jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Theodoros Anagnostopoulos | Ioakeim Spiliopoulos
Source: WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT (article, 2024-05-13)
Field: Chemistry — Organic Chemistry

Classifying PDO Kalamata Olive Oil from Geographic Origins of the Messenia Region based on Statistical Machine Learning Kalamata is a smart city located in southeastern Greece in the Mediterranean basin and it is the capital of the Messenia regional unit. It is known for the famous Protected Designation of Origin (PDO) Kalamata olive oil produced mainly from the Koroneiki olive variety. The PDO Kalamata olive oil, established by Council regulation (EC) No 510/2006, owes its quality and special characteristics to the geographical environment, olive tree variety, and human factor. The PDO Kalamata olive oil is produced exclusively in the regional unit of Messenia, being the main profit of local farmers. However, soil chemical composition, microclimates, and agronomic factors are changed within the Messenia spatial area leading to differentiation of PDO Kalamata olive oil characteristic. In this paper, we use statistical machine learning algorithms to determine the geographical origin of Kalamata olive oil at PDO level based on synchronous excitation−emission fluorescence spectroscopy of olive oils. Evaluations of the statistical models are promising for differentiating the origin of PDO Kalamata olive oil with high values of prediction accuracy thus enabling companies that process and bottle kalamata olive oil to choose olive oil from a specific region of Messenia that fulfills certain characteristics. Concretely, the current research effort focuses on a specific olive oil variety within a limited geographic region. Intuitively, future research should also focus on validation of the proposed methodology to other olive oil varieties and production areas. Olive oil ; Statistical analysis ; Artificial intelligence ; Statistics ; Computer science ; Mathematics ; Food science ; Chemistry Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Isotope Analysis in Ecology
Authors: Benjamin B. Hoar | Weitong Zhang | Yuanzhou Chen | Jingwen Sun | Hongyuan Sheng | Yucheng Zhang | Jenny Y. Yang | Cyrille Costentin | Quanquan Gu | Chong Liu
Source: ChemRxiv (preprint, 2024-05-03)
Field: Chemistry — Electrochemistry

Redox-detecting deep learning for mechanism discernment in multi-redox cyclic voltammograms In electrochemical analysis, mechanism assignment is fundamental to understanding the chemistry of a system. The detection and classification of electrochemical mechanisms in cyclic voltammetry set the foundation for subsequent quantitative evaluation and practical application, but are often based on relatively subjective visual analyses. Deep-learning (DL) techniques provide an alternative, automated means that can support experimentalists in mechanism assignment. Herein, we present a custom DL architecture dubbed as EchemNet, capable of assigning both voltage windows and mechanism classes to electrochemical events within multi-redox cyclic voltammograms. The developed technique detects over 96% of all electrochemical events in simulated test data and shows a classification accuracy of up to 97.2% on redox events with 8 known mechanisms. This newly developed DL model, the first of its kind, proves the feasibility of redox-event detection and electrochemical mechanism classification with minimal a priori knowledge. The DL model will augment human researchers’ productivity and constitute a critical component in a general-purpose autonomous electrochemistry laboratory. Redox ; Discernment ; Cyclic voltammetry ; Mechanism (biology) ; Chemistry ; Electrochemistry ; Computer science ; Inorganic chemistry ; Electrode ; Physical chemistry ; Philosophy ; Epistemology Electrochemical Analysis and Applications ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Ishan Nadkarni | Jinu Jeong | Bugra Yalcin | N. R. Aluru
Source: The Journal of Physical Chemistry A (article, 2024-11-14)
Field: Materials Science — Materials Chemistry

Modulating Coarse-Grained Dynamics by Perturbing Free Energy Landscapes We introduce an approach to describe the long-time dynamics of multiatomic molecules by modulating the free energy landscape (FEL) to capture dominant features of the energy-barrier crossing dynamics of the all-atom (AA) system. Notably, we establish that the self-diffusion coefficient of coarse-grained (CG) systems can be accurately delineated by enhancing conservative force fields with high-frequency perturbations. Using theoretical arguments, we show that these perturbations do not alter the lower-order distribution functions, thereby preserving the structure of the AA system after coarse-graining. We demonstrate the utility of this approach using molecular dynamics simulations of simple molecules in bulk with distinct dynamical characteristics with and without time scale separations as well as for inhomogeneous systems where a fluid is confined in a slit-like nanochannel. Additionally, we also apply our approach to more powerful many-body potentials optimized by using machine learning (ML). Dynamics (music) ; Energy (signal processing) ; Environmental science ; Statistical physics ; Geology ; Physics ; Computer science ; Acoustics Material Dynamics and Properties ; Theoretical and Computational Physics ; Nanopore and Nanochannel Transport Studies
Authors: Shida He | Xiucai Ye | Tetsuya Sakurai
Source: Medinformatics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

CLOP-hERG: The Contrastive Learning Optimized Pre-trained Model for Representation Learning in Predicting Drug-Induced hERG Channel Blockers During drug development, ensuring that drug molecules do not block the hERG (human Ether-à-go-go-Related Gene) channel is critical. If this channel is blocked, it can cause many cardiovascular-related diseases. However, traditional experimental detection methods are expensive and time-consuming. In this work, we proposed a novel deep learning framework CLOP-hERG, that combines contrastive learning with the RoBERTa pre-trained model to predict whether drug molecules will block the hERG channel. We employed data augmentation techniques on molecular structures to ensure that our model can capture the multifaceted information of the molecules. Besides, we used a contrastive learning strategy to enable the model to learn meaningful molecular features from large unlabeled datasets. The RoBERTa pre-trained model played a pivotal role in this process, giving our model with a robust representational learning capability. The model, obtained through contrastive learning, was further fine-tuned to achieve high-precision prediction of hERG blockers. Through a series of experiments, we demonstrated the effectiveness of CLOP-hERG. This work provides a novel and effective strategy for predicting hERG blockers and provides some insights for other similar pharmaceutical tasks. hERG ; Computer science ; Artificial intelligence ; Block (permutation group theory) ; Channel (broadcasting) ; Drug discovery ; Machine learning ; Process (computing) ; Representation (politics) ; Chemistry ; Potassium channel ; Biology ; Mathematics ; Biophysics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cardiac electrophysiology and arrhythmias
Authors: Subhasmita Kar | S. J. Ray
Source: ACS Applied Materials & Interfaces (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Exploration of Intrinsically Spin-Ordered Two-Dimensional (2D) Nanomagnets The existence of spontaneous spin-ordering in two-dimensional (2D) nanomagnets holds significant importance due to their several unique and promising properties that distinguish them from conventional 2D materials. In recent times, machine learning (ML) has emerged as a powerful tool for effectively exploring and identifying the optimal 2D materials for specific applications or properties within a limited span of time. Here, we have introduced ML-accelerated approaches to specifically estimate the properties, such as the HSE bandgap and magnetoanisotropic energy (MAE) of 2D magnetic materials. Supervised ML algorithms were employed to derive the descriptors that are capable of predicting the properties of intrinsic 2D magnetic materials. Furthermore, the feature selection score is also calculated to reduce the feature space complexity and improve the model accuracy. The input features were obtained from the C2DB database, and models were constructed using linear regression, Lasso, decision tree, random forest, XG Boost, and support vector machine algorithms. The random forest model predicted the HSE band gaps with an unprecedented low root-mean-square error (RMSE) of 0.22 eV, while the linear regression gives the best fit with RMSEs of 0.25 and 0.22 meV for the MAE(<i>x</i>) and MAE(<i>y</i>), respectively. Therefore, the integration of interpretable analytical models with density functional theory offers a swift and reliable approach for uncovering the properties of intrinsic 2D magnetic materials. This collaborative methodology not only ensures speed in analysis but also enriches the material space. Nanomagnet ; Materials science ; Nanotechnology ; Spin (aerodynamics) ; Mechanical engineering ; Engineering ; Physics Machine Learning in Materials Science ; Graphene research and applications ; Ferroelectric and Negative Capacitance Devices
Authors: Jinyoung Byun | Donggeon Lee | E. H. Hwang | Sooran Kim | Ji‐Sang Park
Source: The Journal of Physical Chemistry A (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Convergence Analysis and Predictions for Optimizing Reciprocal Grids: A First-Principles and Machine Learning Study When crystalline materials are investigated by performing first-principles density functional theory (DFT) calculations, the reciprocal grid should be fine enough to obtain the converged total energy and electronic structure. Herein, we performed a convergence test of the total energy for the density of reciprocal points to determine fine enough reciprocal grids for high-throughput calculations. Our results show that the nonlinearity of the band structures affects the convergence of the total energy, especially for materials with a finite band gap. We further investigate which physical properties make a finer reciprocal grid necessary based on machine learning (ML) analysis. Our developed models using DFT-based features and elemental properties-based features exhibit <i>R</i><sup>2</sup> of 0.803 and 0.880, respectively. Our ML model quantitatively shows the importance of nonlinearity and band gaps in predicting errors in total energy calculations. Furthermore, our ML model using elemental features can be applied to estimate the appropriate reciprocal grid, facilitating high-throughput calculations. Reciprocal ; Convergence (economics) ; Grid ; Density functional theory ; Reciprocal lattice ; Computer science ; Nonlinear system ; Band gap ; Energy (signal processing) ; Electronic structure ; Throughput ; Statistical physics ; Algorithm ; Applied mathematics ; Topology (electrical circuits) ; Mathematics ; Computational chemistry ; Physics ; Geometry ; Quantum mechanics ; Chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Lasse Laurson | Mikko J. Alava
Source: Journal of Materials Science Materials Theory (article, 2024-05-13)
Field: Materials Science — Materials Chemistry

Dislocation-precipitate interactions in crystals: from the BKS model to collective dislocation dynamics Abstract The increase in the yield stress due to the presence of obstacles to dislocation motion such as precipitates is a multiscale phenomenon. The details on the nanoscale when an individual dislocation runs into a precipitate play an important role in determining plasticity on a macroscopic scale. The classical analysis of this phenomenon is due to Bacon, Kocks and Scattergood (BKS) from early 1970’s and has been followed by a large body of work both developing the theory and applying it to real experiments and their understanding. Beyond the microscopic details the next level of complexity is met in the micrometer scale when the physics of the yielding and the yield stress depend on two mechanisms: the dislocation-precipitate interaction, and the collective dynamics of the whole ensemble of dislocations in the volume. In this review we discuss the BKS relation and collective dislocation dynamics in precipitation-hardened crystals in the light of recent research, including large-scale discrete dislocation dynamics simulations, statistical physics ideas, and machine learning developments. Dislocation ; Dynamics (music) ; Materials science ; Condensed matter physics ; Dislocation creep ; Crystallography ; Physics ; Chemistry Microstructure and mechanical properties ; nanoparticles nucleation surface interactions ; High-pressure geophysics and materials
Authors: Qi Zhang | Junlin Dong | Chuan Zhou | Dantong Zhang | Shuguang Yuan | Denis Kramer | Dongfeng Xue | Chao Peng
Source: STAR Protocols (article, 2024-01-09)
Field: Materials Science — Materials Chemistry

Machine learning for data-driven design of high-safety lithium metal anode Here, we present a protocol for developing an inorganic-organic hybrid interphase layer using the self-assembled monolayers technique to enhance the surface of the lithium metal anode. We describe steps for extracting organic molecules from open-sourced databases and calculating their microscopic properties. We then detail procedures for developing a machine learning model for predicting the ionic diffusion barrier and preparing the inputs for prediction. This protocol enables a cost-effective workflow to identify promising self-assembled monolayers with exceptional performance. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2023).<sup>1</sup>. Workflow ; Anode ; Computer science ; Protocol (science) ; Lithium (medication) ; Monolayer ; Lithium metal ; Materials science ; Nanotechnology ; Database ; Chemistry ; Electrode Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Nasir Mehmood | Jianbo Wang | Qingfang Liu
Source: Physica Scripta (article, 2024-06-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Feature engineering mediated machine learning prediction of high-frequency property of soft magnetic thin films Abstract High-frequency applications including magnetic write heads, inductors, transformers and sensors etc, demand thin films with tremendous soft magnetic properties. In this regard, the resonance frequency of the soft magnetic thin films is of crucial importance which has a direct relation with material properties. Meanwhile, machine learning (ML) has recently evolved as an efficient tool for material designing tasks including material-property relationship. For an ML algorithm to efficiently perform such tasks, feature engineering is implemented, in which a set of descriptors is designed that can best represent the data. Here, we employ the ML technique, combined with data augmentation and feature engineering, for the estimation of the resonance frequency ( f r ) value of thin films for high-frequency applications from different sets of theoretical and experimental features. Among the selected experimental features, when anisotropy field is combined with saturation magnetization, the ML model’s performance is improved, which is in line with the Kittel’s expression for f r estimation. Finally, it is shown that the data augmentation can improve the training score, and reduce the mean squared error as well as bias of the models. The outcomes of this work are of key importance in paving the way for ML implementation in material-property analysis and time and cost-effective high-frequency device designing. Computer science ; Feature (linguistics) ; Thin film ; Property (philosophy) ; Mean squared error ; Anisotropy ; Feature engineering ; Materials science ; Algorithm ; Artificial intelligence ; Nanotechnology ; Mathematics ; Optics ; Deep learning ; Physics Magnetic Properties and Applications ; Magnetic properties of thin films ; Magnetic and transport properties of perovskites and related materials
Authors: Jie Liu
Source: Computational energy science. (article, 2024-10-05)
Field: Chemistry — Spectroscopy

Multi-scale molecular simulation methods in energy storage systems Multi-scale molecular simulation methods, integrating Molecular Dynamics (MD) and Monte Carlo (MC) techniques, have emerged as transformative tools for studying complex fluid behaviors in nanoporous media. This perspective highlights the synergy between MD’s atomistic precision and MC’s statistical efficiency, enabling accelerated simulations of rare events and large-scale systems. Applications span energy storage (shale gas adsorption, CO₂ sequestration, hydrogen leakage mitigation), demonstrating the versatility of these methods. Key innovations include spatial-temporal coarsening strategies and novel state definitions that balance computational speed with accuracy. Challenges such as error propagation in coarsening and force field limitations are discussed, alongside future directions leveraging machine learning and multi-physics integration. Document Type: Perspective Cited as: Liu, J. Multi-scale molecular simulation methods in energy storage systems. Computational Energy Science, 2024, 1(4): 164-166. https://doi.org/10.46690/compes.2024.04.01 References Cao, S. C., Yuan, Y., Jung, J., et al. Effects of end-member sediments on CO2 hydrate formation: Implications for geological carbon storage. Advances in Geo-Energy Research, 2024, 14(3): 224-237. Feng, X., Liu, J., Shi, J., et al. Phase equilibrium, thermodynamics, hydrogen-induced effects and the interplay mechanisms in underground hydrogen storage. Computational Energy Science, 2024, 1(1): 46-64. Liu, J., Tang, Q., Kou, J., et al. A quantitative study on the approximation error and speed-up of the multi-scale MCMC (Monte Carlo Markov chain) method for molecular dynamics. Journal of Computational Physics, 2022, 469: 111491. Liu, J., Zhang, T., Sun, S. Molecular insights into the carbon dioxide sequestration in kerogen: An accelerated algorithm coupling molecular dynamics simulations and Monte Carlo methods. Process Safety and Environmental Protection, 2024a, 185: 1336-1351. Liu, J., Zhang, T., Sun, S. Molecular mechanisms of hydrogen leakage through caprock in moisture and residual gas conditions: A molecular dynamics-Monte Carlo study. Physics of Fluids, 2024b, 36(2)：022024. Wu, L., Qiao, L., Fan, J., et al. Investigation on natural gas leakage and diffusion characteristics based on CFD. Gas Science and Engineering, 2024, 123: 205238. Scale (ratio) ; Computer science ; Energy storage ; Process engineering ; Engineering ; Physics ; Thermodynamics Advanced NMR Techniques and Applications ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Lorna Chepkemoi | Daisy Salifu | Lucy Kananu Murungi | Henri E. Z. Tonnang
Source: Journal of Data Analysis and Information Processing (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

High Dimension Multivariate Data Analysis for Small Group Samples of Chemical Volatile Profiles of African Nightshade Species Quantitative headspace analysis of volatiles emitted by plants or any other living organisms in chemical ecology studies generates large multidimensional data that require extensive mining and refining to extract useful information. More often the number of variables and the quantified volatile compounds exceed the number of observations or samples and hence many traditional statistical analysis methods become inefficient. Here, we employed machine learning algorithm, random forest (RF) in combination with distance-based procedure, similarity percentage (SIMPER) as preprocessing steps to reduce the data dimensionality in the chemical profiles of volatiles from three African nightshade plant species before subjecting the data to non-metric multidimensional scaling (NMDS). In addition, non-parametric methods namely permutational multivariate analysis of variance (PERMANOVA) and analysis of similarities (ANOSIM) were applied to test hypothesis of differences among the African nightshade species based on the volatiles profiles and ascertain the patterns revealed by NMDS plots. Our results revealed that there were significant differences among the African nightshade species when the data’s dimension was reduced using RF variable importance and SIMPER, as also supported by NMDS plots that showed S. scabrum being separated from S. villosum and S. sarrachoides based on the reduced data variables. The novelty of our work is on the merits of using data reduction techniques to successfully reveal differences in groups which could have otherwise not been the case if the analysis were performed on the entire original data matrix characterized by small samples. The R code used in the analysis has been shared herein for interested researchers to customise it for their own data of similar nature. Dimension (graph theory) ; Multivariate statistics ; Group (periodic table) ; Multivariate analysis ; Chromatography ; Chemistry ; Mathematics ; Statistics ; Environmental chemistry ; Organic chemistry ; Combinatorics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Phytochemicals and Antioxidant Activities
Authors: Zachary Humphreys | Xenophon Evangelopoulos | Stavros Gerolymatos | Edward O. Pyzer‐Knapp | Andrew I. Cooper
Source: ChemRxiv (preprint, 2024-08-08)
Field: Materials Science — Materials Chemistry

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the model complexity which in turn scales up training times and memory consumption. In this work we focus on efficiently capturing global interactions ``in-model'', through long-range edge attentions with minimal memory footprints. We introduce a novel ``contextual'' message passing scheme that better captures global interactions by attending on edges from both the local and global environment of each node in an edge-update fashion. The performance of the proposed model (LiCOMPGNN) is tested on a diverse set of materials property prediction benchmarks and demonstrates competitiveness against state-of-the-art models in several prediction tasks whilst being an order of magnitude smaller in terms of trainable parameters. We further augment the framework to a multiplex graph setting for solid-state data with reciprocal space features taken into account in a multimodal message passing regime. We demonstrate the representation capacity of the proposed variant along with others in maintaining supercell invariance in crystalline property prediction tasks. Computer science ; Message passing ; Representation (politics) ; Property (philosophy) ; Graph ; Inference ; Theoretical computer science ; Enhanced Data Rates for GSM Evolution ; Focus (optics) ; Artificial intelligence ; Distributed computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography