jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kevin M. Dalton | Jack B. Greisman | Doeke R. Hekstra
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-01-07)
Field: Materials Science — Materials Chemistry

A Unifying Bayesian Framework for Merging X-ray Diffraction Data A bstract Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering. Diffraction ; Monochromatic color ; Femtosecond ; Electron diffraction ; Reflection (computer programming) ; Bayesian probability ; Electron density ; Optics ; Scattering ; Merge (version control) ; Physics ; Computational physics ; Statistical physics ; Computer science ; Electron ; Artificial intelligence ; Laser ; Quantum mechanics Enzyme Structure and Function ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Petr Henyš | Lukáš Čapek
Source: Journal of the Textile Institute (article, 2021-01-04)
Field: Materials Science — Polymers and Plastics

Individual yarn fibre extraction from micro-CT: multilevel machine learning approach The internal structure and mechanics of the fibre materials, such as yarn or woven textile, are highly complex. Exploring the fibre structure is an essential step in material engineering either from the experimental or computational point of view. In this study, a new method to extract geometrical and morphological parameters of fibre structures is proposed. The method benefits from standard image analysis and machine learning technique to efficiently extract fibre segments from microcomputer tomography data. The proposed algorithm is tested on the yarn and woven textile materials with different resolution and quality. The developed method can extract the individual fibres with varying accuracy from 73% to 100% with processing time 2–5 s on the tested samples. Yarn ; Textile ; Woven fabric ; Materials science ; Point (geometry) ; Image processing ; Composite material ; Fabric structure ; Microcomputer ; Computer science ; Artificial intelligence ; Image (mathematics) ; Mathematics ; Geometry Textile materials and evaluations ; Material Properties and Processing ; Industrial Vision Systems and Defect Detection
Authors: Yunzheng Wang | Jing Ning | Li Lu | Michel Bosman | Robert E. Simpson
Source: arXiv (Cornell University) (preprint, 2021-07-05)
Field: Materials Science — Materials Chemistry

A scheme for simulating multi-level phase change photonics materials Chalcogenide phase change materials (PCMs) have been extensively applied in data storage, and they are now being proposed for high resolution displays, holographic displays, reprogrammable photonics, and all-optical neural networks. These wide-ranging applications all exploit the radical property contrast between the PCMs different structural phases, extremely fast switching speed, long-term stability, and low energy consumption. Designing PCM photonic devices requires an accurate model to predict the response of the device during phase transitions. Here, we describe an approach that accurately predicts the microstructure and optical response of phase change materials during laser induced heating. The framework couples the Gillespie Cellular Automata approach for modelling phase transitions with effective medium theory and Fresnel equations. The accuracy of the approach is verified by comparing the PCM optical response and microstructure evolution with the results of nanosecond laser switching experiments. We anticipate that this approach to simulating the switching response of PCMs will become an important component for designing and simulating programmable photonics devices. The method is particularly important for predicting the multi-level optical response of PCMs, which is important for all-optical neural networks and PCM-programmable perceptrons. Photonics ; Computer science ; Materials science ; Chalcogenide ; Optical switch ; Ranging ; Electronic engineering ; Optoelectronics ; Telecommunications ; Engineering Phase-change materials and chalcogenides ; Photonic Crystals and Applications ; Neural Networks and Reservoir Computing
Authors: Mengbo Guo | Xuyang Xu | Han Xie
Source: ChemRxiv (preprint, 2021-12-20)
Field: Materials Science — Materials Chemistry

Predicting the band gap of binary compounds from machine-learning regression methods Density functional theory (DFT) is a ubiquitous first-principles method, but the approximate nature of the exchange-correlation functional poses an inherent limitation for the accuracy of various computed properties. In this context, surrogate models based on machine learning have the potential to provide a more efficient and physically meaningful understanding of electronic properties, such as the band gap. Here, we construct a gradient boosting regression (GBR) model for prediction of the band gap of binary compounds from simple physical descriptors, using a dataset of over 4000 DFT-computed band gaps. Out of 27 features, electronegativity, periodic group, and highest occupied energy level exhibit the highest importance score, consistent with the underlying physics of the electronic structure. We obtain a model accuracy of 0.81 and root mean squared error of 0.26 eV using the top five features, achieving accuracy comparable to previously reported values but employing less number of features. Our work presents a rapid and interpretable prediction model for solid-state band gap with high fidelity to DFT and can be extended beyond binary materials considered in this study. Electronegativity ; Binary number ; Band gap ; Gradient boosting ; Mean squared error ; Density functional theory ; Artificial intelligence ; Boosting (machine learning) ; Computer science ; Machine learning ; Regression ; Mathematics ; Physics ; Computational chemistry ; Chemistry ; Random forest ; Statistics ; Condensed matter physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Hunain Alkhateb | Hashem Khaled Almashaqbeh | Jennifer Edmunson | Michael Fiske | Yacoub M. Najjar | Damian Stoddard
Source: Earth and Space 2021 (article, 2021-04-15)
Field: Materials Science — Materials Chemistry

Optimizing Magnesium Phosphate Binders with Boric Acid for Additive Construction Applications The authors are proposing a comprehensive research approach and terminology for providing a framework to utilize an ordinary portland cement (OPC) alternative class of materials for both extraterrestrial habitats and terrestrial 3D construction applications, where sand and clay can replace regolith. Magnesia-based binders (MBBs) have shown a great promise for use in different additive construction (AC) applications due to its superior properties such as high compressive strength, rapid setting time, and excellent durability, in addition to the possibility of harvesting these binders from lunar and Martian regolith. Our primary objective in this research is to prove the concept of utilizing magnesia phosphate cement (MPC) composites with different additives, including boric acid, graphene nanoplatelets (GnP), acetic acid, and potentially Martian and lunar regolith simulants as a potential construction material for the planetary habitation exploration missions. Before adjusting the mix design properties required to meet specific 3D construction printing needs, several aspects must be studied in depth to provide a better understanding of the MPC paste and composite behavior. This work aims to give more insight into the relative physical, mechanical, thermal, and chemical performance of the MPC pastes with varying additions of boric acid. The experimental results show that boric acid can change the phase compositions of the MPC paste and affect its performance. It can improve the initial setting time of the paste and alter the structure of its binding products. Finally, our proposed research creatively utilizes artificial neural networks (ANN) to investigate and subsequently optimize both the MPC pastes and composite formulation for real-time adjusting of 3D printing processes based on the desired material performance criteria. Boric acid ; Magnesium phosphate ; Materials science ; Regolith ; Compressive strength ; Durability ; Composite number ; Magnesium ; Cement ; Process engineering ; Composite material ; Astrobiology ; Chemistry ; Engineering ; Metallurgy Magnesium Oxide Properties and Applications ; Bone Tissue Engineering Materials ; Environmental Engineering and Cultural Studies
Authors: Aleksander E. P. Durumeric | Gregory A. Voth
Source: arXiv (Cornell University) (preprint, 2021-09-15)
Field: Materials Science — Materials Chemistry

Explaining classifiers to understand coarse-grained models Bottom-up coarse-grained molecular dynamics models are parameterized using complex effective Hamiltonians. These models are typically optimized to approximate high dimensional data from atomistic simulations. In contrast, human validation of these models is often limited to low dimensional statistics that do not necessarily differentiate between the CG model and said atomistic simulations. We propose that explainable machine learning can directly convey high-dimensional error to scientists and use Shapley additive explanations do so in two coarse-grained protein models. Parameterized complexity ; Computer science ; Contrast (vision) ; Multiple Models ; Statistical physics ; Machine learning ; Artificial intelligence ; Algorithm ; Physics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Eko Yudhi Prastowo
Source: Jurnal Teknik Informatika dan Sistem Informasi (article, 2021-08-18)
Field: Chemistry — Organic Chemistry

Pengenalan Jenis Kayu Berdasarkan Citra Makroskopik Menggunakan Metode Convolutional Neural Network Until now, wood has an irreplaceable function. Building materials, shipping, furniture, sports equipment, carvings and handicrafts using wood. Indonesia has more than 4,000 types of wood, so choosing the right wood is a challenge because choosing the wrong type of wood can make the quality of processed products decline and not as expected. In addition, proper identification of timber can also prevent illegal logging, especially on certain types of wood which are now increasingly scarce. Recognition to wood by looking directly is a difficult thing for ordinary people to do and can only be done by a wood expert, so it is necessary to find a method of recognizing wood that can be used by people independently. One method that can be used to identify type of wood is image processing based on characteristics of wood which include color, fiber direction and texture. This paper will describe recognition of wood-based image processing using Convolutional Neural Network (CNN) method. This method is derived from Neural Networks with addition of an extraction layer feature, which can reduce free parameters that are not needed by the system. Wood image data used in this study are four types of wood that are often used as raw materials for making houses and furniture, namely Glugu, Teak, Sengon and Waru. Results of this study were able to recognize four types of wood with an accuracy of 95% in 600 epochs/iteration, so that it can be used as a simple, easy and inexpensive wood recognition system. Convolutional neural network ; Computer science ; Wood industry ; Wood processing ; Identification (biology) ; Artificial intelligence ; Woodworking ; Pulp and paper industry ; Artificial neural network ; Image processing ; Pattern recognition (psychology) ; Computer vision ; Image (mathematics) ; Engineering ; Forestry ; Geography Wood and Agarwood Research ; Computer Science and Engineering ; Agricultural and Environmental Management
Authors: Tim Cofala | Thomas Teusch | Oliver Krämer
Source:  (article, 2021-07-18)
Field: Materials Science — Materials Chemistry

Spatial Generation of Molecules with Transformers Generative models play an important role in the discovery of new molecules. However, these models often operate on a string or graph based representation of molecules, without taking spatial information into consideration. In this paper we introduce an autoregressive generative neural network operating directly in the three-dimensional space of atoms. By leveraging the powerful multi-head self-attention mechanism of the transformer architecture the model predicts a set of atoms and a corresponding distance matrix to generate molecules in a translation and rotation invariant manner. We demonstrate that the model can be utilized to generate completely new molecules that resemble the training data in their structural properties. Furthermore, the model is capable of completing unconnected sets of atoms to valid molecular structures. Computer science ; Transformer ; Generative model ; Generative grammar ; Matrix representation ; Theoretical computer science ; Graph ; Rotation matrix ; Representation (politics) ; Artificial intelligence ; Topology (electrical circuits) ; Algorithm ; Voltage ; Physics ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Neural Networks and Applications
Authors: Eliane K. Assoi | Olivier K. Bagui | Benoît K. Kouakou | Adolphe Y. Gbogbo | Doudjo Soro | Jérémie T. Zoueu
Source: Australian Journal of Crop Science (article, 2021-07-07)
Field: Chemistry — Analytical Chemistry

Estimating maturity by measuring pH, sugar, dry matter, water and vitamin C content of cashew apple (Anacardium occidentale) from remote spectral reflectance data using neural network In agricultural sector, maturity is the main decision criterion for starting the harvest. This criterion is usually revealed by a number of parameters such as pH, sugar, dry matter, water and vitamin C, which are informative but technically tedious to measure. The cashew apple is the hypertrophied peduncle which is attached to the cashew nut. It is a nutritious (very juicy fruit (85 to 90% water), sweet (7 to 13% carbohydrates), acidic and vitamin C content) fruit with high therapeutic and medicinal properties. The cashew apple is used as a raw material for many industrial applications (juice and alcohol). This research was conducted as a preliminary step towards the development of a real-time remote sensing technique for assessing the quality of tropical fruits. Spectral acquisitions were carried out from intact cashew apple using optical system composed reflector coupled with spectrometer USB 4000 FL from Ocean Optics (350-1100 nm). Immediately after spectral acquisition, the samples were analyzed by using chemical methods (sugar content, dry matter content, water content, vitamin C and pH). Preprocessing treatment method, bootstrap method was required to create statistical new samples and to increase the number of samples required. This method was used to improve the predictive performance of calibration model. Statistical models of prediction were developed using an artificial neural network (ANN) method. The results obtained from the models built by ANN showed strong relationships between predicted and experimental values: (Rsquare = 0.9870, RMSE= 0.0262) for pH, (Rsquare=0.9869, RMSE=0.1392) for Sugar, (Rsquare=0.9726, RMSE=0.3333) for water content, (Rsquare=0.9703, RMSE=0.3464) for vitamin C and (Rsquare=0.9922, RMSE= 5.0304, RMSE=5.0304) for dry matter. These results confirm the potential of visible spectroscopy to predict quality parameters of cashew apples remotely and make decisions about best harvest time Sugar ; Coefficient of determination ; Dry matter ; Mathematics ; Environmental science ; Food science ; Chemistry ; Agronomy ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Phytochemicals and Antioxidant Activities
Authors: Susanto Budi Sulistyo | Siswantoro Siswantoro | Agus Margiwiyatno | Masrukhi | A Mustofa | Arief Sudarmaji | Rifah Ediati | Riana Listanti | Hety Handayani Hidayat
Source: IOP Conference Series Earth and Environmental Science (article, 2021-02-01)
Field: Chemistry — Analytical Chemistry

Handheld arduino-based near infrared spectrometer for non-destructive quality evaluation of siamese oranges Abstract Quality evaluation, in particular chemical properties, of orange fruit commonly conducted by destructive method by extracting its juice. A near infrared spectrometer (NIRS) can be used to quantify orange chemical properties with non-destructive method. This research aimed to design a handheld NIRS using AS7263 sensor and Arduino programming to estimate the Siamesa orange quality in its acidity (pH), total soluble solids (TSS) and vitamin C. The AS7263 sensor has six NIR channels for different wavelengths, i.e. R (610 nm), S (680 nm), T (730 nm), U (760 nm), V (810 nm) and W (860 nm). For that, a performance test was carried out using 300 samples of orange. Result show that evaluation of orange quality in acidity and TSS has mean absolute percentage error (MAPE) &lt; 10%, and the vitamin C shows &gt; 10%. In addition, the estimation of orange chemical properties by backpropagation neural network (BPNN) yielded better results compared to simple regression and multiple regression methods. Mobile device ; Arduino ; Spectrometer ; Infrared ; Quality (philosophy) ; Computer science ; Remote sensing ; Embedded system ; Physics ; Optics ; Operating system ; Geography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Dye analysis and toxicity
Authors: Maurice Dörr | Maximilian M. Hielscher | Jonny Proppe | Siegfried R. Waldvogel
Source: ChemElectroChem (article, 2021-06-16)
Field: Chemistry — Electrochemistry

Electrosynthetic Screening and Modern Optimization Strategies for Electrosynthesis of Highly Value‐added Products Abstract Invited for this month's cover picture is the group of Siegfried R. Waldvogel at Johannes Gutenberg ‐University in Mainz accompanied by Jonny Proppe at Georg‐August University in Göttingen. The cover picture shows three schemes illustrating the methods Principal Component Analysis, Machine Learning, and Design of Experiment as a representative of statistical methods to be used for optimization in electroorganic synthesis. The green power plug highlights the great potential of the combination of electrochemistry with these methods to propel the field to a highly sustainable future. All of the elements are backed with the contour plot of a mathematic model. Read the full text of the Minireview at 10.1002/celc.202100318 . Cover (algebra) ; Electrosynthesis ; Principal (computer security) ; Field (mathematics) ; Computer science ; Value (mathematics) ; Plot (graphics) ; Artificial intelligence ; Engineering ; Mathematics ; Mechanical engineering ; Machine learning ; Electrochemistry ; Chemistry ; Statistics Electrochemical Analysis and Applications
Authors: Mert Y. Sengul | Yao Song | Linglin He | Adri C. T. van Duin | Ying Hung | Tirthankar Dasgupta
Source: IEEE Transactions on Automation Science and Engineering (article, 2021-09-14)
Field: Materials Science — Materials Chemistry

CLAIMED: A CLAssification-Incorporated Minimum Energy Design to Explore a Multivariate Response Surface With Feasibility Constraints Motivated by the problem of optimization of force-field systems in physics using large-scale computer simulations, we consider exploration of a deterministic complex multivariate response surface. The objective is to find input combinations that generate output close to some desired or “target” vector. Despite reducing the problem to exploration of the input space with respect to a 1-D loss function, the search is nontrivial and challenging due to infeasible input combinations, high dimensionalities of the input and output space and multiple “desirable” regions in the input space, and the difficulty of emulating the objective function well with a surrogate model. We propose an approach that is based on combining machine learning techniques with smart experimental design ideas to locate multiple good regions in the input space. Note to Practitioners—ReaxFF is a force field that incorporates complex functions with associated inputs in order to describe the inter- and intra-atomic interactions in materials systems. A typical ReaxFF force field consists of hundreds of parameters (inputs) per element type. During the development of a force field for a molecular system of interest, using computer simulations, these parameters are optimized to reproduce hundreds of material properties close to some benchmark reference values. Finding “good” combinations of hundreds of parameters that produce hundreds of reference values close to their gold standards is a challenging problem because there may be several parameter combinations that may be “almost equally good” or “equally desirable.” To add to the complication, several input combinations simply lead to a system crash, not producing any output at all. Standard global optimization methods do not address such a problem. We propose a novel framework that can address this problem. Beyond the ReaxFF optimization, it can be applied to multiobjective optimization in engineering and the physical sciences, where there are unknown constraints and the focus is on obtaining several good points that can serve as alternatives to a single global optimum. Parameter space ; Benchmark (surveying) ; Force field (fiction) ; Field (mathematics) ; Computer science ; ReaxFF ; Function (biology) ; Multivariate statistics ; Mathematical optimization ; Algorithm ; Mathematics ; Artificial intelligence ; Machine learning ; Molecular dynamics ; Physics Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Probabilistic and Robust Engineering Design