jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kaushik Sivaramakrishnan | Joy H. Tannous | Vignesh Chandrasekaran
Source: Industrial & Engineering Chemistry Research (article, 2023-10-19)
Field: Chemistry — Analytical Chemistry

Prediction of Thermogravimetric Data for Asphaltenes Extracted from Deasphalted Oil Using Machine Learning Techniques Thermogravimetric analysis (TGA) has been extensively used in the bitumen literature to investigate its thermal stability and various stages of thermal decomposition. The primary aim of these studies has been to calculate the kinetic parameters, such as activation energy and the pre-exponential factor of each thermal event. However, in our current paper, we explore the application of three machine learning (ML) techniques, namely, support vector regression (SVR), random forest (RF), and gradient booster regression (GBR), to predict the TGA data for the asphaltenes extracted from the feed and products of visbreaking of three types of materials: (i) deasphalted oil (DAO), (ii) DAO doped with 5.55 wt % indene, and (iii) DAO doped with 11.11 wt % indene. The addition of indene was shown to significantly affect the free-radical chemistry of DAO in a previous work, and the key contribution of our work in this paper was to minimize the requirement of the TGA instrument to obtain the mass loss curves by employing ML techniques on available experimental data. This will reduce the human errors involved in sample preparation and data collection as well as decrease the process time in obtaining the TGA data as compared to experimentation. We observed that the regression techniques based on decision trees, i.e., RF and GBR, showed the best performance and highest prediction accuracy of >0.99 for predicting the TGA data of the asphaltenes extracted from the feed and products obtained by reacting the feedstocks at visbreaking reaction times of 30, 45, and 60 min. A number of inputs were considered for the ML models, such as the temperature of the TGA chamber and sample, heat supplied to the sample, visbreaking time, and time spent inside the TGA chamber. The novelty of our work lies in the fact that no previous study has reproduced the TGA data for asphaltenes extracted from DAO and indene-added DAO and their visbroken products through ML approaches, and we believe that the results of this work will help in fastening the process times in the heavy oil industry by eliminating the need for offline measuring instruments. Thermogravimetric analysis ; Asphaltene ; Chemistry ; Indene ; Computer science ; Process engineering ; Organic chemistry Petroleum Processing and Analysis ; Spectroscopy and Chemometric Analyses ; Hydrocarbon exploration and reservoir analysis
Authors: Darwin Barayang Putungan | Su Shaosen | Liang Gao | Ankit Goyal | Shi‐Hsin Lin | Akhil Garg
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Prediction of sodium binding energy on 2D VS<sub>2</sub><i>via</i> machine learning: a robust accompanying method to <i>ab initio</i> random structure searching In this work, we employed the back-propagation neural network (BPNN) for predicting the energetics of different sodium adsorption phases on the VS<sub>2</sub> monolayer generated <i>via ab initio</i> random structure searching (AIRSS). Two key adsorption features were identified as inputs: the average Na-Na distance and a defined adsorption feature marker that indicates the number of nearest-neighbor pairs within a sodium cluster. Using the stoichiometric structure Na<sub>0.5</sub>VS<sub>2</sub> as the test system, we first generated 50 random sensible structures <i>via</i> AIRSS and optimized them <i>via</i> density functional theory (DFT) calculations to obtain the sodium binding energy per atom. From these, 30 were utilized to train 3000 BPNNs with varying numbers of neurons and types of activation functions. The remaining 20 were employed to verify the generalization of the best identified BPNN model for the considered Na<sub>0.5</sub>VS<sub>2</sub> system. The calculated mean absolute error for the predicted sodium binding energy per atom is smaller than 0.1 eV. This suggests that the identified BPNN model was able to predict the sodium binding energy per atom on VS<sub>2</sub> with outstanding accuracy. Our results demonstrated that with the assistance of BPNN, it is possible to perform AIRSS with hundreds of random sensible structures without relying solely on DFT calculations. The uniqueness of this method lies on the utilization of a very large number of BPNN models to be trained by a relatively small number of structures. This is particularly very useful for large systems wherein the data come from DFT calculations, which is computationally expensive. Moreover, with the assistance of machine learning, the theoretical estimation of important metal-ion battery metrics such as specific energy capacity and open circuit voltage <i>via</i> AIRSS could be made more accurate and reliable. Ab initio ; Energy (signal processing) ; Sodium ; Binding energy ; Computational chemistry ; Random forest ; Statistical physics ; Chemistry ; Computer science ; Materials science ; Chemical physics ; Physics ; Machine learning ; Atomic physics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Reuben Adewuyi | J.O. Aweda | Faith Ogunwoye | Peter Omoniyi | Tien‐Chien Jen
Source: Metallurgical and Materials Engineering (article, 2023-12-16)
Field: Materials Science — Materials Chemistry

Prediction of Impact Strength of TIG Welded Cr-Mo Steel Using Artificial Neural Networks Welding is a critical and energy-intensive process with significant importance in the manufacturing industry, enabling the creation of joints capable of withstanding diverse loads without failure. Accurate prediction of welding parameters' effects on the thermal cycle and strength of metals during and after welding is essential to ensure the reliability of welds. This study investigates the influence of welding parameters such as welding current, material thickness, number of weld passes, and electrode diameter on the impact strength of Cr-Mo steel bars. Pure tungsten with 2% thoriated Tungsten Inert Gas (TIG) electrodes was used to join the metal sheets autogenously. Artificial neural network (ANN) was used in creating the model that predicts the impact strength of the steel. Sample with welding parameters of 15 mm thickness, 90 A current, 3 weld passes, and Ø2.4 mm electrode size exhibited the highest impact strength. Furthermore, the analysis of variance (ANOVA) results show that the material thickness and number of weld passes contribute significantly to the impact strength of the steel. The ANN model trained by the Levenberg-Marquardt algorithm had an average training dataset root mean square error (RSME) of 4.12%. This study contributes to the reliability and performance of welded joints in various applications. Materials science ; Artificial neural network ; Gas tungsten arc welding ; Welding ; Izod impact strength test ; Metallurgy ; Composite material ; Ultimate tensile strength ; Artificial intelligence ; Computer science ; Arc welding Metal Alloys Wear and Properties ; Surface Treatment and Residual Stress ; Advanced machining processes and optimization
Authors: Sergei Manzhos | Manabu Ihara
Source: The Journal of Chemical Physics (article, 2023-12-01)
Field: Materials Science — Materials Chemistry

A controlled study of the effect of deviations from symmetry of the potential energy surface (PES) on the accuracy of the vibrational spectrum computed with collocation Symmetry, in particular permutational symmetry, of a potential energy surface (PES) is a useful property in quantum chemical calculations. It facilitates, in particular, state labelling and identification of degenerate states. In many practically important applications, however, these issues are unimportant. The imposition of exact symmetry and the perception that it is necessary create additional methodological requirements narrowing or complicating algorithmic choices that are thereby biased against methods and codes that by default do not incorporate symmetry, including most off-the-shelf machine learning methods that cannot be directly used if exact symmetry is demanded. By introducing symmetric and unsymmetric errors into the PES of H2CO in a controlled way and computing the vibrational spectrum with collocation using symmetric and nonsymmetric collocation point sets, we show that when the deviations from an ideal PES are random, imposition of exact symmetry does not bring any practical advantages. Moreover, a calculation ignoring symmetry may be more accurate. We also compare machine-learned PESs with and without symmetrization and demonstrate that there is no advantage of imposing exact symmetry for the accuracy of the vibrational spectrum. Symmetry (geometry) ; Degenerate energy levels ; Symmetrization ; Rotational symmetry ; Computer science ; Statistical physics ; Mathematics ; Theoretical physics ; Quantum mechanics ; Physics ; Mathematical analysis ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Corentin van den Broek d'Obrenan | F. Galliano | Jeremy Minton | Viktor Botev | R. Wu
Source: Journal of Interdisciplinary Methodologies and Issues in Sciences (article, 2023-08-12)
Field: Chemistry — Physical and Theoretical Chemistry

Searching for carriers of the diffuse interstellar bands across disciplines, using Natural Language Processing The explosion of scientific publications overloads researchers with information. This is even more dramatic for interdisciplinary studies, where several fields need to be explored. A tool to help researchers overcome this is Natural Language Processing (NLP): a machine-learning (ML) technique that allows scientists to automatically synthesize information from many articles. As a practical example, we have used NLP to conduct an interdisciplinary search for compounds that could be carriers for Diffuse Interstellar Bands (DIBs), a long-standing open question in astrophysics. We have trained a NLP model on a corpus of 1.5 million cross-domain articles in open access, and fine-tuned this model with a corpus of astrophysical publications about DIBs. Our analysis points us toward several molecules, studied primarily in biology, having transitions at the wavelengths of several DIBs and composed of abundant interstellar atoms. Several of these molecules contain chromophores, small molecular groups responsible for the molecule's colour, could be promising candidate carriers. Identifying viable carriers demonstrates the value of using NLP to tackle open scientific questions, in an interdisciplinary manner. Computer science ; Domain (mathematical analysis) ; Natural language processing ; Artificial intelligence ; Physics ; Mathematics History and advancements in chemistry ; Inorganic Fluorides and Related Compounds
Authors: Anirban Chandra | Troy D. Loeffler | Henry Chan | Xiaoyu Wang | G. B. Stephenson | Michael J. Servis | Subramanian K. R. S. Sankaranarayanan
Source: The Journal of Chemical Physics (article, 2023-07-11)
Field: Materials Science — Materials Chemistry

Reinforcement learning based hybrid bond-order coarse-grained interatomic potentials for exploring mesoscale aggregation in liquid–liquid mixtures Exploring mesoscopic physical phenomena has always been a challenge for brute-force all-atom molecular dynamics simulations. Although recent advances in computing hardware have improved the accessible length scales, reaching mesoscopic timescales is still a significant bottleneck. Coarse-graining of all-atom models allows robust investigation of mesoscale physics with a reduced spatial and temporal resolution but preserves desired structural features of molecules, unlike continuum-based methods. Here, we present a hybrid bond-order coarse-grained forcefield (HyCG) for modeling mesoscale aggregation phenomena in liquid-liquid mixtures. The intuitive hybrid functional form of the potential offers interpretability to our model, unlike many machine learning based interatomic potentials. We parameterize the potential with the continuous action Monte Carlo Tree Search (cMCTS) algorithm, a reinforcement learning (RL) based global optimizing scheme, using training data from all-atom simulations. The resulting RL-HyCG correctly describes mesoscale critical fluctuations in binary liquid-liquid extraction systems. cMCTS, the RL algorithm, accurately captures the mean behavior of various geometrical properties of the molecule of interest, which were excluded from the training set. The developed potential model along with the RL-based training workflow could be applied to explore a variety of other mesoscale physical phenomena that are typically inaccessible to all-atom molecular dynamics simulations. Mesoscale meteorology ; Mesoscopic physics ; Statistical physics ; Reinforcement learning ; Multiscale modeling ; Computer science ; Molecular dynamics ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry ; Quantum mechanics Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Theoretical and Computational Physics
Authors: Hossein Ravanbakhsh | Razyeh Behbahani | Hamidreza Yazdani Sarvestani | Elham Kiyani | Meysam Rahmat | Mikko Karttunen | Behnam Ashrafi
Source: Advanced Engineering Materials (article, 2023-05-01)
Field: Materials Science — Ceramics and Composites

Combining Finite Element and Machine Learning Methods to Predict Structures of Architectured Interlocking Ceramics A smart method, a combination of finite element and machine learning techniques, has been developed to predict the thermo-mechanical responses of the architectured ceramics as described in the article, number 2201408, by Hamidreza Yazdani Sarvestani, Mikko Karttunen, Behnam Ashrafi, and co-workers. The developed strategy is able to find the high-performance ceramics subjected to various thermo-mechanical loading with a reduced computational cost. These materials can be manufactured using a subtractive manufacturing scalable platform. Materials science ; Finite element method ; Ceramic ; Interlocking ; Scalability ; Mechanical engineering ; Composite material ; Structural engineering ; Computer science ; Engineering Advanced ceramic materials synthesis ; Mineral Processing and Grinding
Authors: Alexandra Wörnhör | Matthias Demant | Henri Vahlman | Stefan Rein
Source: EPJ Photovoltaics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

A self-consistent hybrid model connects empirical and optical models for fast, non-destructive inline characterization of thin, porous silicon layers Epitaxially-grown wafers on top of sintered porous silicon are a material-efficient wafer production process, that is now being launched into mass production. This production process makes the material-expensive sawing procedure obsolete since the wafer can be easily detached from its seed substrate. With high-throughput inline production processes, fast and reliable evaluation processes are crucial. The quality of the porous layers plays an important role regarding a successful detachment. Therefore, we present a fast and non-destructive investigation algorithm of thin, porous silicon layers. We predict the layer parameters directly from inline reflectance data by using a convolutional neural network (CNN), which is inspired by a comprehensive optical modelling approach from literature. There, a numerical fitting approach on reflection curves calculated with a physical model is performed. By adding the physical model to the CNN, we create a hybrid model, that not only predicts layer parameters, but also recalculates reflection curves. This allows a consistency check for a self-supervised network optimization. Evaluation on experimental data shows a high similarity with Scanning Electron Microscopy (SEM) measurements. Since parallel computation is possible with the CNN, 30.000 samples can be evaluated in roughly 100 ms. Wafer ; Silicon ; Materials science ; Layer (electronics) ; Computer science ; Characterization (materials science) ; Substrate (aquarium) ; Porous silicon ; Convolutional neural network ; Computation ; Reflection (computer programming) ; Throughput ; Optoelectronics ; Algorithm ; Artificial intelligence ; Nanotechnology ; Geology Silicon Nanostructures and Photoluminescence ; Thin-Film Transistor Technologies ; Semiconductor materials and devices
Authors: Qing Miao | Quanzi Yuan
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine learning coarse-grained models of dissolutive wetting: a droplet on soluble surfaces Dissolutive wetting is not only a key problem in application fields such as energy, medicine, micro-devices and <i>etc.</i>, but also a frontier issue of academic research. As an important tool for exploring the micro-mechanisms of dissolutive wetting, molecular dynamics simulations are limited by simulation scale and force field parameters. Thus, artificial intelligence is introduced into the multi-scale simulation framework to tackle such challenges. By combining density functional theory, molecular dynamics simulations and experiments, we obtain a coarse-grained model of the glucose-water dissolution pair. Furthermore, the structure of the solid molecules and the hydration shell near the solute particles are calculated by quantum mechanics/molecular mechanics to verify the accuracy of the model. Finally, the applicability of the coarse-grained model in dissolutive wetting is proven by experimental results. We believe our machine learning method not only lays a foundation for exploring the micro-mechanisms of dissolutive wetting, but also provides a general approach for obtaining the force field parameters of different systems. Wetting ; Molecular dynamics ; Force field (fiction) ; Field (mathematics) ; Foundation (evidence) ; Computer science ; Statistical physics ; Scale (ratio) ; Materials science ; Nanotechnology ; Physics ; Artificial intelligence ; Chemistry ; Thermodynamics ; Computational chemistry ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; nanoparticles nucleation surface interactions
Authors: Andrea Rizzi | Paolo Carloni | Michele Parrinello
Source: arXiv (Cornell University) (preprint, 2023-02-15)
Field: Materials Science — Materials Chemistry

Multimap targeted free energy estimation We present a new method to compute free energies at a quantum mechanical (QM) level of theory from molecular simulations using cheap reference potential energy functions, such as force fields. To overcome the poor overlap between the reference and target distributions, we generalize targeted free energy perturbation (TFEP) to employ multiple configuration maps. While TFEP maps have been obtained before from an expensive training of a normalizing flow neural network (NN), our multimap estimator allows us to use the same set of QM calculations to both optimize the maps and estimate the free energy, thus removing almost completely the overhead due to training. A multimap extension of the multistate Bennett acceptance ratio estimator is also derived for cases where samples from two or more states are available. Furthermore, we propose a one-epoch learning policy that can be used to efficiently avoid overfitting when computing the loss function is expensive compared to generating data. Finally, we show how our multimap approach can be combined with enhanced sampling strategies to overcome the pervasive problem of poor convergence due to slow degrees of freedom. We test our method on the HiPen dataset of drug-like molecules and fragments, and we show that it can accelerate the calculation of the free energy difference of switching from a force field to a DFTB3 potential by about 3 orders of magnitude compared to standard FEP and by a factor of about 8 compared to previously published nonequilibrium calculations. Overfitting ; Estimator ; Computer science ; Free energy perturbation ; Convergence (economics) ; Energy (signal processing) ; Algorithm ; Artificial neural network ; Quantum ; Degrees of freedom (physics and chemistry) ; Force field (fiction) ; Mathematical optimization ; Function (biology) ; Statistical physics ; Artificial intelligence ; Mathematics ; Physics ; Molecular dynamics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics
Authors: Liwei Fu | Xiwei Wang | Karsten Frenner | Stephan Reichelt
Source:  (article, 2023-08-10)
Field: Materials Science — Surfaces, Coatings and Films

Comparative analysis of grating reconstruction: deep learning versus Levenberg-Marquardt methods Model-based optical scatterometry is a widely utilized non-destructive measuring technique in semiconductor manufacturing for retrieving features on wafers. It offers an attractive solution for quality control and process monitoring. However, the increasing complexity of 3D nanoscale device structures presents significant challenges for optical scatterometry. To address these challenges, it is crucial to integrate different methods and create a hybrid metrology approach that could encompass measurements, modeling, and data analysis techniques. To tackle this objective, we explore in this study two alternative approaches for parameter reconstruction, distinct from the conventional library search method. The first approach utilizes a neural network based on a Resnet architecture, while the second approach employs the Levenberg-Marquardt algorithm, a nonlinear least square fitting technique. By performing a comparative analysis of the two methods, we propose a strategy to combine them for accurate and efficient parameter reconstructions. Levenberg–Marquardt algorithm ; Computer science ; Semiconductor device fabrication ; Artificial neural network ; Process (computing) ; Artificial intelligence ; Metrology ; Nonlinear system ; Grating ; Machine learning ; Wafer ; Computer engineering ; Materials science ; Optics ; Nanotechnology Optical Coatings and Gratings ; Advanced Measurement and Metrology Techniques ; Photonic and Optical Devices
Authors: Fleming Kretschmer | Eva-Maria Harrieder | Martin Hoffmann | Sebastian Böcker | Michael Witting
Source: ChemRxiv (preprint, 2023-07-05)
Field: Chemistry — Spectroscopy

RepoRT: A comprehensive repository for small molecule retention times Liquid chromatography is frequently employed for the separation of metabolites and other small molecules. Prediction of retention times via machine learning methods can assist compound annotation. Yet, transferable predictions are intrinsically complicated for novel compounds and novel chromatographic conditions because retention times depend both on compound structure and the employed chromatographic system. We present RepoRT, the first repository for retention time data. RepoRT presently contains 373 datasets, 8809 unique compounds, and 88,325 retention time entries measured on 49 different chromatographic columns using varying eluents, flow rates, and temperatures. We put particular effort on making RepoRT “machine learning-ready”: We performed an extensive manual curation, cleaning and completion of the available data; we developed automated methods for data validation during upload; we collected more than 45,000 different columns of different vendors with their lengths, particle sizes, inner diameters, and pore sizes to create a database with normalized column names; and, we ensured that features required for transferable predictions, such as parameters numerically describing the selectivity of chromatographic columns, are readily available. For version control and reproducible research, RepoRT is hosted on GitHub. Retention time ; Column (typography) ; Upload ; Chromatography ; Chromatographic separation ; Computer science ; Chemistry ; High-performance liquid chromatography Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods