jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Craig Warren Davis | Louise Camenzuli | Aaron D. Redman
Source: Environmental Toxicology and Chemistry (article, 2022-03-09)
Field: Chemistry — Analytical Chemistry

Predicting Primary Biodegradation of Petroleum Hydrocarbons in Aquatic Systems: Integrating System and Molecular Structure Parameters using a Novel Machine-Learning Framework Quantitative structure-property relationship (QSPR) models for predicting primary biodegradation of petroleum hydrocarbons have been previously developed. These models use experimental data generated under widely varied conditions, the effects of which are not captured adequately within model formalisms. As a result, they exhibit variable predictive performance and are unable to incorporate the role of study design and test conditions on the assessment of environmental persistence. To address these limitations, a novel machine-learning System-Integrated Model (HC-BioSIM) is presented, which integrates chemical structure and test system variability, leading to improved prediction of primary disappearance time (DT50) values for petroleum hydrocarbons in fresh and marine water. An expanded, highly curated database of 728 experimental DT50 values (181 unique hydrocarbon structures compiled from 13 primary sources) was used to develop and validate a supervised model tree machine-learning model. Using relatively few parameters (6 system and 25 structural parameters), the model demonstrated significant improvement in predictive performance (root mean square error = 0.26, R<sup>2</sup> = 0.67) over existing QSPR models. The model also demonstrated improved accuracy of persistence (P) categorization (i.e., "Not P/P/vP"), with an accuracy of 96.8%, and false-positive and -negative categorization rates of 0.4% and 2.7%, respectively. This significant improvement in DT50 prediction, and subsequent persistence categorization, validates the need for models that integrate experimental design and environmental system parameters into biodegradation and persistence assessment. Environ Toxicol Chem 2022;41:1359-1369. © 2022 ExxonMobil Biomedical Sciences, Inc. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. Biodegradation ; Petroleum ; Primary (astronomy) ; Aquatic ecosystem ; Biochemical engineering ; Aquatic environment ; Environmental science ; Environmental chemistry ; Chemistry ; Ecology ; Biology ; Engineering ; Organic chemistry Petroleum Processing and Analysis ; Microbial bioremediation and biosurfactants ; Computational Drug Discovery Methods
Authors: Ryo Kawada | Katsuhiro Endo | Daisuke Yuhara | Kenji Yasuoka
Source: Soft Matter (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

MD-GAN with multi-particle input: the machine learning of long-time molecular behavior from short-time MD data Molecular dynamics simulation is a method of investigating the behavior of molecules, which is useful for analyzing a variety of structural and dynamic properties and mechanisms of phenomena. However, the huge computational cost of large-scale and long-time simulations is an enduring problem that must be addressed. MD-GAN is a machine learning-based method that can evolve part of the system at any time step, accelerating the generation of molecular dynamics data [Endo <i>et al.</i>, <i>Proceedings of the AAAI Conference on Artificial Intelligence</i>, 2018, <b>32</b>]. For the accurate prediction of MD-GAN, sufficient information on the dynamics of a part of the system should be included with the training data. Therefore, the selection of the part of the system is important for efficient learning. In a previous study, only one particle (or vector) of each molecule was extracted as part of the system. The effectiveness of adding information from other particles to the learning process is investigated in this study. When the dynamics of three particles of each molecule were used in the polyethylene experiment, the diffusion was successfully predicted using the training data with a time length of approximately 40%, compared to the single-particle input. Surprisingly, the unobserved transition of diffusion in the training data was also predicted using this method. The reduced cost for the generation of training MD data achieved in this study is useful for accelerating MD-GAN. Molecular dynamics ; Diffusion ; Computer science ; Process (computing) ; Particle (ecology) ; Artificial intelligence ; Machine learning ; Statistical physics ; Simulation ; Physics ; Chemistry ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Sukriti Singh | Raghavan B. Sunoj
Source: iScience (article, 2022-06-22)
Field: Materials Science — Materials Chemistry

A transfer learning approach for reaction discovery in small data situations using generative model Sustainable practices in chemical sciences can be better realized by adopting interdisciplinary approaches that combine the advantages of machine learning (ML) on the initially acquired small data in reaction discovery. Developing new reactions generally remains heuristic and even time and resource intensive. For instance, synthesis of fluorine-containing compounds, which constitute ∼20% of the marketed drugs, relies on deoxyfluorination of abundantly available alcohols. Herein, we demonstrate the use of a recurrent neural network-based deep generative model built on a library of just 37 alcohols for effective learning and exploration of the chemical space. The proof-of-concept ML model is able to generate good quality, synthetically accessible, higher-yielding novel alcohol molecules. This protocol would have superior utility for deployment into a practical reaction discovery pipeline. Chemical space ; Computer science ; Generative grammar ; Pipeline (software) ; Artificial intelligence ; Heuristic ; Drug discovery ; Machine learning ; Resource (disambiguation) ; Transfer of learning ; Artificial neural network ; Data science ; Biochemical engineering ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Raheleh Ghouchan Nezhad Noor Nia | Mehrdad Jalali | Matthias Mail | Yulia Ivanisenko | Christian Kübel
Source: ACS Omega (article, 2022-04-04)
Field: Materials Science — Materials Chemistry

Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network There is a growing trend toward the use of interaction network methods and algorithms, including community-based detection methods, in various fields of science. The approach is already used in many applications, for example, in social sciences and health informatics to analyze behavioral patterns during the COVID-19 pandemic, protein-protein networks in biological sciences, agricultural science, economy, and so forth. This paper attempts to build interaction networks based on high-entropy alloy (HEA) descriptors in order to discover HEA communities with similar functionality. In addition, these communities could be leveraged to discover new alloys not yet included in the data set without any experimental laboratory effort. This research has been carried out using two community detection algorithms, the Louvain algorithm and the enhanced particle swarm optimization (PSO) algorithm. The data set, which is used in this paper, includes 90 HEAs and 6 descriptors. The results reveal 13 alloy communities, and the accuracy of the results is validated by the modularity. The experimental results show that the method with the PSO-based community detection algorithm can achieve alloy communities with an average accuracy improvement of 0.26 compared to the Louvain algorithm. Furthermore, some characteristics of HEAs, for example, their phase composition, could be predicted by the extracted communities. Also, the HEA phase composition has been predicted by the proposed method and achieved about 93% precision. Computer science ; Particle swarm optimization ; Entropy (arrow of time) ; Machine learning ; Set (abstract data type) ; Data mining ; Artificial intelligence Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Yan Cao | Ye Sheng | Xin Li | Lili Xi | Jiong Yang
Source: Frontiers in Materials (article, 2022-02-25)
Field: Materials Science — Materials Chemistry

Application of Materials Genome Methods in Thermoelectrics Materials genome methods have played an essential role in accelerating the discovery of high-performance novel materials, and include high-throughput calculation, database construction, and machine learning. Over the past decades, these approaches have been increasingly used in lithium battery materials, solar cells, transparent conductors, and thermoelectrics. Thermoelectrics are functional materials that can directly convert electricity into heat and vice versa, offering new ideas for conventional power generation and refrigeration. The application of high-throughput methods can achieve more efficient screening of new thermoelectric materials and accelerate experimental development. This review summarizes the recent progress in the application of materials genome methods for different thermoelectric materials, such as half-Heuslers, diamond-like structures, oxides, and other materials. Finally, current advances in machine learning for thermoelectrics are discussed. The progress of the theoretical design of thermoelectrics has driven the development of high-performance thermoelectrics. Thermoelectric materials ; Nanotechnology ; Electricity ; Engineering physics ; Thermoelectric effect ; Materials science ; Computer science ; Process engineering ; Engineering ; Electrical engineering ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Junho Jung | Seok-Jae Lee | Hee-Soo Kim
Source: Materials (article, 2022-10-07)
Field: Materials Science — Materials Chemistry

Estimation of Average Grain Size from Microstructure Image Using a Convolutional Neural Network In this study, the average grain size was evaluated from a microstructure image using a convolutional neural network. Since the grain size in a microstructure image can be directly measured and verified in the original image, unlike the chemical composition or mechanical properties of material, it is more appropriate to validate the training results quantitatively. An analysis of microstructure images, such as grain size, can be performed manually or using image analysis software; however, it is expected that the analysis would be simpler and faster with machine learning. Microstructure images were created using a phase-field simulation, and machine learning was carried out with a convolutional neural network model. The relationship between the microstructure image and the average grain size was not judged by classification, as the goal was to have different results for each microstructure using regression. The results showed high accuracy within the training range. The average grain sizes of experimental images with explicit grain boundary were well estimated by the network. The mid-layer image was analyzed to examine how the network understood the input microstructure image. The network seemed to recognize the curvatures of the grain boundaries and estimate the average grain size from these curvatures. Microstructure ; Grain size ; Convolutional neural network ; Artificial intelligence ; Artificial neural network ; Image (mathematics) ; Materials science ; Image processing ; Pattern recognition (psychology) ; Computer science ; Composite material X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels
Authors: Bofeng Shi | Yumei Zhou | Daqing Fang | Yuan Tian | Xiangdong Ding | Jun Sun | Turab Lookman | Dezhen Xue
Source: Journal of Materials Informatics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Estimating the performance of a material in its service space via Bayesian active learning: a case study of the damping capacity of Mg alloys In addition to being determined by its chemical composition and processing conditions, the performance of a material is also affected by the variables of its service space, including temperature, pressure, and frequency. A rapid means to estimate the performance of a material in its service space is urgently required to accelerate the screening of materials with targeted performance. In the present study, a materials informatics approach is proposed to rapidly predict performance within a service space based on existing data. We utilize an active learning loop, which employs an ensemble machine learning method to predict the performance, followed by a Bayesian experimental design to minimize the number of experiments for refinement and validation. This approach is demonstrated by predicting the damping properties of a ZE62 magnesium alloy in a service space defined by frequency, strain amplitude, and temperature based on the available data for other magnesium alloys. Several utility functions that recommend a particular experiment to refine the estimates of the service space are used and compared. In particular, the standard deviation is found to reduce the prediction error most efficiently. After augmenting the database with nine new experimental measurements, the uncertainties associated with the predicted damping capacities are largely reduced. Our method allows us to forecast the properties in the service space of a given material by rapid refinement of the predictions via experiment measurements. Bayesian optimization ; Materials informatics ; Bayesian probability ; Space (punctuation) ; Service (business) ; Computer science ; Standard deviation ; Chemical space ; Machine learning ; Materials science ; Artificial intelligence ; Mathematics ; Statistics Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Metal and Thin Film Mechanics
Authors: J. Zhang | Feng Zhang | Dingxin Wei | Laijun Liu | Xiong Liu | Dangqi Fang | Guoxu Zhang | Xi Chen | Dawei Wang
Source: Physical review. B./Physical review. B (article, 2022-03-28)
Field: Materials Science — Materials Chemistry

Structural phase transition of monochalcogenides investigated with machine learning As machine learning becomes increasingly important in science and engineering, it holds the promise to provide a universal approach applicable to various systems to investigate their crystalline phase transitions. Here, we build and train accurate artificial neural networks that can distinguish tiny energy difference, which is crucial to predict the crystalline phase transitions. Employing the trained artificial neural networks in Monte Carlo simulations as the surrogate energy function, we apply this approach to monochalcogenides, including bulk and two-dimensional monolayer SnTe and GeTe, investigating their crystalline phase transitions. The machine-learning approach, when viewed as providing a universal mathematical structure, can be transferred to the investigation of other materials when the training data set generated with ab initio methods are available. Moreover, the machine-learning approach resolves the difficulties associated with constructing the effective Hamiltonian for monochalcogenides, showing great potential with its accuracy and efficiency. Artificial neural network ; Artificial intelligence ; Phase transition ; Ab initio ; Computer science ; Hamiltonian (control theory) ; Machine learning ; Statistical physics ; Materials science ; Condensed matter physics ; Physics ; Mathematics ; Quantum mechanics ; Mathematical optimization Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Ming Xia | Marie‐Christine Record | Pascal Boulet
Source: Materials (article, 2022-12-27)
Field: Materials Science — Materials Chemistry

Investigation of PbSnTeSe High-Entropy Thermoelectric Alloy: A DFT Approach Thermoelectric materials have attracted extensive attention because they can directly convert waste heat into electric energy. As a brand-new method of alloying, high-entropy alloys (HEAs) have attracted much attention in the fields of materials science and engineering. Recent researches have found that HEAs could be potentially good thermoelectric (TE) materials. In this study, special quasi-random structures (SQS) of PbSnTeSe high-entropy alloys consisting of 64 atoms have been generated. The thermoelectric transport properties of the highest-entropy PbSnTeSe-optimized structure were investigated by combining calculations from first-principles density-functional theory and on-the-fly machine learning with the semiclassical Boltzmann transport theory and Green-Kubo theory. The results demonstrate that PbSnTeSe HEA has a very low lattice thermal conductivity. The electrical conductivity, thermal electronic conductivity and Seebeck coefficient have been evaluated for both n-type and p-type doping. N-type PbSnTeSe exhibits better power factor (PF = S<sup>2</sup>σ) than p-type PbSnTeSe because of larger electrical conductivity for n-type doping. Despite high electrical thermal conductivities, the calculated ZT are satisfactory. The maximum ZT (about 1.1) is found at 500 K for n-type doping. These results confirm that PbSnTeSe HEA is a promising thermoelectric material. Alloy ; Materials science ; Thermoelectric effect ; High entropy alloys ; Thermodynamics ; Statistical physics ; Metallurgy ; Physics Advanced Thermoelectric Materials and Devices ; Chalcogenide Semiconductor Thin Films ; Phase-change materials and chalcogenides
Authors: Feng Yan | Kai Song | Liang Gao | Xuejun Wei
Source: Materials Today Communications (article, 2022-01-29)
Field: Materials Science — Metals and Alloys

DCLF: A divide-and-conquer learning framework for the predictions of steel hardness using multiple alloy datasets Materials science ; Alloy ; Cluster analysis ; Process (computing) ; Machine learning ; Artificial intelligence ; Computer science ; Metallurgy Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science
Authors: Alexandre Mallet | Cyrille Charnier | Éric Latrille | Ryad Bendoula | Jean‐Michel Roger | Jean‐Philippe Steyer
Source: Water Research (article, 2022-10-29)
Field: Chemistry — Analytical Chemistry

Fast and robust NIRS-based characterization of raw organic waste: Using non-linear methods to handle water effects Partial least squares regression ; Linear regression ; Support vector machine ; Anaerobic digestion ; Test set ; Biological system ; Linear model ; Artificial neural network ; Convolutional neural network ; Computer science ; Methane ; Artificial intelligence ; Machine learning ; Process engineering ; Chemistry ; Engineering Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Daniele Lanzoni | Fabrizio Rovaris | Francesco Montalenti
Source: Scientific Reports (article, 2022-03-08)
Field: Materials Science — Materials Chemistry

Machine learning potential for interacting dislocations in the presence of free surfaces Computing the total energy of a system of N interacting dislocations in the presence of arbitrary free surfaces is a difficult task, requiring Finite Element (FE) numerical calculations. Worst, high accuracy requires very fine meshes in the proximity of each dislocation core. Here we show that FE calculations can be conveniently replaced by a Machine Learning (ML) approach. After formulating the elastic problem in terms of one and two-body terms only, we use Sobolev training to obtain consistent information on both energy and forces, fitted using a feed-forward neural network (NN) architecture. As an example, we apply the proposed methodology to corrugated, heteroepitaxial semiconductor films, searching for the minimum-energy dislocation distributions by using Monte Carlo. Importantly, the presence of an interaction cutoff allows for the application of the method to systems of different sizes without the need to repeat training. Millions of energy evaluations are performed, a task which would have been impossible by brute-force FE calculations. Finally, we show how forces can be exploited in running 2D ML-based dislocation dynamics simulations. Dislocation ; Polygon mesh ; Computer science ; Task (project management) ; Monte Carlo method ; Energy (signal processing) ; Artificial neural network ; Finite element method ; Cutoff ; Core (optical fiber) ; Statistical physics ; Artificial intelligence ; Algorithm ; Computational science ; Physics ; Mathematics ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Jinkwan Kwoen | Yasuhiko Arakawa
Source: Journal of Crystal Growth (article, 2022-06-27)
Field: Materials Science — Materials Chemistry

Multiclass classification of reflection high-energy electron diffraction patterns using deep learning Reflection high-energy electron diffraction ; Electron diffraction ; Molecular beam epitaxy ; Reflection (computer programming) ; Diffraction ; Optics ; Sample (material) ; Chemistry ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Materials science ; Epitaxy ; Physics ; Nanotechnology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Semiconductor materials and devices
Authors: Yijun Lu | Baochuan Wang | Shuyue Chen | Bo Yang
Source: Molecular Catalysis (article, 2022-08-10)
Field: Materials Science — Materials Chemistry

Quantifying the error propagation in microkinetic modeling of catalytic reactions with model-predicted binding energies Catalysis ; Binding energy ; Kinetic energy ; Chemistry ; Scaling ; Propagation of uncertainty ; Transition state ; Activation energy ; Computational chemistry ; Statistical physics ; Thermodynamics ; Reaction rate ; Biological system ; Physical chemistry ; Computer science ; Physics ; Algorithm ; Quantum mechanics ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts