Potential Of Machine Learning Research Articles

Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia

Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to limited data availability and complexities in effective care. This study delves into harnessing the potential of machine learning to enhance care strategies for orphan diseases, specifically focusing on allogeneic hematopoietic cell transplantation (allo-HCT) in T-cell prolymphocytic leukemia. The investigation evaluates how varying numbers of variables impact model performance, considering the rarity of the disease. Utilizing data from the Center for International Blood and Marrow Transplant Research, the study scrutinizes outcomes following allo-HCT for T-cell prolymphocytic leukemia. Diverse machine learning models were developed to forecast acute graft-versus-host disease (aGvHD) occurrence and its distinct grades post-allo-HCT. Assessment of model performance relied on balanced accuracy, F1 score, and ROC AUC metrics. The findings highlight the Linear Discriminant Analysis (LDA) classifier achieving the highest testing balanced accuracy of 0.58 in predicting aGvHD. However, challenges arose in its performance during multi-class classification tasks. While affirming the potential of machine learning in enhancing care for orphan diseases, the study underscores the impact of limited data and disease rarity on model performance.

Gas adsorption and framework flexibility of CALF-20 explored via experiments and simulations

In 2021, Svante, in collaboration with BASF, reported successful scale up of CALF-20 production, a stable MOF with high capacity for post-combustion CO2 capture which exhibits remarkable stability towards water. CALF-20’s success story in the MOF commercialisation space provides new thinking about appropriate structural and adsorptive metrics important for CO2 capture. Here, we combine atomistic-level simulations with experiments to study adsorptive properties of CALF-20 and shed light on its flexible crystal structure. We compare measured and predicted CO2 and water adsorption isotherms and explain the role of water-framework interactions and hydrogen bonding networks in CALF-20’s hydrophobic behaviour. Furthermore, regular and enhanced sampling molecular dynamics simulations are performed with both density-functional theory (DFT) and machine learning potentials (MLPs) trained to DFT energies and forces. From these simulations, the effects of adsorption-induced flexibility in CALF-20 are uncovered. We envisage this work would encourage development of other MOF materials useful for CO2 capture applications in humid conditions.

Prediction of Glass Chemical Composition and Type Identification Based on Machine Learning Algorithms

Ancient glass artifacts were susceptible to weathering from the environment, causing changes in their chemical composition, which pose significant obstacles to the identification of glass products. Analyzing the chemical composition of ancient glass has been beneficial for evaluating their weathering status and proposing measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learning techniques were used. The results indicated that a significant correlation (p < 0.05) could only observed between surface weathering and the glass types (high-potassium and lead–barium). Based on the random forest and logistic regression models, the primary chemical components that signify glass types and weathering status were determined using PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented a superior ability to identify glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates the great potential of machine learning for glass chemical component estimation and glass type and weathering status identification, providing technical guidance for the appraisal of ancient glass artifacts.

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.

Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

Interaction between microcapsules and crack using XFEM and data-driven fracture toughness predictive model

The interaction mechanism between microstructures of materials, including microcrack, inclusion and interface defect, is crucial for studying the microscopic damage of materials. The interaction between microcapsules and cracks in self-healing material is examined using a combination of the extended finite element method (XFEM) and the interaction integral method, which is implemented by a program written in Fortran language. The non-dimensional stress intensity factors (SIFs) of a crack with a single microcapsule, a pair of microcapsules and a cluster of microcapsules are investigated. Moreover, the data-driven fracture toughness predictive model using a genetic programming-based symbolic regression (SR) technique is proposed by training the data from the XFEM program. Results reveal that the non-dimensional SIFs are related to the mechanical property, geometry and interface defect of microcapsule. The influence of microcapsule on the SIF is limited within a distance of five times the microcapsule radius. The predictive model quantifies the contribution of microscopic structures on the macroscopic fracture parameter, highlighting the potential of machine learning in guiding the quantitative design and discovery of high-performance smart materials.

Artificial intelligence approaches for tinnitus diagnosis: leveraging high-frequency audiometry data for enhanced clinical predictions

This research investigates the application of machine learning to improve the diagnosis of tinnitus using high-frequency audiometry data. A Logistic Regression (LR) model was developed alongside an Artificial Neural Network (ANN) and various baseline classifiers to identify the most effective approach for classifying tinnitus presence. The methodology encompassed data preprocessing, feature extraction focused on point detection, and rigorous model evaluation through performance metrics including accuracy, Area Under the ROC Curve (AUC), precision, recall, and F1 scores. The main findings reveal that the LR model, supported by the ANN, significantly outperformed other machine learning models, achieving an accuracy of 94.06%, an AUC of 97.06%, and high precision and recall scores. These results demonstrate the efficacy of the LR model and ANN in accurately diagnosing tinnitus, surpassing traditional diagnostic methods that rely on subjective assessments. The implications of this research are substantial for clinical audiology, suggesting that machine learning, particularly advanced models like ANNs, can provide a more objective and quantifiable tool for tinnitus diagnosis, especially when utilizing high-frequency audiometry data not typically assessed in standard hearing tests. The study underscores the potential for machine learning to facilitate earlier and more accurate tinnitus detection, which could lead to improved patient outcomes. Future work should aim to expand the dataset diversity, explore a broader range of algorithms, and conduct clinical trials to validate the models' practical utility. The research highlights the transformative potential of machine learning, including the LR model and ANN, in audiology, paving the way for advancements in the diagnosis and treatment of tinnitus.

Simulating CO2 diffusivity in rigid and flexible Mg-MOF-74 with machine-learning force fields

The flexibility of metal–organic frameworks (MOFs) affects their gas adsorption and diffusion properties. However, reliable force fields for simulating flexible MOFs are lacking. As a result, most atomistic simulations so far have been carried out assuming rigid MOFs, which inevitably overestimates the gas adsorption energy. Here, we show that this issue can be addressed by applying a machine-learning potential, trained on quantum chemistry data, to atomistic simulations. We find that inclusion of flexibility is particularly important for simulating CO2 chemisorption in MOFs with coordinatively unsaturated metal sites. Specifically, we demonstrate that the diffusion of CO2 in a flexible Mg-MOF-74 structure is about one order of magnitude faster than in a rigid one, challenging the rigid-MOF assumption in previous simulations.

Liquid–liquid transition and ice crystallization in a machine-learned coarse-grained water model

Mounting experimental evidence supports the existence of a liquid-liquid transition (LLT) in high-pressure supercooled water. However, fast crystallization of supercooled water has impeded identification of the LLT line TLL(p) in experiments. While the most accurate all-atom (AA) water models display a LLT, their computational cost limits investigations of its interplay with ice formation. Coarse-grained (CG) models provide over 100-fold computational efficiency gain over AA models, enabling the study of water crystallization, but have not yet shown to have a LLT. Here, we demonstrate that the CG machine-learned water model Machine-Learned Bond-Order Potential (ML-BOP) has a LLT that ends in a critical point at pc = 170 ± 10 MPa and Tc = 181 ± 3 K. The TLL(p) of ML-BOP is almost identical to the one of TIP4P/2005, adding to the similarity in the equation of state of liquid water in both models. Cooling simulations reveal that ice crystallization is fastest at the LLT and its supercritical continuation of maximum heat capacity, supporting a mechanistic relationship between the structural transformation of water to a low-density liquid (LDL) and ice formation. We find no signature of liquid-liquid criticality in the ice crystallization temperatures. ML-BOP replicates the competition between formation of LDL and ice observed in ultrafast experiments of decompression of the high-density liquid (HDL) into the region of stability of LDL. The simulations reveal that crystallization occurs prior to the coarsening of the HDL and LDL domains, obscuring the distinction between the highly metastable first-order LLT and pronounced structural fluctuations along its supercritical continuation.

Machine learning methods for background potential estimation in 2DEGs

Two-dimensional electron gases (2DEGs) can show exceptional carrier mobility, making them promising candidates for future quantum technologies. However, impurities and defects can significantly degrade their performance, impacting transport, conductivity, and coherence times. We leverage scanning gate microscopy (SGM) and machine learning approaches to extract the potential landscape of 2DEGs from SGM data. We compare three techniques: image-to-image translation with generative adversarial networks (GANs), cellular neural networks (CNNs), and an evolutionary search algorithm. Notably, the evolutionary approach outperforms both alternatives in defect identification and analysis. This work clarifies the interaction between defects and 2DEG properties, demonstrating the potential of machine learning for understanding and manipulating quantum materials, facilitating advancements in quantum computing and nanoelectronics.

Reservoir temperature prediction based on characterization of water chemistry data—case study of western Anatolia, Turkey

Reservoir temperature estimation is crucial for geothermal studies, but traditional methods are complex and uncertain. To address this, we collected 83 sets of water chemistry and reservoir temperature data and applied four machine learning algorithms. These models considered various input factors and underwent data preprocessing steps like null value imputation, normalization, and Pearson coefficient calculation. Cross-validation addressed data volume issues, and performance metrics were used for model evaluation. The results revealed that our machine learning models outperformed traditional fluid geothermometers. All machine learning models surpassed traditional methods. The XGBoost model, based on the F-3 combination, demonstrated the best prediction accuracy with an R2 of 0.9732, while the Bayesian ridge regression model using the F-4 combination had the lowest performance with an R2 of 0.8302. This study highlights the potential of machine learning for accurate reservoir temperature prediction, offering geothermal professionals a reliable tool for model selection and advancing our understanding of geothermal resources.

Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study

In this study, neural networks and support vector regression (SVR) were employed to predict the degradation over three pharmaceutically active compounds (PhACs): Ibuprofen (IBP), diclofenac (DCF), and caffeine (CAF) within a stirred reactor featuring a flotation cell with two non-concentric ultraviolet lamps. A total of 438 datapoints were collected from published works and distributed into 70% training and 30% test datasets while cross-validation was utilized to assess the training reliability. The models incorporated 15 input variables concerning reaction kinetics, molecular properties, hydrodynamic information, presence of radiation, and catalytic properties. It was observed that the Support Vector Regression (SVR) presented a poor performance as the ε hyperparameter ignored large error over low concentration levels. Meanwhile, the Artificial Neural Networks (ANN) model was able to provide rough estimations on the expected degradation of the pollutants without requiring information regarding reaction rate constants. The multi-objective optimization analysis suggested a leading role due to ozone kinetic for a rapid degradation of the contaminants and most of the results required intensification with hydrogen peroxide and Fenton process. Although both models were affected by accuracy limitations, this work provided a lightweight model to evaluate different Advanced Oxidation Processes (AOPs) by providing general information regarding the process operational conditions as well as know molecular and catalytic properties.

A comprehensive assessment of machine learning algorithms for enhanced characterization and prediction in orodispersible film development

Orodispersible films (ODFs) have emerged as innovative pharmaceutical dosage forms, offering patient-specific treatment through adjustable dosing and the combination of diverse active ingredients. This expanding field generates vast datasets, requiring advanced analytical techniques for deeper understanding of data itself. Machine learning is becoming an important tool in the rapidly changing field of pharmaceutical research, particularly in drug preformulation studies. This work aims to explore into the application of machine learning methods for the analysis of experimental data obtained by ODF characterization in order to obtain an insight into the factors governing ODF performance and use it as guidance in pharmaceutical development. Using a dataset derived from extensive experimental studies, various machine learning algorithms were employed to cluster and predict critical properties of ODFs. Our results demonstrate that machine learning models, including Support vector machine, Random forest and Deep learning, exhibit high accuracy in predicting the mechanical properties of ODFs, such as flexibility and rigidity. The predictive models offered insights into the complex interaction of formulation variables. This research is a pilot study that highlights the potential of machine learning as a transformative approach in the pharmaceutical field, paving the way for more efficient and informed drug development processes.

Application of machine learning in predicting the thermal conductivity of single-filler polymer composites

Polymer composites with superior thermal conductivity and low electrical conductivity are pivotal in the cooling of electronic devices. Despite their prevalence, the accurate prediction of the thermal conductivity of these composites remains a challenge. The emergence of machine learning (ML) provides a groundbreaking approach to solving this issue. In this study, we constructed a comprehensive dataset collected from previous experimental papers and successfully predicted the thermal conductivity of single-filler polymer composites using four ML regression algorithms: random forest regression (RFR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and Gaussian process regression (GPR). By employing feature engineering to select pertinent features from the original datasets, the accuracy of the four models on the test set was improved, among which GBDT exhibited the highest accuracy with the Pearson correlation coefficient value of 0.981. Factors such as filler volume fraction and matrix thermal conductivity significantly influence the thermal conductivity of composite materials, while the thermal conductivity of the fillers has a relatively minor impact. Additionally, we identified the topological polar surface area (TPSA) as a crucial descriptor for surface modifications, quantifying diverse surface-modifying agents. Due to the incorporation of more descriptors, ML models exhibit higher precision and broader applicability compared to empirical formulas. Our study provides an effective tool for predicting the thermal conductivity of polymer composites with single fillers and underscores the potential of machine learning in accelerating materials design.

Advancing spine care through AI and machine learning: overview and applications.

Machine learning (ML), a subset of artificial intelligence, is crucial for spine care and research due to its ability to improve treatment selection and outcomes, leveraging the vast amounts of data generated in health care for more accurate diagnoses and decision support. ML's potential in spine care is particularly notable in radiological image analysis, including the localization and labeling of anatomical structures, detection and classification of radiological findings, and prediction of clinical outcomes, thereby paving the way for personalized medicine. The manuscript discusses ML's application in spine care, detailing supervised and unsupervised learning, regression, classification, and clustering, and highlights the importance of both internal and external validation in assessing ML model performance. Several ML algorithms such as linear models, support vector machines, decision trees, neural networks, and deep convolutional neural networks, can be used in the spine domain to analyze diverse data types (visual, tabular, omics, and multimodal).

Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials

As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic interactions, accurate ab initio molecular dynamics simulations relying on the first-principles calculation of the energies and forces have opened the way to predictive simulations of aqueous systems. Still, these simulations are very demanding, which prevents the study of complex systems and their properties. Modern machine learning potentials (MLPs) have now reached a mature state, allowing us to overcome these limitations by combining the high accuracy of electronic structure calculations with the efficiency of empirical force fields. In this Perspective, we give a concise overview about the progress made in the simulation of water and aqueous systems employing MLPs, starting from early work on free molecules and clusters via bulk liquid water to electrolyte solutions and solid–liquid interfaces.

Revealing the critical role of vanadium in radiation damage of tungsten-based alloys

Refractory tungsten-based medium- and high-entropy alloys form a promising class of strong, heat- and radiation-resistant materials for nuclear applications. Here, we systematically explore the radiation tolerance of Mo-Nb-Ta-V-W alloys using molecular dynamics simulations and a machine-learned interatomic potential. Going from pure W to W-based equiatomic binaries, ternaries, quaternary, and the quinary high-entropy alloy, we simulate the radiation damage accumulation up to 0.4–0.8 dpa through cumulative collision cascades. We find that the radiation damage and evolution are controlled by a combination of cascade-induced clustering, the balance in migration of vacancies and interstitials, and defect cluster binding energies. These mechanisms are strongly influenced by atom size, among which vanadium as the smallest atom is decisive. In pure W and alloys without V, the microstructure is characterised by large and growing interstitial dislocation loops. In stark contrast, in alloys containing V, vacancy dislocation loops are formed directly in collision cascades and the balanced migration rate of vacancies and interstitials promotes recombination and prevents growth of interstitial loops. The atom size differences also lead to clear segregation, with small atoms (V) in interstitial clusters and larger atoms (Ta, Nb) segregating around vacancy clusters. Furthermore, the small cluster sizes, formation of vacancy loops, segregation, and balanced migration in V-containing alloys lead to low swelling and an exceptionally efficient defect annealing compared to pure W and V-free alloys. Our results highlight WTaV as a promising low-activation material, where almost 90% of defects are annealed at 2000 K during 5 ns compared to <20% in pure W.

Detecting the high/low-speed large-scale structures by a machine learning perspective in turbulent boundary

The current developments in machine learning have facilitated the application of data-driven strategies for predicting the evolution of vortex structures in turbulent boundary layers. This study combines the attached eddy hypothesis with the extreme gradient boosting model to forecast large-scale high/low-speed motion based on a series of input signals. The performance of the trained model, as quantified by the percentage of accurately predicted high/low-speed regions, exhibits variability concerning the deviation between the input velocity fluctuation and the predicted output value at z/δ=0.016, where ‘z’ represents the wall-normal height and ‘δ’ indicates the boundary layer thickness. Our findings underscore the significant potential of machine learning in predicting high/low-speed regions within large-scale motion.

Using Machine Learning to Predict Axial Pile Capacity

Accurate estimation of the ultimate axial load bearing capacity of piles is necessary to ensure the safety of the supported structures and to prevent cost overruns. Traditional mechanics-based design methods do not always predict pile capacity accurately, or precisely, leaving room for improvement. This study focuses on the potential of machine learning (ML) in estimating pile capacity. A dataset of 546 load tests was compiled from three databases. The baseline performance of traditional design methods was first established by comparing the capacities computed using four traditional approaches, against the capacities interpreted from load tests using Davisson’s criterion. Sixteen different ML techniques were explored. First, the optimal feature selection technique for model training was investigated. Second, hyperparameters of each technique were optimized. The process involved the training of 32,000 different models and tuning their hyperparameters. Next the dataset was randomly split into training (70%) and testing (30%) for comparing the 16 different ML regression models. Each of the optimized models was then trained using six feature sets. The performance of each of the 16 ML models with the best performing feature subset was compared with the baseline performance from traditional methods. Evaluation criteria included measured versus predicted capacities, influence of soil type on accuracy, as well as the absence of pile diameter, or length effects on accuracy and precision. In general, the ML methods performed significantly better than the best traditional method. The current research demonstrated that ML may offer advantages in geotechnical design when large datasets are available.

Accurate neural-network-based fitting of full-dimensional N2 –Ar and N2–CH4 two-body potential energy surfaces aimed at spectral simulations

We describe the development of machine-learned (ML) potentials for flexible, weakly interacting monomers. A recently suggested permutationally invariant polynomial neural network (PIP-NN) approach is utilised to represent the full-dimensional two-body component of the molecular pair energy. To ensure the asymptotic zero-interaction limit, a tailored subset of the full invariant polynomial basis set is utilised, and their variables are modified to achieve a better fit of the correct asymptotic behaviour at a long range. This new technique is used to build full-dimensional potentials for the two-body N 2 –Ar and N 2 –CH 4 interactions by fitting databases of ab initio energies calculated at the coupled-cluster level of theory. The second virial coefficient, fully accounting for molecular flexibility, is then calculated within the classical framework using the obtained PIP-NN potential surfaces. A trajectory-based simulation of the N 2 –Ar collision-induced absorption is conducted, covering both the far- and mid-infrared ranges.

Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper During Chemical Vapor Deposition.

In recent years, liquid metal catalysts have emerged as a compelling choice for the controllable, large-scale, and high-quality synthesis of two-dimensional materials. At present, there is little mechanistic understanding of the intricate catalytic process, though, of its governing factors or what renders it superior to growth at the corresponding solid catalysts. Here, we report on a combined experimental and computational study of the kinetics of graphene growth during chemical vapor deposition on a liquid copper catalyst. By monitoring the growing graphene flakes in real time using in situ radiation-mode optical microscopy, we explore the growth morphology and kinetics over a wide range of CH4-to-H2 pressure ratios and deposition temperatures. Constant growth rates of the flakes' radius indicate a growth mode limited by precursor attachment, whereas methane-flux-dependent flake shapes point to limited precursor availability. Large-scale free energy simulations enabled by an efficient machine-learning moment tensor potential trained to density functional theory data provide quantitative barriers for key atomic-scale growth processes. The wealth of experimental and theoretical data can be consistently combined into a microkinetic model that reveals mixed growth kinetics that, in contrast to the situation at solid Cu, is partly controlled by precursor attachment alongside precursor availability. Key mechanistic aspects that directly point toward the improved graphene quality are a largely suppressed carbon dimer attachment due to the facile incorporation of this precursor species into the liquid surface and a low-barrier ring-opening process that self-heals 5-membered rings resulting from remaining dimer attachments.