jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pablo Moscato | Mohammad Nazmul Haque | Kevin Huang | Julia Sloan | Jon C. de Oliveira
Source: arXiv (Cornell University) (preprint, 2020-11-27)
Field: Materials Science — Materials Chemistry

Learning to extrapolate using continued fractions: Predicting the critical temperature of superconductor materials In the field of Artificial Intelligence (AI) and Machine Learning (ML), the approximation of unknown target functions $y=f(\mathbf{x})$ using limited instances $S={(\mathbf{x^{(i)}},y^{(i)})}$, where $\mathbf{x^{(i)}} \in D$ and $D$ represents the domain of interest, is a common objective. We refer to $S$ as the training set and aim to identify a low-complexity mathematical model that can effectively approximate this target function for new instances $\mathbf{x}$. Consequently, the model's generalization ability is evaluated on a separate set $T=\{\mathbf{x^{(j)}}\} \subset D$, where $T \neq S$, frequently with $T \cap S = \emptyset$, to assess its performance beyond the training set. However, certain applications require accurate approximation not only within the original domain $D$ but also in an extended domain $D'$ that encompasses $D$. This becomes particularly relevant in scenarios involving the design of new structures, where minimizing errors in approximations is crucial. For example, when developing new materials through data-driven approaches, the AI/ML system can provide valuable insights to guide the design process by serving as a surrogate function. Consequently, the learned model can be employed to facilitate the design of new laboratory experiments. In this paper, we propose a method for multivariate regression based on iterative fitting of a continued fraction, incorporating additive spline models. We compare the performance of our method with established techniques, including AdaBoost, Kernel Ridge, Linear Regression, Lasso Lars, Linear Support Vector Regression, Multi-Layer Perceptrons, Random Forests, Stochastic Gradient Descent, and XGBoost. To evaluate these methods, we focus on an important problem in the field: predicting the critical temperature of superconductors based on physical-chemical characteristics. Perceptron ; Artificial intelligence ; Stochastic gradient descent ; Algorithm ; Lipschitz continuity ; Machine learning ; Computer science ; Gradient descent ; Lasso (programming language) ; Regression ; Support vector machine ; Mathematics ; Mathematical optimization ; Artificial neural network ; Statistics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Non-Destructive Testing Techniques
Authors: Nur Ibrahim | YN Fu’adah | NK Caecar Pratiwi | Syamsul Rizal | Koredianto Usman
Source: IOP Conference Series Materials Science and Engineering (article, 2020-12-01)
Field: Chemistry — Analytical Chemistry

Computer Aided System for Gambung Tea Identification using Convolutional Neural Network Abstract Tea commodity has a very strategic role for Indonesian economy. In 2012 the tea commodity was able to generate foreign exchange of US $ 156.74 million. Nationally, tea industry contributes a Gross Domestic Product (GDP) of around Rp. 1.2 trillion. The total tea plantation area in Indonesia entirely covering 123 938 hectares. The conception of sustainable tea production includes three aspects, economic development, social development and environmental protection. One of the steps towards sustainable tea production is survey and identification. The process continues with the selection of suitable planting material (seedlings). Gambung series, are planting material seeds that have been recommended by the Indonesia Ministry of Agriculture. The Gambung series has a potential yield of 4.000 - 5.800 kg/hectare of dried tea. The morphological similarity level of Gambung series is very high, because the elders of the clones are from the same crossing parents. Experts who are able to identify Gambung clone are very limited. This process is susceptible to errors and is very dependent on the presence of experts. If an error occurs in the process of identifying the type of clone, it will interferes with the breeding process. Errors in the selection of recommended clones will be detrimental to the process for long time period, due to the economic age of the tea plant can reach 50 years. From this issue, it is very necessary to design a system that is able to identify the planting material of Gambung series clones. The system is designed to classify Gambung and Non Gambung tea series using Convolutional Neural Network (CNN) with a high accuracy and low loss rate. Hectare ; Commodity ; Identification (biology) ; Product (mathematics) ; Indonesian ; Sowing ; Production (economics) ; Yield (engineering) ; Agriculture ; Business ; Agricultural science ; Sustainable development ; Agricultural economics ; Geography ; Economics ; Biology ; Mathematics ; Horticulture ; Botany Spectroscopy and Chemometric Analyses
Authors: Chia-Che Chung | Hsin-Cheng Lin | Wan Khairunizam | Min-Fong Yang | C. W. Liu
Source:  (article, 2020-06-01)
Field: Materials Science — Materials Chemistry

Interpretable Neural Network to Model and to Reduce Self-Heating of FinFET Circuitry An interpretable neural network (NN) is used to model the self-heating (SH) in complex FinFET circuits. The NN training/testing datasets from 3 -stage to 37 -stage chain circuits in folded layout composed of inverter (INV)/NAND/NOR are simulated by our distributed R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> -C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> SPICE model [1], [2]. The interfacial thermal resistance [3], boundary scattering [4], alloy scattering [5], and layout dependence are considered. The NN interpretation by feature importance analysis is consistent with the thermal physics. Stage# is the most important feature of the NN prediction. Both via2 bundle positions and via2 numbers (via2#) are effective to reduce SH. As compared to SPICE, NN prediction in 37 -stage INV chain computes 3×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> X faster with accuracy loss . The high computation efficiency and high precision make NN feasible to predict chain circuits up to 40 stages, which cannot be simulated by SPICE due to long computation time. Spice ; Artificial neural network ; Computer science ; Feature (linguistics) ; Artificial intelligence ; Computation ; Algorithm ; Electronic circuit ; Topology (electrical circuits) ; Electronic engineering ; Engineering ; Electrical engineering Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design ; Semiconductor materials and devices
Authors: Nicolai Ree | Andreas H. Göller | Jan H. Jensen
Source: ChemRxiv (preprint, 2020-12-18)
Field: Materials Science — Materials Chemistry

RegioSQM20: Improved Prediction of the Regioselectivity of Electrophilic Aromatic Substitutions &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;We present RegioSQM20, a new version of RegioSQM (&lt;i&gt;Chem. Sci&lt;/i&gt;. 2018, 9, 660), which predicts the regioselectivities of electrophilic aromatic substitution (EAS) re- actions from the calculation of proton affinities. The following improvements have been made: The open source semiempirical tight binding program xtb is used instead of the closed source MOPAC program. Any low energy tautomeric forms of the input molecule are identified and regioselectivity predictions are made for each form. Finally, RegioSQM20 offers a qualitative prediction of the reactivity of each tautomer (low, medium, or high) based on the reaction center with the highest proton affinity. The inclusion of tautomers increases the success rate from 90.7% to 92.7%. RegioSQM20 is compared to two machine learning based models: one developed by Struble et al. (&lt;i&gt;React. Chem. Eng&lt;/i&gt;. 2020, 5, 896) specifically for regioselectivity predictions of EAS reactions (WLN) and a more generally applicable reactivity predictor (IBM RXN) de- veloped by Schwaller et al. (&lt;i&gt;ACS Cent. Sci&lt;/i&gt;. 2019, 5, 1572). RegioSQM20 and WLN offers roughly the same success rates for the entire data sets (without considering tau- tomers), while WLN is many orders of magnitude faster. The accuracy of the more general IBM RXN approach is somewhat lower: 76.3%-85.0%, depending on the data set. The code is freely available under the MIT open source license and will be made available as a webservice (regiosqm.org) in the near future. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Tautomer ; Regioselectivity ; Chemistry ; Electrophile ; Reactivity (psychology) ; Computational chemistry ; IBM ; Stereochemistry ; Medicinal chemistry ; Physics ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Chemistry Cycloaddition Reactions
Authors: Soojung Yang | Kyung-Hoon Lee | Seongok Ryu
Source: arXiv (Cornell University) (preprint, 2020-03-17)
Field: Materials Science — Materials Chemistry

A comprehensive study on the prediction reliability of graph neural networks for virtual screening Prediction models based on deep neural networks are increasingly gaining attention for fast and accurate virtual screening systems. For decision makings in virtual screening, researchers find it useful to interpret an output of classification system as probability, since such interpretation allows them to filter out more desirable compounds. However, probabilistic interpretation cannot be correct for models that hold over-parameterization problems or inappropriate regularizations, leading to unreliable prediction and decision making. In this regard, we concern the reliability of neural prediction models on molecular properties, especially when models are trained with sparse data points and imbalanced distributions. This work aims to propose guidelines for training reliable models, we thus provide methodological details and ablation studies on the following train principles. We investigate the effects of model architectures, regularization methods, and loss functions on the prediction performance and reliability of classification results. Moreover, we evaluate prediction reliability of models on virtual screening scenario. Our result highlights that correct choice of regularization and inference methods is evidently important to achieve high success rate, especially in data imbalanced situation. All experiments were performed under a single unified model implementation to alleviate external randomness in model training and to enable precise comparison of results. Computer science ; Machine learning ; Reliability (semiconductor) ; Artificial intelligence ; Regularization (linguistics) ; Artificial neural network ; Inference ; Probabilistic logic ; Randomness ; Data mining Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Joerg Doellinger | Christian Blumenscheit | Andy Schneider | Peter Lasch
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-03-03)
Field: Chemistry — Spectroscopy

Optimization of data-independent acquisition using predicted libraries for deep and accurate proteome profiling ABSTRACT In silico spectral library prediction of all possible peptides from whole organisms has a great potential for improving proteome profiling by data-independent acquisition (DIA) and extending its scope of application. In combination with other recent improvements in the field of mass spectrometry (MS)-based proteomics, including sample preparation, peptide separation and data analysis, we aimed to uncover the full potential of such an advanced DIA strategy by optimization of the data acquisition. The results demonstrate that the combination of high-quality in silico libraries, reproducible and high-resolution peptide separation using micro-pillar array columns as well as neural network supported data analysis enables the use of long MS scan cycles without impairing the quantification performance. The study demonstrates that mean coefficient of variations of 4 % were obtained even at only 1.5 data points per peak (full width at half maximum) across different gradient lengths, which in turn improved proteome coverage up to more than 8000 proteins from HeLa cells using empirically-corrected libraries and more than 7000 proteins using a whole human in silico predicted library. These data were obtained using a Q Exactive orbitrap mass spectrometer with moderate scanning speed (12 Hz) and perform very well in comparison to recent studies using more advanced MS instruments, which underline the high potential of this optimization strategy for various applications in clinical proteomics, microbiology and molecular biology. Proteome ; Orbitrap ; In silico ; Mass spectrometry ; Profiling (computer programming) ; Computational biology ; Computer science ; Proteomics ; Data acquisition ; Biological system ; Chemistry ; Data mining ; Bioinformatics ; Chromatography ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Sebastian Watzele
Source: mediaTUM  (Technical University of Munich) (article, 2020-01-01)
Field: Chemistry — Electrochemistry

Methodological Aspects of In-Depth Electrochemical Characterization of Metal and Metal Oxide Electrocatalysts In this work, a method has been developed to estimate the relative contribution of Volmer-Heyrovsky and Volmer-Tafel mechanisms. It is based on electrochemical probing of the frequency dependent impedance of Pt microelectrodes. Additionally, a novel approach was elaborated to benchmark electrocatalysts using industrial relevant conditions such as operation at high current densities and at elevated temperatures and in concentrated electrolytes. Further, new method for the determination of ECSA of OER electrocatalysts using electrochemical impedance spectroscopy measurements was developed. Tafel equation ; Dielectric spectroscopy ; Electrolyte ; Electrochemistry ; Materials science ; Metal ; Characterization (materials science) ; Oxide ; Microelectrode ; Electrical impedance ; Analytical Chemistry (journal) ; Chemistry ; Inorganic chemistry ; Nanotechnology ; Electrode ; Metallurgy ; Environmental chemistry ; Physical chemistry ; Electrical engineering ; Engineering Electrochemical Analysis and Applications ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Raj Ganesh S. Pala
Source: Frontiers in Chemistry (article, 2020-06-19)
Field: Materials Science — Materials Chemistry

Should All Electrochemical Energy Materials Be Isomaterially Heterostructured to Optimize Contra and Co-varying Physicochemical Properties? Sustainable energy and chemical/material transformation constrained by limited greenhouse gas generation impose a grand challenge and posit outstanding opportunities to electrochemical material devices. Dramatic advancements in experimental and computational methodologies have captured detailed insights into the working of these material devices at a molecular scale and have brought to light some fundamental constraints that impose bounds on efficiency. We propose that the coupling of molecular events in the material device gives rise to contra-varying or co-varying properties and efficiency improving partial decoupling of such properties can be achieved via introducing engineered heterogeneities. A specific class of engineered heterogeneity is in the form of isomaterial heterostructures comprised of non-native and native polymorphs. The non-native polymorph differs from their native/ground state bulk polymorph in terms of its discrete translational symmetry and we anticipate specific symmetry relationships exist between non-native and native structures that enable the formation of interfaces that enhance efficiency. We present circumstantial evidence and provide speculative mechanisms for such an approach with the hope that a more comprehensive delineation of proposed material design will be undertaken. Decoupling (probability) ; Nanotechnology ; Materials science ; Biochemical engineering ; Computer science ; Chemical physics ; Chemistry ; Engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Jakob C. Dahl | Xingzhi Wang | Xiao Huang | Emory M. Chan | A. Paul Alivisatos
Source: ChemRxiv (preprint, 2020-05-13)
Field: Materials Science — Materials Chemistry

Elucidating the Weakly Reversible Cs-Pb-Br Perovskite Nanocrystal Reaction Network with High-Throughput Maps and Transformations &lt;p&gt;Advances in automation and data analytics can aid exploration of the complex chemistry of nanoparticles. Lead halide perovskite colloidal nanocrystals provide an interesting proving ground: there are reports of many different phases and transformations, which has made it hard to form a coherent conceptual framework for their controlled formation through traditional methods. In this work, we systematically explore the portion of Cs-Pb-Br synthesis space in which many optically distinguishable species are formed using high-throughput robotic synthesis to understand their formation reactions. We deploy an automated method that allows us to determine the relative amount of absorbance that can be attributed to each species in order to create maps of the synthetic space. These in turn facilitate improved understanding of the interplay between kinetic and thermodynamic factors that underlie which combination of species are likely to be prevalent under a given set of conditions. Based on these maps, we test potential transformation routes between perovskite nanocrystals of different shapes and phases. We find that shape is determined kinetically, but many reactions between different phases show equilibrium behavior. We demonstrate a dynamic equilibrium between complexes, monolayers and nanocrystals of lead bromide, with substantial impact on the reaction outcomes. This allows us to construct a chemical reaction network that qualitatively explains our results as well as previous reports and can serve as a guide for those seeking to prepare a particular composition and shape. &lt;/p&gt; Perovskite (structure) ; Nanocrystal ; Halide ; Throughput ; Monolayer ; Chemistry ; Computer science ; Nanotechnology ; Materials science ; Chemical physics ; Inorganic chemistry ; Crystallography Machine Learning in Materials Science
Authors: Tariq Shah | Khalid Saeed
Source: Journal of Advances in Science and Engineering (article, 2020-05-31)
Field: Materials Science — Materials Chemistry

Experimental study on silver nanoparticles: synthesis, photo-degradation and analysis The aim of present study was waste water treatment via advanced oxidation process (AOP). Wet chemical precipitation method was used to prepare silver nanoparticles (Ag NPs). The Ag NPs were employed for photo catalytic degradation of Congo red (CR) dye in aqueous medium. The scanning electron microscopy (SEM) investigation shows agglomerated form of Ag NPs. The average sizes of agglomerations are below 600 nm. Energy dispersive X-rays spectroscopy (EDX) and ultraviolet light visible spectroscopy (UV/Vis) also established the formation of Ag NPs. The photo-degradation study reveals that Ag NPs degraded by 73% of CR dye in 480 min. Catalytic dosage study shows the dye degradation was increased vice versa as increased the amount of Ag NPs and then almost level off after 0.025 g of catalyst. In pH study it was observed that degradation of CR dye increased as pH increased. The recovered catalyst study also significantly degraded the CR dye. Catalysis ; Congo red ; Silver nanoparticle ; Degradation (telecommunications) ; Scanning electron microscope ; Aqueous solution ; Nanoparticle ; Precipitation ; Nuclear chemistry ; Visible spectrum ; Ultraviolet visible spectroscopy ; Spectroscopy ; Materials science ; Chemical engineering ; Photocatalysis ; Energy-dispersive X-ray spectroscopy ; Chemistry ; Nanotechnology ; Adsorption ; Organic chemistry Nanoparticles: synthesis and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanomaterials for catalytic reactions
Authors: Dan Zhao | Anthony K. Cheetham | Shuhei Furukawa | Susumu Kitagawa | Qiang Xü | Wei Zhang | Ruqiang Zou
Source: APL Materials (article, 2020-04-01)
Field: Materials Science — Materials Chemistry

Open framework materials for energy applications Exploitation of missing linker in Zr-based metal-organic framework as the catalyst support for selective oxidation of benzyl alcohol Materials science ; Engineering physics ; Nanotechnology ; Engineering Covalent Organic Framework Applications ; Advanced Battery Materials and Technologies ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Xingzhi Wang | Jie Li | Hyun Dong Ha | Jakob C. Dahl | Teresa Head‐Gordon | A. Paul Alivisatos
Source: ChemRxiv (preprint, 2020-09-22)
Field: Materials Science — Materials Chemistry

AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles The synthesis quality of artificial inorganic nanocrystals is most often assessed by transmission electron microscopy (TEM) for which new high-throughput advances have dramatically increased both the quantity and information richness of metal nanoparticle (mNP) characterization. Existing automated data analysis algorithms of TEM mNP images generally adopt a supervised approach, requiring a significant effort in human preparation of labelled data that reduces objectivity, efficiency, and generalizability. We have developed an unsupervised algorithm AutoDetect-mNP for automated analysis of TEM images that objectively extracts morphological information of convex mNPs from TEM images based on their shape attributes, requiring little to no human input in the process. The performance of AutoDetect-mNP is tested on two datasets of bright field TEM images of Au nanoparticles with different shapes, demonstrating that the algorithm is quantitatively reliable, and can thus serve as a generalizable measure of the morphology distributions of any mNP synthesis. The AutoDetect-mNP algorithm will aid in future developments of high-throughput characterization of newly synthesized mNPs, and the future advent of time-resolved TEM studies that can investigate reaction mechanisms of mNP synthesis and reactivity. Characterization (materials science) ; Nanoparticle ; Transmission electron microscopy ; Computer science ; Materials science ; Nanotechnology ; Artificial intelligence ; Algorithm Machine Learning in Materials Science