jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pengchao Zhang | Axel Tosello Gardini | Xuefei Xu | Michele Parrinello
Source: Journal of Chemical Information and Modeling (article, 2024-04-15)
Field: Chemistry — Physical and Theoretical Chemistry

Intramolecular and Water Mediated Tautomerism of Solvated Glycine Understanding tautomerism and characterizing solvent effects on the dynamic processes pose significant challenges. Using enhanced-sampling molecular dynamics based on state-of-the-art deep learning potentials, we investigated the tautomeric equilibria of glycine in water. We observed that the tautomerism between neutral and zwitterionic glycine can occur through both intramolecular and intermolecular proton transfers. The latter proceeds involving a contact anionic-glycine-hydronium ion pair or separate cationic-glycine-hydroxide ion pair. These pathways with comparable barriers contribute almost equally to the reaction flux. Tautomer ; Intramolecular force ; Chemistry ; Glycine ; Intermolecular force ; Hydroxide ; Cationic polymerization ; Hydronium ; Proton ; Hydrogen bond ; Solvent ; Ion ; Photochemistry ; Computational chemistry ; Molecule ; Stereochemistry ; Inorganic chemistry ; Organic chemistry ; Amino acid Crystallography and molecular interactions ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: E. Slootman | Igor Poltavsky | Ravindra Shinde | Jacopo Cocomello | Saverio Moroni | Alexandre Tkatchenko | Claudia Filippi
Source: Journal of Chemical Theory and Computation (preprint, 2024-07-14)
Field: Materials Science — Materials Chemistry

Accurate Quantum Monte Carlo Forces for Machine-Learned Force Fields: Ethanol as a Benchmark Quantum Monte Carlo (QMC) is a powerful method to calculate accurate energies and forces for molecular systems. In this work, we demonstrate how we can obtain accurate QMC forces for the fluxional ethanol molecule at room temperature by using either multideterminant Jastrow-Slater wave functions in variational Monte Carlo or just a single determinant in diffusion Monte Carlo. The excellent performance of our protocols is assessed against high-level coupled cluster calculations on a diverse set of representative configurations of the system. Finally, we train machine-learning force fields on the QMC forces and compare them to models trained on coupled cluster reference data, showing that a force field based on the diffusion Monte Carlo forces with a single determinant can faithfully reproduce coupled cluster power spectra in molecular dynamics simulations. Quantum Monte Carlo ; Monte Carlo method ; Statistical physics ; Benchmark (surveying) ; Diffusion Monte Carlo ; Force field (fiction) ; Coupled cluster ; Cluster (spacecraft) ; Physics ; Monte Carlo molecular modeling ; Dynamic Monte Carlo method ; Field (mathematics) ; Work (physics) ; Quantum ; Computer science ; Molecule ; Quantum mechanics ; Mathematics ; Markov chain Monte Carlo Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Advanced Chemical Physics Studies
Authors: Hui Yang | Woojo Kim | Hocheon Yoo | Eun Kwang Lee
Source: Advanced Materials Technologies (article, 2024-08-01)
Field: Materials Science — Polymers and Plastics

Organic Electrochemical Transistors with Microporous Structures via Phase‐Separation for Enhancing Long‐Term Plasticity in Artificial Synapses Abstract Artificial synapses, inspired by the intricate design of biological synapses, utilize electrical, chemical, and mechanical signals to transmit and retain information. Recent advances have involved research on artificial synapses based on organic electrochemical transistors (OECTs), emphasizing low power consumption and rapid response times. A notable challenge arises when the gate voltage is removed, causing doped ions to return quickly to the electrolyte. A simple yet efficient approach is used to solve this problem: forming a microporous active layer using a phase separation method. This technique can maximize the contact area between the electrolyte and the active layer, enhancing ion doping/de‐doping in OECTs. Improvements in the product of hole mobility and volumetric capacitance is achieved. The electrostatic coupling effect and electrochemical doping in synaptic OECTs occur better than in the pristine active layer, yielding enhanced performance with higher short‐term and long‐term synaptic plasticity, compared to pristine OECTs. Moreover, improved ambipolar characteristics is shown by n ‐dopant injection. This paper reports a way to improve performance by simply modifying the surface shape of the active layer using the phase separation, contributing to advancements in artificial synapses for neural networks. Microporous material ; Term (time) ; Materials science ; Separation (statistics) ; Phase (matter) ; Electrochemistry ; Transistor ; Nanotechnology ; Computer science ; Chemistry ; Electrical engineering ; Composite material ; Electrode ; Voltage ; Engineering ; Machine learning ; Organic chemistry ; Physics Conducting polymers and applications ; Advanced Memory and Neural Computing ; Organic Electronics and Photovoltaics
Authors: Rute A. S. Ferreira | Sandra F. H. Correia | Lianshe Fu | Pétia Georgieva | Mário Antunes | Paulo André
Source: Scientific Reports (article, 2024-02-20)
Field: Materials Science — Polymers and Plastics

Predicting the efficiency of luminescent solar concentrators for solar energy harvesting using machine learning Building-integrated photovoltaics (BIPV) is an emerging technology in the solar energy field. It involves using luminescent solar concentrators to convert traditional windows into energy generators by utilizing light harvesting and conversion materials. This study investigates the application of machine learning (ML) to advance the fundamental understanding of optical material design. By leveraging accessible photoluminescent measurements, ML models estimate optical properties, streamlining the process of developing novel materials, offering a cost-effective and efficient alternative to traditional methods, and facilitating the selection of competitive materials. Regression and clustering methods were used to estimate the optical conversion efficiency and power conversion efficiency. The regression models achieved a Mean Absolute Error (MAE) of 10%, which demonstrates accuracy within a 10% range of possible values. Both regression and clustering models showed high agreement, with a minimal MAE of 7%, highlighting the efficacy of ML in predicting optical properties of luminescent materials for BIPV. Building-integrated photovoltaics ; Cluster analysis ; Computer science ; Photovoltaics ; Solar energy ; Energy conversion efficiency ; Mean absolute percentage error ; Process engineering ; Efficient energy use ; Regression analysis ; Materials science ; Machine learning ; Photovoltaic system ; Optoelectronics ; Artificial neural network ; Engineering ; Electrical engineering Transition Metal Oxide Nanomaterials ; Random lasers and scattering media ; Perovskite Materials and Applications
Authors: Hang Hu | Andrew Singh | Dan Lehnherr | Velabo Mdluli | Stephanie W. Chun | Amanda M. Makarewicz | Joseph R. Gouker | Ophelia Ukaegbu | Shasha Li | Xiujuan Wen | David G. McLaren | Juan E. Velásquez | Jeffrey C. Moore | Stephanie Galanie | Emmanuel Appiah‐Amponsah | Erik L. Regalado
Source: Analytical Chemistry (article, 2024-01-02)
Field: Chemistry — Spectroscopy

Accelerating Pharmaceutical Process Development with an Acoustic Droplet Ejection-Multiple Reaction Monitoring-Mass Spectrometry Workflow Fast-paced pharmaceutical process developments (e.g., high-throughput experimentation, directed evolution, and machine learning) involve the introduction of fast, sensitive, and accurate analytical assays using limited sample volumes. In recent years, acoustic droplet ejection (ADE) coupled with an open port interface has been invented as a sampling technology for mass spectrometry, providing high-throughput nanoliter analytical measurements directly from the standard microplates. Herein, we introduce an ADE-multiple reaction monitoring-mass spectrometry (ADE-MRM-MS) workflow to accelerate pharmaceutical process research and development (PR&D). This systematic workflow outlines the selection of MRM transitions and optimization of assay parameters in a data-driven manner using rapid measurements (1 sample/s). The synergy between ADE sampling and MRM analysis enables analytical assays with excellent sensitivity, selectivity, and speed for PR&D reaction screenings. This workflow was utilized to develop new ADE-MRM-MS assays guiding a variety of industrial processes, including (1) screening of Ni-based catalysts for C-N cross-coupling reaction at 1 Hz and (2) high-throughput regioisomer analysis-enabled enzyme library screening for peptide ligation reaction. ADE-MRM-MS assays were demonstrated to deliver accurate results that are comparable to conventional liquid chromatography (LC) experiments while providing >100-fold throughput enhancement. Chemistry ; Workflow ; Mass spectrometry ; Process (computing) ; Process development ; Selected reaction monitoring ; Process engineering ; Chromatography ; Tandem mass spectrometry ; Database Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Lorenzo Strani | Marina Cocchi | Daniele Tanzilli | Alessandra Biancolillo | Federico Marini | Raffaele Vitale
Source: TrAC Trends in Analytical Chemistry (article, 2024-12-24)
Field: Chemistry — Analytical Chemistry

One class classification (class modelling): State of the art and perspectives Classification, i.e., the prediction of one or more qualitative attributes of samples based on the measured data, is ubiquitous in chemistry, and, more specifically, in analytical chemistry. Among the possible classification strategies, class modelling techniques, which aim at describing one category at a time, present several advantages over discriminant ones, especially when dealing with asymmetric problems featuring one category of interest being well characterized and representatively sampled and another (made of everything that is not belonging to the first specific group) being under-represented by definition and highly heterogeneous. In this review, the fundamentals of class modelling are illustrated, together with an overview of the main techniques of this kind proposed in the literature, namely Soft Independent Modelling of Class Analogy (SIMCA), Unequal Class Spaces (UNEQ), Potential Functions (PF), Partial Least Squares (PLS)-based algorithms, One Class-Support Vector Machines (OC-SVM) or Neural Networks (NN)-based strategies. • Highlights (max 85 characters including spaces). • Strategies for class-modeling are presented and critically discussed. • Recent applications in analytical chemistry are also reviewed. • Possible future scenarios and perspectives are sketched. Class (philosophy) ; Mathematics ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Statistical Methods and Models
Authors: Amin Hamed Mashhadzadeh | Maryam Zarghami Dehaghani | Amir Hamed Mashhadzadeh | Aidyn Kadyr | Boris Golman | Christos Spitas | Κωνσταντίνος Κώστας
Source: Computational Materials Science (article, 2024-04-07)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted design of boron and nitrogen doped graphene nanosheets with tailored thermomechanical properties Graphene ; Boron ; Materials science ; Doping ; Nanotechnology ; Nitrogen ; Boron nitride ; Chemical engineering ; Chemistry ; Optoelectronics ; Organic chemistry ; Engineering Graphene research and applications ; Thermal properties of materials ; Boron and Carbon Nanomaterials Research
Authors: Sai Mahit Vadaddi | Qiyuan Zhao | Brett M. Savoie
Source: The Journal of Physical Chemistry A (article, 2024-03-22)
Field: Materials Science — Materials Chemistry

Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor. Transferability ; Computer science ; Graph ; Activation energy ; Theoretical computer science ; Chemistry ; Machine learning ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Tim Hsu | Babak Sadigh | Vasily V. Bulatov | Fei Zhou
Source: Journal of Chemical Theory and Computation (article, 2024-03-15)
Field: Materials Science — Materials Chemistry

Score Dynamics: Scaling Molecular Dynamics with Picoseconds Time Steps via Conditional Diffusion Model We propose score dynamics (SD), a general framework for learning accelerated evolution operators with large timesteps from molecular dynamics (MD) simulations. SD is centered around scores or derivatives of the transition log-probability with respect to the dynamical degrees of freedom. The latter play the same role as force fields in MD but are used in denoising diffusion probability models to generate discrete transitions of the dynamical variables in an SD time step, which can be orders of magnitude larger than a typical MD time step. In this work, we construct graph neural network-based SD models of realistic molecular systems that are evolved with 10 ps timesteps. We demonstrate the efficacy of SD with case studies of the alanine dipeptide and short alkanes in aqueous solution. Both equilibrium predictions derived from the stationary distributions of the conditional probability and kinetic predictions for the transition rates and transition paths are in good agreement with MD. Our current SD implementation is about 2 orders of magnitude faster than the MD counterpart for the systems studied in this work. Open challenges and possible future remedies to improve SD are also discussed. Molecular dynamics ; Statistical physics ; Scaling ; Picosecond ; Work (physics) ; Water model ; Computer science ; Physics ; Chemistry ; Mathematics ; Computational chemistry ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Nanopore and Nanochannel Transport Studies
Authors: Michael Adesokan | Bolanle Otegbayo | Emmanuel Oladeji Alamu | Michael Afolabi Olutoyin | Busie Maziya‐Dixon
Source: Journal of Food Composition and Analysis (article, 2024-08-30)
Field: Chemistry — Analytical Chemistry

Evaluating the dry matter content of raw yams using hyperspectral imaging spectroscopy and machine learning Hyperspectral imaging ; Spectroscopy ; Content (measure theory) ; Dry matter ; Artificial intelligence ; Chemistry ; Environmental science ; Computer science ; Mathematics ; Botany ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Isotope Analysis in Ecology ; Meat and Animal Product Quality
Authors: Nada Boukelkal | Soufiane Rahal | Redha Rebhi | Mabrouk Hamadache
Source: Journal of Molecular Graphics and Modelling (article, 2024-03-11)
Field: Chemistry — Organic Chemistry

QSPR for the prediction of critical micelle concentration of different classes of surfactants using machine learning algorithms Quantitative structure–activity relationship ; Support vector machine ; Molecular descriptor ; Linear regression ; Artificial neural network ; Critical micelle concentration ; Artificial intelligence ; Machine learning ; Mathematics ; Computer science ; Micelle ; Biological system ; Chemistry ; Organic chemistry Surfactants and Colloidal Systems ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Shen Chao | Wenkang Zhan | M. Y. Li | Zhenyu Sun | Jian Tang | Zhaofeng Wu | Chi Xu | Bo Xu | Chao Zhao | Zhanguo Wang
Source: Journal of Semiconductors (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Development of in situ characterization techniques in molecular beam epitaxy Abstract Ex situ characterization techniques in molecular beam epitaxy (MBE) have inherent limitations, such as being prone to sample contamination and unstable surfaces during sample transfer from the MBE chamber. In recent years, the need for improved accuracy and reliability in measurement has driven the increasing adoption of in situ characterization techniques. These techniques, such as reflection high-energy electron diffraction, scanning tunneling microscopy, and X-ray photoelectron spectroscopy, allow direct observation of film growth processes in real time without exposing the sample to air, hence offering insights into the growth mechanisms of epitaxial films with controlled properties. By combining multiple in situ characterization techniques with MBE, researchers can better understand film growth processes, realizing novel materials with customized properties and extensive applications. This review aims to overview the benefits and achievements of in situ characterization techniques in MBE and their applications for material science research. In addition, through further analysis of these techniques regarding their challenges and potential solutions, particularly highlighting the assistance of machine learning to correlate in situ characterization with other material information, we hope to provide a guideline for future efforts in the development of novel monitoring and control schemes for MBE growth processes with improved material properties. Molecular beam epitaxy ; Characterization (materials science) ; In situ ; Materials science ; Epitaxy ; Nanotechnology ; Optoelectronics ; Chemistry Electronic and Structural Properties of Oxides ; Ga2O3 and related materials ; ZnO doping and properties
Authors: Zhenzhen Dong | Min Zhang | Weirong Li | Fenggang Wen | Guoqing Dong | Lu Zou | Yongqiang Zhang
Source: Materials (article, 2024-08-14)
Field: Materials Science — Materials Chemistry

Development of a Predictive Model for Carbon Dioxide Corrosion Rate and Severity Based on Machine Learning Algorithms Carbon dioxide corrosion is a pervasive issue in pipelines and the petroleum industry, posing substantial risks to equipment safety and longevity. Accurate prediction of corrosion rates and severity is essential for effective material selection and equipment maintenance. This paper begins by addressing the limitations of traditional corrosion prediction methods and explores the application of machine learning algorithms in CO2 corrosion prediction. Conventional models often fail to capture the complex interactions among multiple factors, resulting in suboptimal prediction accuracy, limited adaptability, and poor generalization. To overcome these limitations, this study systematically organized and analyzed the data, performed a correlation analysis of the data features, and examined the factors influencing corrosion. Subsequently, prediction models were developed using six algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), XGBoost, and LightGBM. The results revealed that SVM exhibited the lowest performance on both training and test sets, while RF achieved the best results with R<sup>2</sup> values of 0.92 for the training set and 0.88 for the test set. In the classification of corrosion severity, RF, LightGBM, SVM, and KNN were utilized, with RF demonstrating superior performance, achieving an accuracy of 99% and an F1-score of 0.99. This study highlights that machine learning algorithms, particularly Random Forest, offer substantial potential for predicting and classifying CO2 corrosion. These algorithms provide innovative approaches and valuable insights for practical applications, enhancing predictive accuracy and operational efficiency in corrosion management. Random forest ; Support vector machine ; Machine learning ; Corrosion ; Artificial intelligence ; Computer science ; Predictive modelling ; Generalization ; Decision tree ; Test set ; Gradient boosting ; Algorithm ; Data mining ; Materials science ; Mathematics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Structural Integrity and Reliability Analysis
Authors: Zening Qu | Wei Wang | Yan He
Source: Toxics (article, 2024-01-30)
Field: Chemistry — Inorganic Chemistry

Prediction of Uranium Adsorption Capacity in Radioactive Wastewater Treatment with Biochar Recently, Japan's discharge of wastewater from the Fukushima nuclear disaster into the ocean has attracted widespread attention. To effectively address the challenge of separating uranium, the focus is on finding a healthy and environmentally friendly way to adsorb uranium using biochar. In this paper, a BP neural network is combined with each of the four meta-heuristic algorithms, namely Particle Swarm Optimization (PSO), Differential Evolution (DE), Cheetah Optimization (CO) and Fick's Law Algorithm (FLA), to construct four prediction models for the uranium adsorption capacity in the treatment of radioactive wastewater with biochar: PSO-BP, DE-BP, CO-BP, FLA-BP. The coefficient of certainty (R<sup>2</sup>), error rate and CEC test set are used to judge the accuracy of the model based on the BP neural network. The results show that the Fick's Law Algorithm (FLA) has a better search ability and convergence speed than the other algorithms. The importance of the input parameters is quantitatively assessed and ranked using XGBoost in order to analyze which parameters have a greater impact on the predictions of the model, which indicates that the parameters with the greatest impact are the initial concentration of uranium (C<sub>0</sub>, mg/L) and the mass percentage of total carbon (C, %). To sum up, four prediction models can be applied to study the adsorption of uranium by biochar materials during actual experiments, and the advantage of Fick's Law Algorithm (FLA) is more obvious. The method of model prediction can significantly reduce the radiation risk caused by uranium to human health during the actual experiment and provide some reference for the efficient treatment of uranium wastewater by biochar. Biochar ; Uranium ; Particle swarm optimization ; Adsorption ; Wastewater ; Environmental science ; Chemistry ; Algorithm ; Mathematics ; Environmental engineering ; Physics ; Nuclear physics Radioactive element chemistry and processing ; Radioactivity and Radon Measurements ; Nuclear and radioactivity studies