Soft materials nanoarchitectonics: liquid crystals, polymers, gels, biomaterials, and others

• Katsuhiko Ariga

Beilstein J. Nanotechnol. 2025, 16, 1025–1067, doi:10.3762/bjnano.16.77

Time-resolved probing of laser-induced nanostructuring processes in liquids

• Maximilian Spellauge,
• David Redka,
• Mianzhen Mo,
• Changyong Song,
• Heinz Paul Huber and
• Anton Plech

Beilstein J. Nanotechnol. 2025, 16, 968–1002, doi:10.3762/bjnano.16.74

• substantial production of single-pulse data, which awaits prompt online analysis to facilitate practical implementation of the investigation. Such streamlined management of large data sets can be supported by implementing machine learning algorithms. We anticipate a more pronounced utilization of deep

Recent advances in photothermal nanomaterials for ophthalmic applications

• Jiayuan Zhuang,
• Linhui Jia,
• Chenghao Li,
• Rui Yang,
• Jiapeng Wang,
• Wen-an Wang,
• Heng Zhou and
• Xiangxia Luo

Beilstein J. Nanotechnol. 2025, 16, 195–215, doi:10.3762/bjnano.16.16

• machine learning to accelerate material development, exploring more types of photothermal nanomaterials, exploring more diverse composite photothermal material formulations, utilizing advanced characterization techniques, and collaborating with multidisciplinary researchers, more advanced and effective

• development cycles and high cost. The rapid advancement in AI and machine learning is revolutionizing material design and screening processes [219]. Machine learning has achieved significant success in predicting various material properties, including morphology, toxicity, photothermal characteristics

• , synthesis methods, and activity [220]. Developing machine learning models for ophthalmic photothermal nanomaterials will expedite the development of high-performance target materials and alleviate the burden of extensive experimental work. Advanced characterization tools, theoretical simulations, and high

A review of metal-organic frameworks and polymers in mixed matrix membranes for CO₂ capture

• Charlotte Skjold Qvist Christensen,
• Nicholas Hansen,
• Mahboubeh Motadayen,
• Nina Lock,
• Martin Lahn Henriksen and
• Jonathan Quinson

Beilstein J. Nanotechnol. 2025, 16, 155–186, doi:10.3762/bjnano.16.14

• (1) mechanisms of CO2 sorption in MOFs, (2) considerations related to the integration of MOFs in MMMs, (3) CO2 capture performance of MOF-based MMMs, (4) advancements in MOF-based MMM materials design through machine learning, and (5) considerations for the implementation of MOF-based MMMs in large

The round-robin approach applied to nanoinformatics: consensus prediction of nanomaterials zeta potential

• Dimitra-Danai Varsou,
• Arkaprava Banerjee,
• Joyita Roy,
• Kunal Roy,
• Giannis Savvas,
• Haralambos Sarimveis,
• Ewelina Wyrzykowska,
• Mateusz Balicki,
• Tomasz Puzyn,
• Georgia Melagraki,
• Iseult Lynch and
• Antreas Afantitis

Beilstein J. Nanotechnol. 2024, 15, 1536–1553, doi:10.3762/bjnano.15.121

• underlying models. Using a publicly available dataset, four research groups (NovaMechanics Ltd. (NovaM)-Cyprus, National Technical University of Athens (NTUA)-Greece, QSAR Lab Ltd.-Poland, and DTC Lab-India) built five distinct machine learning (ML) models for the in silico prediction of the zeta potential

• ]. Another example of an IATA is the combination of predictions from two or more individual models under a consensus framework. Consensus models combine outputs from several individual models built upon different sets of descriptors and/or machine learning (ML) algorithms, leading to more trustworthy results

Integrating high-performance computing, machine learning, data management workflows, and infrastructures for multiscale simulations and nanomaterials technologies

• Fabio Le Piane,
• Mario Vozza,
• Matteo Baldoni and
• Francesco Mercuri

Beilstein J. Nanotechnol. 2024, 15, 1498–1521, doi:10.3762/bjnano.15.119

• , Turin, Corso Duca degli Abruzzi 24, Italy 10.3762/bjnano.15.119 Abstract This perspective article explores the convergence of advanced digital technologies, including high-performance computing (HPC), artificial intelligence, machine learning, and sophisticated data management workflows. The primary

• digital methodologies in advanced research. Keywords: artificial intelligence; high-performance computing; HPC; machine learning; materials modelling; multiscale modelling; nanomaterials; semantic data management; Introduction Digital technologies have ushered in a new era of materials science, enabling

• time and length scales, from atomic and molecular-level interactions to the macroscale, that govern the structural, mechanical, and thermal properties of materials [4][5]. More recently, data-driven approaches, such as machine learning (ML) and artificial intelligence (AI), are revolutionizing

Polymer lipid hybrid nanoparticles for phytochemical delivery: challenges, progress, and future prospects

• Iqra Rahat,
• Pooja Yadav,
• Aditi Singhal,
• Mohammad Fareed,
• Jaganathan Raja Purushothaman,
• Mohammed Aslam,
• Raju Balaji,
• Sonali Patil-Shinde and
• Md. Rizwanullah

Beilstein J. Nanotechnol. 2024, 15, 1473–1497, doi:10.3762/bjnano.15.118

Recent progress on field-effect transistor-based biosensors: device perspective

• Billel Smaani,
• Fares Nafa,
• Mohamed Salah Benlatrech,
• Ismahan Mahdi,
• Hamza Akroum,
• Mohamed walid Azizi,
• Khaled Harrar and
• Sayan Kanungo

Beilstein J. Nanotechnol. 2024, 15, 977–994, doi:10.3762/bjnano.15.80

• variants for designing highly sensitive FET biosensors. However, there are still several possibilities that can be recommended for future work, such as implementation of artificial intelligence (AI) and machine learning (ML) algorithms for 3D and 2D FET-based biosensors. In this regard, the ML-based neural

Identification of structural features of surface modifiers in engineered nanostructured metal oxides regarding cell uptake through ML-based classification

• Indrasis Dasgupta,
• Totan Das,
• Biplab Das and
• Shovanlal Gayen

Beilstein J. Nanotechnol. 2024, 15, 909–924, doi:10.3762/bjnano.15.75

• governing the cellular uptake of ENMOs. The study will direct scientists in the design of ENMOs of higher cellular uptake efficiency for better therapeutic response. Keywords: Bayesian classification; cellular uptake; machine learning; nanoparticles (NPs); Introduction In recent years, the rapid

• uptake in the PaCa2 cell line [22][23][24][25][26][27]. In the current study, we have performed a distinctive approach by developing nano-QSAR machine learning-based classification models that encompass not only the cellular uptake data of the PaCa2 cell line but also the two additional cell lines HUVEC

• using the fivefold cross-validation procedure. Additionally, the model’s quality was evaluated by looking at the receiver operating characteristic (ROC) plot as well as specificity, sensitivity, and accuracy values [40][41][42]. Development of other machine learning models Calculation of descriptors and

On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems

• Shan He,
• Julen Segura Abarrategi,
• Harbil Bediaga,
• Sonia Arrasate and
• Humberto González-Díaz

Beilstein J. Nanotechnol. 2024, 15, 535–555, doi:10.3762/bjnano.15.47

• and NDD compounds, as well as the various assays involved. Artificial intelligence/machine learning (AI/ML) algorithms have the potential to accelerate this process by predicting the most promising NDD and NP candidates for assaying. Nevertheless, the relatively limited amount of reported data on

• N2D3S activity compared to assayed NDDs makes AI/ML analysis challenging. In this work, the IFPTML technique, which combines information fusion (IF), perturbation theory (PT), and machine learning (ML), was employed to address this challenge. Initially, we conducted the fusion into a unified dataset

• serve as valuable tools in the design of drug delivery systems for neurosciences. Keywords: artificial neural network (ANN); linear discriminant analysis (LDA); machine learning; nanoparticle; neurodegenerative diseases; Introduction Over time, there has been a significant shift in global dietary

Multiscale modelling of biomolecular corona formation on metallic surfaces

• Parinaz Mosaddeghi Amini,
• Ian Rouse,
• Julia Subbotina and
• Vladimir Lobaskin

Beilstein J. Nanotechnol. 2024, 15, 215–229, doi:10.3762/bjnano.15.21

• proteins using the same fragment-based approach. To avoid the need to run a time-consuming parameterization protocol based on metadynamics simulations, we produce PMFs for the glucose bead using a machine-learning technique (PMFPredictor) trained on previous metadynamics results [38]. For the lactose

• decomposition, generating PMFs via traditional or machine-learning approaches, and constructing a coarse-grained representation for input to UA. To simplify this procedure for more complex molecules, we have developed a Python script (MolToFragments.py) employing RDKit [46] to automate splitting larger

Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO₂ with periodic table descriptors using machine learning approaches

• Joyita Roy,
• Souvik Pore and
• Kunal Roy

Beilstein J. Nanotechnol. 2023, 14, 939–950, doi:10.3762/bjnano.14.77

• nanomaterials based on structure similarities with known substances. Materials with similar structures are likely to produce similar toxicity through comparable mechanisms. The development of machine learning (ML) approaches, such as artificial neural networks (ANNs), decision trees, logistic regression (LR

• model depending on the previous one. In the bagging algorithm, replica data sets are generated that minimize prediction variance in machine learning. An iterative algorithm performs a series of repeated steps to gradually improve the model’s performance or to optimize a specific parameter. The

• adjusting its parameters to minimize a specific cost or error function. These algorithms play a crucial role in training machine learning models and are fundamental to many optimization and learning techniques. Fine-tuning the model parameters through iterations helps to improve the model’s performance and

A wearable nanoscale heart sound sensor based on P(VDF-TrFE)/ZnO/GR and its application in cardiac disease detection

• Yi Luo,
• Jian Liu,
• Jiachang Zhang,
• Yu Xiao,
• Ying Wu and
• Zhidong Zhao

Beilstein J. Nanotechnol. 2023, 14, 819–833, doi:10.3762/bjnano.14.67

• energy harvesting [15]. Applying machine learning classification algorithms in the domain of human physiological signal detection is presently a prominent area of research. A notable study by R. Guo et al. [16] successfully integrated deep learning techniques with frictional hydrogel sensors to achieve

• corresponding results of the piezoelectric coefficient are presented in Figure 14b. KNN heart sound classification recognition algorithm The KNN classification algorithm is widely used in machine learning, and its fundamental classification idea is that a sample in a feature space belongs to the same category

• represents the seven optimal bases that have been selected. In this experiment, the “Classification Learner toolbox” in MATLAB was used to train a heart sound classification model. This toolbox allowed us to explore supervised machine learning by selecting various classifiers, exploring data, selecting

Transferability of interatomic potentials for silicene

• Marcin Maździarz

Beilstein J. Nanotechnol. 2023, 14, 574–585, doi:10.3762/bjnano.14.48

• –Weber, EDIP, ReaxFF, COMB, and machine-learning-based interatomic potentials. A quantitative systematic comparison and a discussion of the results obtained are reported. Keywords: 2D materials; DFT; force fields; interatomic potentials; mechanical properties; silicene; Introduction We are living in

• silicon and five polymorphs of silicon dioxide SNAP [43]: the machine-learning-based (ML-IAP) linear variant of spectral neighbor analysis potential (SNAP) fitted to total energies and interatomic forces in ground-state Si, strained structures, and slab structures obtained from DFT calculations qSNAP [43

• ]: the machine-learning-based (ML-IAP) quadratic variant of spectral neighbor analysis potential (qSNAP) fitted to total energies and interatomic forces in ground-state Si, strained structures, and slab structures obtained from DFT calculations SO(3) [44]: the machine-learning-based (ML-IAP) variant of

Frequency-dependent nanomechanical profiling for medical diagnosis

• Santiago D. Solares and
• Alexander X. Cartagena-Rivera

Beilstein J. Nanotechnol. 2022, 13, 1483–1489, doi:10.3762/bjnano.13.122

• also enable performance analyses that could be extended to more complex and relevant scenarios, aided by advanced analysis tools such as machine learning (see below in Figure 1). Decoding frequency-dependent nanomechanical measurements for disease study and follow-up The broad adoption of AFM

• latter method raises serious concerns about the patient’s health. After acquisition of tissue physical data, a computer data analysis is performed to determine the frequency-dependent mechanical properties (e.g., ES and EL). Then, an unsupervised machine learning cluster analysis is performed to identify

A superconducting adiabatic neuron in a quantum regime

• Marina V. Bastrakova,
• Dmitrii S. Pashin,
• Dmitriy A. Rybin,
• Andrey E. Schegolev,
• Nikolay V. Klenov,
• Igor I. Soloviev,
• Anastasiya A. Gorchavkina and
• Arkady M. Satanin

Beilstein J. Nanotechnol. 2022, 13, 653–665, doi:10.3762/bjnano.13.57

• obtained results indicate the conditions under which the neuron possesses the required sigmoid activation function. Keywords: Josephson junction; quantum neuron; quantum-classical neural networks; superconducting quantum interferometer; Introduction The implementation of machine learning algorithms is

• such a solution since both superconducting quantum machine learning circuits [16][17][18][19][20][21][22] and superconducting ANNs [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] are rapidly developed nowadays. Robust implementation of the considered quantum-classical system would benefit

Tunable superconducting neurons for networks based on radial basis functions

• Andrey E. Schegolev,
• Nikolay V. Klenov,
• Sergey V. Bakurskiy,
• Igor I. Soloviev,
• Mikhail Yu. Kupriyanov,
• Maxim V. Tereshonok and
• Anatoli S. Sidorenko

Beilstein J. Nanotechnol. 2022, 13, 444–454, doi:10.3762/bjnano.13.37

• broadband group signal is extremely important. Also, probabilistic analysis is used in the consideration of stochastic processes [1][2][3][4], as a popular machine learning method for spatial interpolation of non-stationary and non-Gaussian data [5], as a central part of a compensation block to enhance the

A chemiresistive sensor array based on polyaniline nanocomposites and machine learning classification

• Jiri Kroutil,
• Alexandr Laposa,
• Ali Ahmad,
• Jan Voves,
• Vojtech Povolny,
• Ladislav Klimsa,
• Marina Davydova and
• Miroslav Husak

Beilstein J. Nanotechnol. 2022, 13, 411–423, doi:10.3762/bjnano.13.34

• composed by seven different conductive sensors with composite sensing layers are measured and analyzed using machine learning. Statistical tools, such as principal component analysis and linear discriminant analysis, are used as dimensionality reduction methods. Five different classification methods

• dioxide, nitrogen dioxide, carbon monoxide, acetone, and toluene). Moreover, the obtained data were used for machine learning classification. Many pattern recognition models based on intuitive, linear and nonlinear supervised techniques have been explored in E-nose data [11][12]. A considerable number of

• increasing interpretability. We apply PCA and LDA as input data for five machine learning algorithms with a 10-fold cross-validation method. The preprocessing stage was implemented by applying PCA and LDA on the extracted dataset [14][15]. Five different kinds of flexible pattern recognition algorithms have

Identifying diverse metal oxide nanomaterials with lethal effects on embryonic zebrafish using machine learning

• Richard Liam Marchese Robinson,
• Haralambos Sarimveis,
• Philip Doganis,
• Xiaodong Jia,
• Marianna Kotzabasaki,
• Christiana Gousiadou,
• Stacey Lynn Harper and
• Terry Wilkins

Beilstein J. Nanotechnol. 2021, 12, 1297–1325, doi:10.3762/bjnano.12.97

• , embryonic zebrafish (Danio Rerio) are recognised as a key human safety relevant in vivo test system. Hence, machine learning models were developed for identifying metal oxide nanomaterials causing lethality to embryonic zebrafish up to 24 hours post-fertilisation, or excess lethality in the period of 24–120

• these modelling results are on truly external data, which were not used to select the single descriptor model. This will require further laboratory work to generate comparable data to those studied herein. Keywords: data augmentation; embryonic zebrafish; machine learning; nanosafety; nano-QSAR

• that survived at 24 hpf in both the control and dosed groups.) Hence, the modelled lethality data reported at 120 hpf, derived from the raw counts data, may be considered “excess lethality”. Two different machine learning algorithms were applied to try and learn relationships between either the 24 hpf

A review on slip boundary conditions at the nanoscale: recent development and applications

• Ruifei Wang,
• Jin Chai,
• Bobo Luo,
• Xiong Liu,
• Jianting Zhang,
• Min Wu,
• Mingdan Wei and
• Zhuanyue Ma

Beilstein J. Nanotechnol. 2021, 12, 1237–1251, doi:10.3762/bjnano.12.91

• conversion from 3 to 70% [23]. Generally, the methods to investigate slip boundary conditions for nanoconfined liquids include theoretical analysis, physical experiments, and numerical simulations [8][24][25][26][27][28][29][30][31][32][33][34]. In recent years, machine learning methods have also been

Molecular assemblies on surfaces: towards physical and electronic decoupling of organic molecules

• Sabine Maier and
• Meike Stöhr

Beilstein J. Nanotechnol. 2021, 12, 950–956, doi:10.3762/bjnano.12.71

• partial exploration of the potential energy landscape due to the complexity of the system. Recently developed structure search methods that combine machine learning with density functional theory provide the possibility of reliable structure identification of non-planar molecules, as demonstrated for the

The role of convolutional neural networks in scanning probe microscopy: a review

• Ido Azuri,
• Irit Rosenhek-Goldian,
• Neta Regev-Rudzki,
• Georg Fantner and
• Sidney R. Cohen

Beilstein J. Nanotechnol. 2021, 12, 878–901, doi:10.3762/bjnano.12.66

• , various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently

• , convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data. Keywords: atomic force microscopy (AFM); deep learning; machine learning; neural networks; scanning probe microscopy (SPM); Review Introduction: traditional machine learning vs deep learning Machine

• , regression (prediction of quantitative data values), translation (for instance of languages), anomaly detection, de-noising, clustering (grouping similar objects together), and data generation. In this review our major concern is with images, which are most relevant to certain aspects of machine learning as

Reducing molecular simulation time for AFM images based on super-resolution methods

• Zhipeng Dou,
• Jianqiang Qian,
• Yingzi Li,
• Rui Lin,
• Jianhai Wang,
• Peng Cheng and
• Zeyu Xu

Beilstein J. Nanotechnol. 2021, 12, 775–785, doi:10.3762/bjnano.12.61

• resolution. Theoretical investigations are getting highly important for the interpretation of AFM images. Researchers have used molecular simulation to examine the AFM imaging mechanism. With a recent flurry of researches applying machine learning to AFM, AFM images obtained from molecular simulation have

• can be used to speed up the generation of training data and vary simulation resolution for AFM machine learning. Keywords: atomic force microscopy; Bayesian compressed sensing; convolutional neural network; molecular dynamics simulation; super resolution; Introduction Atomic force microscopy methods

• , study the factors affecting resolution [16][17][18][19][20][21][22], and establish an appropriate simulation methodology for the explanation of complex imaging mechanism in liquids [23][24][25]. Besides, there has been a recent flurry of researches applying machine learning to AFM, including predicting

Nanogenerator-based self-powered sensors for data collection

• Yicheng Shao,
• Maoliang Shen,
• Yuankai Zhou,
• Xin Cui,
• Lijie Li and
• Yan Zhang

Beilstein J. Nanotechnol. 2021, 12, 680–693, doi:10.3762/bjnano.12.54

• used for gesture recognition [78][83][84]. The combination of a self-powered motion sensor and a back-end data processing system based on machine learning (ML) can realize sign language recognition for people with language impairment. Zhou et al. [84] fabricated a stretchable sensor for sign language

Intracranial recording in patients with aphasia using nanomaterial-based flexible electronics: promises and challenges

• Qingchun Wang and
• Wai Ting Siok

Beilstein J. Nanotechnol. 2021, 12, 330–342, doi:10.3762/bjnano.12.27

• using machine learning methods to examine the patients’ iEEG signals at the VWFA responding to words that differed in the degree of visual similarity, the researchers found that, shortly after stimulus onset (from approximately 100 to 430 ms), discriminating between words that shared no letters would