of nanoparticle synthesis through machine learning

Machine learning-assisted synthesis of nanoparticles.

Graphical abstract: Intelligent control of nanoparticle synthesis through machine learning
Open Access Review Article

Emerging cold plasma treatment and machine learning prospects for seed priming: a step towards sustainable food production

The perspective of machine learning for modeling plasma treatment parameters in agriculture for the development of synergistic protocols for different types of seed priming.

Graphical abstract: Emerging cold plasma treatment and machine learning prospects for seed priming: a step towards sustainable food production
Tutorial Review

Understanding, discovery, and synthesis of 2D materials enabled by machine learning

Machine learning (ML) is becoming an effective tool for studying 2D materials.

Graphical abstract: Understanding, discovery, and synthesis of 2D materials enabled by machine learning
Critical Review

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

Within the agri-food sector, e-noses, e-tongues, and e-eyes generate interest. This review delves into their principles, challenges, and data processing methods, featuring case studies that contribute to the advancement of e-sensing technologies.

Graphical abstract: Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review
Critical Review

Machine learning for algal biofuels: a critical review and perspective for the future

Machine learning applications in microalgae biofuel production are reviewed; the current states and major trends in research as well as the challenges to overcome are identified.

Graphical abstract: Machine learning for algal biofuels: a critical review and perspective for the future
From the themed collection: 2023 Green Chemistry Reviews
Open Access Critical Review

Machine learning for microfluidic design and control

In this review article, we surveyed the applications of machine learning in microfluidic design and microfluidic control.

Graphical abstract: Machine learning for microfluidic design and control
Communication

Discovery of all-inorganic lead-free perovskites with high photovoltaic performance via ensemble machine learning

A multi-step and multi-stage high-throughput materials search via ensemble machine learning is developed to carefully and comprehensively screen nearly 12 million Image ID:d3mh00967j-t1.gif all-inorganic perovskites with potential high photovoltaic performance.

Graphical abstract: Discovery of all-inorganic lead-free perovskites with high photovoltaic performance via ensemble machine learning
Open Access Communication

Predicting and analyzing organic reaction pathways by combining machine learning and reaction network approaches

By training 50 fundamental organic reactions, the learning model predicted the products and pathways of 35 test reactions. The model identified the key fragment structures of the reaction intermediates.

Graphical abstract: Predicting and analyzing organic reaction pathways by combining machine learning and reaction network approaches
Communication

Improved environmental chemistry property prediction of molecules with graph machine learning

Rapid prediction of environmental chemistry properties is critical for the green and sustainable development of the chemical industry and drug discovery.

Graphical abstract: Improved environmental chemistry property prediction of molecules with graph machine learning
Communication

Learning molecular dynamics: predicting the dynamics of glasses by a machine learning simulator

A graph-based machine learning model is built to predict atom dynamics from their static structure, which, in turn, unveils the predictive power of static structure in dynamical evolution of disordered phases.

Graphical abstract: Learning molecular dynamics: predicting the dynamics of glasses by a machine learning simulator
From the themed collection: Materials Horizons HOT Papers
Open Access Communication

Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution

Schematic of our bidirectional, ML-empowered approach incorporating plasmonic featurization for rapid (<30 s) and accurate determination of the size and size distribution of gold nanosphere (Au NSs) ensembles in real samples.

Graphical abstract: Incorporating plasmonic featurization with machine learning to achieve accurate and bidirectional prediction of nanoparticle size and size distribution
Communication

Generative machine learning algorithm for lattice structures with superior mechanical properties

We present a hybrid neural network and genetic optimization adaptive method incorporating Bézier curves to consider the large design space of lattice structures with superior mechanical properties.

Graphical abstract: Generative machine learning algorithm for lattice structures with superior mechanical properties
Open Access Edge Article

Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules

Fine-tuned GPT-3 shows robust performance for the prediction of electronic and functional properties for organic molecules, with resilience to information loss and noise.

Graphical abstract: Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules
Open Access Edge Article

Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation

The dominant binding mode of the QUB-00006-Int-07 main protease inhibitor during absolute binding free energy simulations.

Graphical abstract: Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation
Open Access Edge Article

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

Contrastive pretraining of chemical reactions by matching augmented reaction representations to improve machine learning performance on small reaction datasets.

Graphical abstract: Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining
Open Access Paper

A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics

An automated flow chemistry platform was designed to collect data for a lithium-halogen exchange reaction. The data was used to train a Bayesian multi-objective optimization algorithm to optimize the process parameters and build process knowledge.

Graphical abstract: A machine learning-enabled process optimization of ultra-fast flow chemistry with multiple reaction metrics
Open Access Paper

Discrimination of mycoplasma infection using machine learning models trained on autofluorescence signatures of host cells

Cellular autofluorescence signatures, considered to represent the physiological state of individual cells, allow us to discriminate mycoplasma infection using machine learning models.

Graphical abstract: Discrimination of mycoplasma infection using machine learning models trained on autofluorescence signatures of host cells
Open Access Paper

Modelling and predicting liquid chromatography retention time for PFAS with no-code machine learning

Machine learning is increasingly popular and promising in environmental science due to its potential in solving various environmental problems, particularly with simple code-free tools.

Graphical abstract: Modelling and predicting liquid chromatography retention time for PFAS with no-code machine learning
Open Access Paper

Classification of chemically modified red blood cells in microflow using machine learning video analysis

We classify native and chemically modified RBCs with an AI based video classifier at high accuracy (>90%). We use chemicals to mimic aspects of typical RBC disorders. This enables a label-free categorization, based on cell shape and flow dynamics.

Graphical abstract: Classification of chemically modified red blood cells in microflow using machine learning video analysis
Paper

Semi-supervised machine learning approach for reaction stoichiometry and kinetic model identification using spectral data from flow reactors

The semi-supervised machine learning approach is an integrated calibration-free modelling framework for identifying reaction systems from spectral data using minimal prior information and it is validated with experimental data obtained in a micro-reactor.

Graphical abstract: Semi-supervised machine learning approach for reaction stoichiometry and kinetic model identification using spectral data from flow reactors
Open Access Paper

An automated and intelligent microfluidic platform for microalgae detection and monitoring

An automated and intelligent microfluidic platform (AIMP), which offers automated system control, intelligent data analysis, and user interaction was developed to provide a cost-effective and portable solution for detecting and monitoring microalgae.

Graphical abstract: An automated and intelligent microfluidic platform for microalgae detection and monitoring
Open Access Paper

Unlocking the predictive power of quantum-inspired representations for intermolecular properties in machine learning

New MODA descriptor, a quantum-inspired representation enhancing ML predictions of molecular properties. By using a wave-function guess, MODA captures electronic structure intricacies to excel in intermolecular property predictions.

Graphical abstract: Unlocking the predictive power of quantum-inspired representations for intermolecular properties in machine learning
Paper

Predicting the pair correlation functions of silicate and borosilicate glasses using machine learning

We report a machine learning method for predicting the atom pair correlation functions of a class of glassy materials.

Graphical abstract: Predicting the pair correlation functions of silicate and borosilicate glasses using machine learning
Open Access Paper

Machine learning prediction of self-assembly and analysis of molecular structure dependence on the critical packing parameter

We used machine learning to predict the self-assembly structures of amphiphilic molecules and analyzed the physical factors affecting their morphologies.

Graphical abstract: Machine learning prediction of self-assembly and analysis of molecular structure dependence on the critical packing parameter
From the themed collection: MSDE Recent HOT Articles
Open Access Paper

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability.

Graphical abstract: Advancing energy storage through solubility prediction: leveraging the potential of deep learning
Open Access Paper

Predicting power plant emissions using public data and machine learning

We show that combining a variety of public datasets and non-linear machine learning models can predict emissions from electric generating units at good accuracy without any proprietary information.

Graphical abstract: Predicting power plant emissions using public data and machine learning
Paper

Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses

The data-driven machine learning approach has greatly improved the predictive accuracy of Tg and Dmax values. The governing rules for GFA have been successfully established through feature significance analysis.

Graphical abstract: Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses
Paper

Unlocking the performance of ternary metal (hydro)oxide amorphous catalysts via data-driven active-site engineering

A machine-learning methodology was applied to unveil the structure–property relationships of the fabricated ternary Ni, Fe, and Co amorphous oxygen evolution catalyst, showcasing remarkable performance and stability via corrosion engineering.

Graphical abstract: Unlocking the performance of ternary metal (hydro)oxide amorphous catalysts via data-driven active-site engineering
From the themed collection: Spotlight on Women in Energy
Paper

Screening of steam-reforming catalysts using unsupervised machine learning

In this article, a bidirectional clustering model proposed for methanol-reforming catalysts demonstrates excellent mathematical performance and is of significance for the discovery of methanol-reforming catalysts.

Graphical abstract: Screening of steam-reforming catalysts using unsupervised machine learning
Paper

Multiplex SERS detection of polycyclic aromatic hydrocarbon (PAH) pollutants in water samples using gold nanostars and machine learning analysis

Polycyclic aromatic hydrocarbons (PAHs) have attracted a lot of environmental concern because of their carcinogenic and mutagenic properties, and the fact they can easily contaminate natural resources such as drinking water and river water.

Graphical abstract: Multiplex SERS detection of polycyclic aromatic hydrocarbon (PAH) pollutants in water samples using gold nanostars and machine learning analysis
Paper

Machine learning predictions of diffusion in bulk and confined ionic liquids using simple descriptors

Ionic liquids have many intriguing properties and widespread applications such as separations and energy storage.

Graphical abstract: Machine learning predictions of diffusion in bulk and confined ionic liquids using simple descriptors
From the themed collection: MSDE Recent HOT Articles
Paper

Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae

We experimentally justify the advantages of jumping on the deep learning trend for image-activated budding yeast sorting and validate its applicability towards morphology-based yeast mutant screening.

Graphical abstract: Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae
Open Access Paper

Machine-learning-aided multiplexed nanoplasmonic biosensor for COVID-19 population immunity profiling

Vaccination and infection rates against variants of COVID-19 in Dane County, WI were determined from low-volume human sera/plasma samples with machine-learning aided nanoplasmonic biosensor. The results agree with the official epidemiological data.

Graphical abstract: Machine-learning-aided multiplexed nanoplasmonic biosensor for COVID-19 population immunity profiling
Open Access Paper

Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane

Unveiling current issues in the investigation of highly-active heterogeneous catalysts using machine learning engineering techniques was discussed in the case of oxidative coupling of methane with support vector regression and Bayesian optimization.

Graphical abstract: Leveraging machine learning engineering to uncover insights into heterogeneous catalyst design for oxidative coupling of methane
Open Access Paper

A machine learning-based nano-photocatalyst module for accelerating the design of Bi2WO6/MIL-53(Al) nanocomposites with enhanced photocatalytic activity

A machine learning-based nano-photocatalyst module for accelerating the design of Bi2WO6/MIL-53(Al) nanocomposites was constructed by four steps. An online web service was established to quickly predict the photocatalytic activity of Bi2WO6/MIL-53(Al).

Graphical abstract: A machine learning-based nano-photocatalyst module for accelerating the design of Bi2WO6/MIL-53(Al) nanocomposites with enhanced photocatalytic activity
Paper

Towards Pareto optimal high entropy hydrides via data-driven materials discovery

Data-driven predictions of metal hydride thermodynamic properties elucidate the Pareto optimal front of high entropy alloy candidates for hydrogen storage.

Graphical abstract: Towards Pareto optimal high entropy hydrides via data-driven materials discovery
Paper

nanoNET: machine learning platform for predicting nanoparticles distribution in a polymer matrix

We report an ML pipeline that predicts the nanoparticle–nanoparticle pair correlation function of a polymer nanocomposite.

Graphical abstract: nanoNET: machine learning platform for predicting nanoparticles distribution in a polymer matrix
Open Access Paper

Leveraging machine learning to consolidate the diversity in experimental results of perovskite solar cells

Application of a machine learning approach to device design. Starting from database analysis followed by a dataset creation based on those insights. Data preprocessing is done to extract features for ML prediction and design new PSCs.

Graphical abstract: Leveraging machine learning to consolidate the diversity in experimental results of perovskite solar cells
Paper

Machine learning-assisted screening of effective passivation materials for P–I–N type perovskite solar cells

The effective passivation material (ITIC) for P–I–N type perovskite solar cells is selected by machine learning. In the verification experiment, the defect density of the perovskite layer is significantly decreased after treatment with ITIC.

Graphical abstract: Machine learning-assisted screening of effective passivation materials for P–I–N type perovskite solar cells
Open Access Paper

Bayesian machine learning optimization of microneedle design for biological fluid sampling

The deployment of microneedles in biological fluid sampling and drug delivery is an emerging field in biotechnology, which contributes greatly to minimally-invasive methods in medicine.

Graphical abstract: Bayesian machine learning optimization of microneedle design for biological fluid sampling
Open Access Paper

Machine learning approaches to the prediction of powder flow behaviour of pharmaceutical materials from physical properties

A Machine Learning (ML) approach was proposed to optimize the manufacturing-route selection from the physical particle properties of a pharmaceutical material.

Graphical abstract: Machine learning approaches to the prediction of powder flow behaviour of pharmaceutical materials from physical properties
Open Access Paper

Development of prediction model for cloud point of thermo-responsive polymers by experiment-oriented materials informatics

A prediction model for cloud point was built by a combination of materials informatics and chemical insight.

Graphical abstract: Development of prediction model for cloud point of thermo-responsive polymers by experiment-oriented materials informatics
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
Paper

Design of an ultra-broadband terahertz absorber based on a patterned graphene metasurface with machine learning

Utilizes machine learning to propose an absorption bandwidth and structural parameters prediction approach for the design of patterned graphene metasurface absorber, which provides a new direction for the precision design of optical devices.

Graphical abstract: Design of an ultra-broadband terahertz absorber based on a patterned graphene metasurface with machine learning
Open Access Paper

High-throughput computational workflow for ligand discovery in catalysis with the CSD

A novel semi-automated, high-throughput computational workflow for ligand/catalyst discovery based on the Cambridge Structural Database is reported.

Graphical abstract: High-throughput computational workflow for ligand discovery in catalysis with the CSD
Paper

Easy and fast prediction of green solvents for small molecule donor-based organic solar cells through machine learning

A fast machine learning based framework is introduced for the prediction of solubility parameters and selection of green solvents for small molecular donor-based organic solar cells.

Graphical abstract: Easy and fast prediction of green solvents for small molecule donor-based organic solar cells through machine learning
Open Access Paper

Identification of fluorescently-barcoded nanoparticles using machine learning

We introduce a machine-learning-assisted workflow to write, read, and classify dye-loaded PLGA–PEG nanoparticles at a single-particle level.

Graphical abstract: Identification of fluorescently-barcoded nanoparticles using machine learning
Paper

Fusing a machine learning strategy with density functional theory to hasten the discovery of 2D MXene-based catalysts for hydrogen generation

We establish a robust and broadly applicable multistep workflow using machine learning algorithms to construct well-trained data-driven models for predicting the hydrogen evolution reaction activity of 4500 MM′XT2-type MXenes.

Graphical abstract: Fusing a machine learning strategy with density functional theory to hasten the discovery of 2D MXene-based catalysts for hydrogen generation
Open Access Paper

Classical and quantum machine learning applications in spintronics

Prediction of physical observables with machine learning for spintronic and molecular devices.

Graphical abstract: Classical and quantum machine learning applications in spintronics
Open Access Paper

Rational design of MoS2-supported Cu single-atom catalysts by machine learning potential for enhanced peroxidase-like activity

Machine learning motivated Cu@MoS2 catalysts design for enhanced peroxidase-like activity.

Graphical abstract: Rational design of MoS2-supported Cu single-atom catalysts by machine learning potential for enhanced peroxidase-like activity
Paper

A Predictive machine-learning model for propagation rate coefficients in radical polymerization

Using ridge regression, the propagation rate coefficients for radical polymerization are correlated with basic molecular properties.

Graphical abstract: A Predictive machine-learning model for propagation rate coefficients in radical polymerization
Open Access Paper

Machine learning assisted binary alloy catalyst design for the electroreduction of CO2 to C2 products

CO2RR binary alloy catalyst design insight gained through density functional theory and machine learning with a focus on COCOH adsorption energy.

Graphical abstract: Machine learning assisted binary alloy catalyst design for the electroreduction of CO2 to C2 products
From the themed collection: Energy Advances: Highlight USA & Canada
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
105 items - Showing page 1 of 2

About this collection

Although relatively new technologies, the rapid development of machine learning (ML) and artificial intelligence (AI) has the potential to revolutionise the way in which we conduct chemistry research.

Demonstrating applications across the chemical sciences, this cross-journal collection highlights recent ML & AI work from across the RSC portfolio. From use in predicting catalyst behaviour, to interpreting analytical data, these papers showcase the cutting edge of this exciting area.


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