jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shreyas Honrao | Xin Yang | Balachandran Radhakrishnan | Shigemasa Kuwata | Hideyuki Komatsu | Atsushi Ohma | Maarten Sierhuis | John W. Lawson
Source: Scientific Reports (article, 2021-08-13)
Field: Materials Science — Materials Chemistry

Discovery of novel Li SSE and anode coatings using interpretable machine learning and high-throughput multi-property screening All-solid-state batteries with Li metal anode can address the safety issues surrounding traditional Li-ion batteries as well as the demand for higher energy densities. However, the development of solid electrolytes and protective anode coatings possessing high ionic conductivity and good stability with Li metal has proven to be a challenge. Here, we present our informatics approach to explore the Li compound space for promising electrolytes and anode coatings using high-throughput multi-property screening and interpretable machine learning. To do this, we generate a database of battery-related materials properties by computing [Formula: see text] migration barriers and stability windows for over 15,000 Li-containing compounds from Materials Project. We screen through the database for candidates with good thermodynamic and electrochemical stabilities, and low [Formula: see text] migration barriers, identifying promising new candidates such as [Formula: see text]N, [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], among others. We train machine learning models, using ensemble methods, to predict migration barriers and oxidation and reduction potentials of these compounds by engineering input features that ensure accuracy and interpretability. Using only a small number of features, our gradient boosting regression models achieve [Formula: see text] values of 0.95 and 0.92 on the oxidation and reduction potential prediction tasks, respectively, and 0.86 on the migration barrier prediction task. Finally, we use Shapley additive explanations and permutation feature importance analyses to interpret our machine learning predictions and identify materials properties with the largest impact on predictions in our models. We show that our approach has the potential to enable rapid discovery and design of novel solid electrolytes and anode coatings. Algorithm ; Computer science ; Machine learning ; Materials science ; Artificial intelligence Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advancements in Battery Materials
Authors: Elisabeth J. Schiessler | Tim Würger | Sviatlana V. Lamaka | Robert H. Meißner | Christian J. Cyron | Mikhail L. Zheludkevich | Christian Feiler | Roland C. Aydin
Source: npj Computational Materials (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models Abstract The degradation behaviour of magnesium and its alloys can be tuned by small organic molecules. However, an automatic identification of effective organic additives within the vast chemical space of potential compounds needs sophisticated tools. Herein, we propose two systematic approaches of sparse feature selection for identifying molecular descriptors that are most relevant for the corrosion inhibition efficiency of chemical compounds. One is based on the classical statistical tool of analysis of variance, the other one based on random forests. We demonstrate how both can—when combined with deep neural networks—help to predict the corrosion inhibition efficiencies of chemical compounds for the magnesium alloy ZE41. In particular, we demonstrate that this framework outperforms predictions relying on a random selection of molecular descriptors. Finally, we point out how autoencoders could be used in the future to enable even more accurate automated predictions of corrosion inhibition efficiencies. Molecular descriptor ; Computer science ; Corrosion ; Chemical space ; Magnesium ; Artificial intelligence ; Identification (biology) ; Feature selection ; Dissolution ; Magnesium alloy ; Biological system ; Machine learning ; Selection (genetic algorithm) ; Materials science ; Pattern recognition (psychology) ; Quantitative structure–activity relationship ; Chemistry ; Drug discovery ; Metallurgy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Christopher M. Andolina | Jacob G. Wright | Nishith Kumar Das | Wissam A. Saidi
Source: Physical Review Materials (article, 2021-08-24)
Field: Materials Science — Materials Chemistry

Improved Al-Mg alloy surface segregation predictions with a machine learning atomistic potential Various industrial/commercial applications use Al-Mg alloys, yet the Mg added to Al materials, to improve strength, is susceptible to surface segregation and oxidation, leaving behind a softer and Al-enriched bulk alloy. To better understand this process and provide a systematic methodology for investigating dopants that can mitigate corrosion, we have developed a robust atomistic deep neural net potential (DNP) using a dataset generated with first-principles density-functional theory (DFT). The potential, validated systematically against DFT values, has been shown to have a high fidelity in calculating different elemental and intermetallic Al-Mg systems' properties. Our calculations predict a linear trend in the formation energy of the Al-Mg alloy and its density as a function of temperature, consistent with experimental literature. Employing the DNP within a hybrid Monte Carlo and molecular dynamics (MC/MD) approach, we predict anisotropic surface segregation for Al-Mg alloys such that (111)(100)(110), with (111) surfaces displaying the lowest segregation enthalpies and Mg enrichment. Furthermore, we model the segregation tendencies by adapting a recently introduced isotherm model for grain boundary segregation. Our results show that this model describes the MC/MD segregation profiles with higher fidelity than the McLean and Fowler-Guggenheim isotherm models. Materials science ; Intermetallic ; Alloy ; Grain boundary ; Molecular dynamics ; Density functional theory ; Thermodynamics ; Metallurgy ; Computational chemistry ; Microstructure Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; X-ray Diffraction in Crystallography
Authors: Harini Narayanan | Mariano Nicolás Cruz Bournazou | Gonzalo Guillén‐Gosálbez | Alessandro Butté
Source: Chemical Engineering Journal (article, 2021-10-22)
Field: Materials Science — Materials Chemistry

Functional-Hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions Interpretability ; Computer science ; Artificial intelligence ; Hybrid system ; Machine learning ; Extrapolation ; Representation (politics) ; Symbolic regression ; Artificial neural network ; Domain (mathematical analysis) ; Regression ; Support vector machine ; Genetic programming ; Mathematics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics
Authors: Ercan Aydoğmuş | Hasan Arslanoğlu | Mustafa Dağ
Source: Journal of Building Engineering (article, 2021-09-20)
Field: Materials Science — Polymers and Plastics

Production of waste polyethylene terephthalate reinforced biocomposite with RSM design and evaluation of thermophysical properties by ANN In this study, bio-unsaturated polyester (BUP) raw material is synthesized using modified palm oil (MPO). A new BUP composite has been improved by adding waste polyethylene terephthalate (WPET) to the obtained synthesis. The modified palm oil supplementation decreased the density, Shore D hardness, and elastic modulus of the BUP composite while increasing its thermal conductivity, and thermal stability. WPET filler has been used to decrease the thermal conductivity of the BUP composite, rise its elastic modulus and Shore D hardness. When parameters affecting experimental conditions are optimized using Response Surface Methodology (RSM); BUP (86 wt%), WPET (11.7 wt%), methyl ethyl ketone peroxide (MEKP: 1.6 wt%), cobalt octoate (Co. Oc: 0.7 wt%), and curing time (CT: 24 h) are approximately determined. BUP composite’s density is 1324 kg/m3, Shore D hardness 84, elastic modulus 346 MPa, thermal conductivity 0.048 W/m·K, and activation energy 135 kJ/mol. Also, when the thermophysical properties of the produced BUP composite have been evaluated using experimental data, it gives consistent results in both Artificial Neural Networks (ANN) and RSM method. Shore durometer ; Materials science ; Response surface methodology ; Polyethylene terephthalate ; Composite number ; Composite material ; Thermal conductivity ; Central composite design ; Elastic modulus ; Chemistry ; Chromatography Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Polymer Nanocomposites and Properties
Authors: Alberto Trentino | Jacob Madsen | Andreas Mittelberger | Clemens Mangler | Toma Susi | Kimmo Mustonen | Jani Kotakoski
Source: Nano Letters (article, 2021-06-09)
Field: Materials Science — Materials Chemistry

Atomic-Level Structural Engineering of Graphene on a Mesoscopic Scale Structural engineering is the first step toward changing properties of materials. While this can be at relative ease done for bulk materials, for example, using ion irradiation, similar engineering of 2D materials and other low-dimensional structures remains a challenge. The difficulties range from the preparation of clean and uniform samples to the sensitivity of these structures to the overwhelming task of sample-wide characterization of the subjected modifications at the atomic scale. Here, we overcome these issues using a near ultrahigh vacuum system comprised of an aberration-corrected scanning transmission electron microscope and setups for sample cleaning and manipulation, which are combined with automated atomic-resolution imaging of large sample areas and a convolutional neural network approach for image analysis. This allows us to create and fully characterize atomically clean free-standing graphene with a controlled defect distribution, thus providing the important first step toward atomically tailored two-dimensional materials. Graphene ; Characterization (materials science) ; Mesoscopic physics ; Nanotechnology ; Materials science ; Atomic units ; Sample preparation ; Sample (material) ; Scanning transmission electron microscopy ; Transmission electron microscopy ; Computer science ; Physics ; Chemistry ; Condensed matter physics Graphene research and applications ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Farzaneh Marahel | Bijan Mombeni Goodajdar | Leila Niknam | Maryam Faridnia | Elham Pournamdari | Salva Mohammad Doost
Source: International Journal of Environmental & Analytical Chemistry (article, 2021-05-05)
Field: Chemistry — Organic Chemistry

Ultrasonic assisted adsorption of methylene blue dye and neural network model for adsorption of methylene blue dye by synthesised Mn-doped PbS nanoparticles In the present study, the applicability of Mn-doped PbS nanoparticle synthesis for eliminating (methylene blue [MB]) dye rapidly from aqueous solutions was investigated. Identical techniques including Brunauer–Emmett–Teller theory, Fourier transform infrared, X-ray diffraction, thermogravimetric analysis, differential thermal analysis and scanning electron microscopy have been utilised to characterise this novel material. The investigation showed the applicability of Mn-doped PbS nanoparticles as an available, suitable and low-cost adsorbent for proper deletion of (MB) dye from aqueous media. Also, the impacts of variables including initial (MB) dye (X1), pH (X2), adsorbent dosage (X3), sonication time (X4), central composite design and response surface methodology (RSM) were shown. In the current article, the values of 10 mgL−1, 0.025 g and 6.0 and 5.0 min were considered as the ideal values for (MB) dye removal, respectively. The adsorption data fitted well with the Langmuir isotherm model with correlation coefficient (R2 > 0.999), and the maximum adsorption capacities (qmax) were observed to be 200.0 mg g−1 for (MB) dye at desired conditions. The pseudo-second-order kinetics coincides quite well with the kinetic results. The thermodynamic parameters including enthalpy, entropy and Gibbs energy were calculated for the adsorption of these (MB) dyes using Mn-doped PbS nanoparticles that are suitable, spontaneous and exothermic. The use of artificial neural network model as a tool for determining mean square error (=0.607) for (MB) dye removal by Mn-doped PbS nanoparticles. Ultrasonic power had important role in shortening the adsorption time of (MB) dye by enhancing the dispersion of adsorbent in solution. Overall, Mn-doped PbS nanoparticles could be a promising adsorbent material for (MB) dye removal from aqueous solutions in a short time. Adsorption ; Thermogravimetric analysis ; Methylene blue ; Aqueous solution ; Brilliant green ; Materials science ; Langmuir adsorption model ; Nanoparticle ; Enthalpy ; Gibbs free energy ; Analytical Chemistry (journal) ; Sonication ; Freundlich equation ; Nuclear chemistry ; Chemical engineering ; Chemistry ; Organic chemistry ; Nanotechnology ; Chromatography ; Thermodynamics Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Feng Gao | Xin Zhang | Biying Tan | Shichao Zhang | Jia Zhang | Dechang Jia | Yu Zhou | PingAn Hu
Source: Small (article, 2021-10-08)
Field: Materials Science — Materials Chemistry

Low Optical Writing Energy Multibit Optoelectronic Memory Based on SnS<sub>2</sub>/h‐BN/Graphene Heterostructure With the rapid development of artificial intelligence and neural network computing, the requirement for information storage in computing is gradually increasing. Floating gate memories based on 2D materials has outstanding characteristics such as non-volatility, optical writing, and optical storage, suitable for application in photonic in-memory computing chips. Notably, the optoelectronic memory requires less optical writing energy, which means lower power consumption and greater storage levels. Here, the authors report an optoelectronic memory based on SnS<sub>2</sub> /h-BN/graphene heterostructure with an extremely low photo-generated hole tunneling barrier of 0.23 eV. This non-volatile multibit floating gate memory shows a high switching ratio of 10<sup>6</sup> and a large memory window range of 64.8 V in the gate range ±40 V. And the memory device can achieve multilevel storage states of 50 under a low power light pulses of 0.32 nW and small light pulse width of 50 ms. Thanks to the Fowler-Nordheim tunneling of the photo-generated holes, the optical writing energy of the optoelectronic memory has been successfully reduced by one to three orders of magnitude compared to existing 2D materials-based systems. Optoelectronics ; Materials science ; Graphene ; Heterojunction ; Quantum tunnelling ; Optical storage ; Photonics ; Computer data storage ; Nanotechnology ; Computer science ; Computer hardware 2D Materials and Applications ; Advanced Memory and Neural Computing ; Neural Networks and Reservoir Computing
Authors: Si Wang | Ziao Shen | Zhenyu Shen | Yuanjun Dong | Yanran Li | Yuxin Cao | Yanmei Zhang | Shengshi Guo | Jianwei Shuai | Yun Yang | Changjian Lin | Xun Chen | Xingcai Zhang | Qiaoling Huang
Source: Nano Today (article, 2021-04-19)
Field: Materials Science — Materials Chemistry

Machine-learning micropattern manufacturing Manufacturing engineering ; Artificial intelligence ; Industrial engineering ; Computer science ; Engineering Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Peter Cats | Sander Kuipers | Sacha de Wind | Robin van Damme | Gabriele M. Coli | Marjolein Dijkstra | René van Roij
Source: APL Materials (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Machine-learning free-energy functionals using density profiles from simulations The formally exact framework of equilibrium Density Functional Theory (DFT) is capable of simultaneously and consistently describing thermodynamic and structural properties of interacting many-body systems in arbitrary external potentials. In practice, however, DFT hinges on approximate (free-)energy functionals from which density profiles (and hence the thermodynamic potential) follow via an Euler–Lagrange equation. Here, we explore a relatively simple Machine-Learning (ML) approach to improve the standard mean-field approximation of the excess Helmholtz free-energy functional of a 3D Lennard-Jones system at a supercritical temperature. The learning set consists of density profiles from grand-canonical Monte Carlo simulations of this system at varying chemical potentials and external potentials in a planar geometry only. Using the DFT formalism, we nevertheless can extract not only very accurate 3D bulk equations of state but also radial distribution functions using the Percus test-particle method. Unfortunately, our ML approach did not provide very reliable Ornstein–Zernike direct correlation functions for small distances. Helmholtz free energy ; Density functional theory ; Monte Carlo method ; Planar ; Supercritical fluid ; Simple (philosophy) ; Hybrid functional ; Hinge ; Molecular dynamics Machine Learning in Materials Science ; Quantum many-body systems ; Phase Equilibria and Thermodynamics
Authors: Necati Çetin | Kevser Karaman | Erman Beyzi | Cevdet Sağlam | Bahadır Demirel
Source: Food Analytical Methods (article, 2021-03-15)
Field: Chemistry — Analytical Chemistry

Comparative Evaluation of Some Quality Characteristics of Sunflower Oilseeds (Helianthus annuus L.) Through Machine Learning Classifiers Sunflower ; Helianthus annuus ; Mathematics ; Linear discriminant analysis ; Artificial intelligence ; Machine learning ; Support vector machine ; Multivariate statistics ; Naive Bayes classifier ; Statistics ; Multilayer perceptron ; Pattern recognition (psychology) ; Computer science ; Artificial neural network Spectroscopy and Chemometric Analyses ; Agricultural Engineering and Mechanization ; Sunflower and Safflower Cultivation
Authors: Xiuqing Zhu | Wencan Huang | Haoyang Lu | Zhanzhang Wang | Xiaojia Ni | Jinqing Hu | Shuhua Deng | Yaqian Tan | Lu Li | Ming Zhang | Chang Qiu | Yayan Luo | Hongzhen Chen | Shanqing Huang | Tao Xiao | Dewei Shang | Yuguan Wen
Source: Scientific Reports (article, 2021-03-10)
Field: Chemistry — Analytical Chemistry

A machine learning approach to personalized dose adjustment of lamotrigine using noninvasive clinical parameters The pharmacokinetic variability of lamotrigine (LTG) plays a significant role in its dosing requirements. Our goal here was to use noninvasive clinical parameters to predict the dose-adjusted concentrations (C/D ratio) of LTG based on machine learning (ML) algorithms. A total of 1141 therapeutic drug-monitoring measurements were used, 80% of which were randomly selected as the "derivation cohort" to develop the prediction algorithm, and the remaining 20% constituted the "validation cohort" to test the finally selected model. Fifteen ML models were optimized and evaluated by tenfold cross-validation on the "derivation cohort," and were filtered by the mean absolute error (MAE). On the whole, the nonlinear models outperformed the linear models. The extra-trees' regression algorithm delivered good performance, and was chosen to establish the predictive model. The important features were then analyzed and parameters of the model adjusted to develop the best prediction model, which accurately described the C/D ratio of LTG, especially in the intermediate-to-high range (≥ 22.1 μg mL<sup>-1</sup> g<sup>-1</sup> day), as illustrated by a minimal bias (mean relative error (%) = + 3%), good precision (MAE = 8.7 μg mL<sup>-1</sup> g<sup>-1</sup> day), and a high percentage of predictions within ± 20% of the empirical values (60.47%). This is the first study, to the best of our knowledge, to use ML algorithms to predict the C/D ratio of LTG. The results here can help clinicians adjust doses of LTG administered to patients to minimize adverse reactions. Lamotrigine ; Computer science ; Machine learning ; Artificial intelligence ; Data science ; Medical physics ; Medicine ; Bioinformatics ; Psychiatry ; Epilepsy ; Biology Analytical Methods in Pharmaceuticals ; Statistical Methods in Clinical Trials ; Computational Drug Discovery Methods
Authors: Alexander Scheinker | Frederick Cropp | Sergio Paiagua | D. Filippetto
Source: Scientific Reports (article, 2021-09-28)
Field: Materials Science — Surfaces, Coatings and Films

An adaptive approach to machine learning for compact particle accelerators Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics. Computer science ; Convolutional neural network ; Deep learning ; Beam (structure) ; Artificial intelligence ; Encoder ; Artificial neural network ; Optics ; Physics Electron and X-Ray Spectroscopy Techniques ; Particle Detector Development and Performance ; Nuclear Physics and Applications
Authors: Chang Wang | Ian Rosbottom | Thomas D. Turner | Sydney Laing | Andrew G. P. Maloney | Ahmad Y. Sheikh | Robert Docherty | Qiuxiang Yin | Kevin J. Roberts
Source: Pharmaceutical Research (article, 2021-05-19)
Field: Chemistry — Organic Chemistry

Molecular, Solid-State and Surface Structures of the Conformational Polymorphic Forms of Ritonavir in Relation to their Physicochemical Properties Inter-relationship between molecular, solid-state and surface structures of the polymorphic forms of ritonavir are quantified in relation to their physical-chemical properties. Ritonavir ; Chemistry ; Pharmaceutical technology ; Molecular conformation ; Stereochemistry ; Human immunodeficiency virus (HIV) ; Molecule ; Biology ; Virology ; Organic chemistry ; Chromatography Chemical Thermodynamics and Molecular Structure ; Crystallization and Solubility Studies ; Crystallography and molecular interactions