jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Krunal Jagdishbhai Koshiya | Gianluca Rizzello | Paul Motzki
Source:  (article, 2023-09-11)
Field: Materials Science — Materials Chemistry

An Embedded System for Data-Based Self-Sensing in Shape Memory Alloy Wire Actuators Abstract Estimation of displacement of a Shape Memory Alloy (SMA) actuator from electrical properties, is one of the attractive attributes of SMA. To utilize this feature, it is necessary to model the correlation between electrical properties and strain of the wire. Identification of the relationship between resistance and displacement is not always straightforward in SMA actuators, as the complexity of the resistance-displacement characteristics may vary depending upon various factors, e.g., biasing mechanism, additional external loads, actuation frequency, as well as the electrical activation strategy for heating SMA wires. In this paper, we present an experimental setup that incorporates a spring-loaded SMA wire mechanism. Resistance across the SMA wire has been measured for different actuation frequencies. In general resistance behavior has been observed and analyzed, which motivates the use of data-based approaches for estimation of the displacement. Recurrent neural networks represent a possible solution for identifying the complex correlation between electrical input and displacement for different configurations of SMA-actuated systems. The displacement estimated by the trained model is then compared against the displacement measured by a laser displacement sensor, for validation purpose. SMA* ; Actuator ; Displacement (psychology) ; Shape-memory alloy ; Materials science ; Computer science ; Control theory (sociology) ; Acoustics ; Structural engineering ; Engineering ; Artificial intelligence ; Physics ; Composite material Shape Memory Alloy Transformations ; Structural Engineering and Vibration Analysis ; Smart Materials for Construction
Authors: Natalia Kireeva | А. Yu. Tsivadze
Source: ChemRxiv (preprint, 2023-12-26)
Field: Materials Science — Materials Chemistry

Rational Screening of Mn(II) Metal-Ligand Complexes for Using as MRI Contrast Agents Using Machine Learning Recent decades have shown a good progress in all the fields of the advanced diagnostic capabilities and personalized medicine. The smart contrast agents (SCAs) that may appear the important element of the proper diagnosis specification should become the standard in a normal clinical practice. Most widely used contrast agents are the kinetically inert and thermodynamically stable complexes of organic ligands with Gd(III) metal ion. However, known safety concerns associated with high toxicity and severe side effects due to demetallization reactions leading to the formation of free Gd(III) ion necessitate the demand in the contrast agents of new generation. It is expected that one of the alternatives will be ML complexes with essential paramagnetic metals such as the Mn(II), Mn(III) and Fe(III). In this study, the experimental data on Mn(II)-based contrast agents (CAs) including the key functional characteristics such as the relaxation enhancement characteristics, kinetic inertness and thermodynamic stability were analyzed. The machine learning methods were used in a combination with the physicochemical and substructure(topology)-based features to estimate the missed experimental data concerned the thermodynamic stability and relaxivity characteristics evaluated at certain conditions. The analysis of the experimental data has shown that the bispidine scaffold can be considered as the reference point for the ligand search as providing with a sufficient compromise for all the investigated functional characteristics of CAs. The analysis of the literature suggests using iodine and fluorine substituents for the electronic and steric hindrance thus preventing the cleavage of the bonds while the histidine residue can be recommended for the pH sensitivity of MRI CAs while targeting the OATPs (organic anion transporting polypeptides) to achieve the hepatobiliary clearance as well as for the enhanced contrast for hepatobiliary system thus personalizing the MRI investigation. Chemistry ; Steric effects ; Paramagnetism ; Chemical stability ; Ligand (biochemistry) ; Combinatorial chemistry ; Computational chemistry ; Stereochemistry ; Organic chemistry Lanthanide and Transition Metal Complexes ; Metal-Organic Frameworks: Synthesis and Applications ; Metal complexes synthesis and properties
Authors: Kyle Bystrom | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2023-03-01)
Field: Materials Science — Materials Chemistry

Nonlocal Machine-Learned Exchange Functional for Molecules and Solids The design of better exchange-correlation functionals for Density Functional Theory (DFT) is a central challenge of modern electronic structure theory. However, current developments are limited by the mathematical form of the functional, with efficient semilocal functionals being inaccurate for many technologically important systems and the more accurate hybrid functionals being too expensive for large solid-state systems due to the use of the exact exchange operator. In this work, we use machine learning combined with exact physical constraints to design an exchange functional that is both orbital-dependent and nonlocal, but which can be evaluated at roughly the cost of semilocal functionals and is significantly faster than hybrid DFT in plane-wave codes. By training functionals with several different feature sets, we elucidate the roles of orbital-dependent and nonlocal features in learning the exchange energy and determine that both types of features provide vital and independently important information to the model. Having trained our new exchange functional with an expressive, nonlocal feature set, we substitute it into existing hybrid functionals to achieve hybrid-DFT accuracy on thermochemical benchmark sets and improve the accuracy of band gap predictions over semilocal DFT. To demonstrate the scalability of our approach as well as the practical benefits of improved band gap prediction, we compute charged defect transition levels in silicon using large supercells. Due to its transferability and computational efficiency for both molecular and extended systems, our model overcomes the cost-accuracy trade-off between semilocal and hybrid DFT, and our general approach provides a feasible path toward a universal exchange-correlation functional with post-hybrid DFT accuracy and semilocal DFT cost. Hybrid functional ; Density functional theory ; Scalability ; Benchmark (surveying) ; Computer science ; Feature (linguistics) ; Hybrid system ; Set (abstract data type) ; Statistical physics ; Algorithm ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Kevin M. Dalton | Jack B. Griesman | Doeke R. Hekstra
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-07-07)
Field: Materials Science — Materials Chemistry

Variational inference for scaling unconventional diffraction data In order to obtain electron density, integrated reflection intensities must be averaged and converted to structure factors.Intensities cannot be merged directly because they are contaminated with systematic errors which vary throughout the data set.These errors originate from a variety of sources including the volume of crystal in the X-ray beam, the mosaic properties of the crystal, and fluctuations in beam intensity.In the conventional setting, it is straightforward to correct these errors through an optimization algorithm known as scaling.In the case of serial and time-resolved crystallography, scaling is complicated by the experimental design.Unlike conventional, rotation-method crystallography, reflections originating from short, monochromatic X-ray pulses are partial; they do not reflect the full intensity of the corresponding Bragg peak.Partiality may either be corrected using a model of the Bragg peaks' shape or by averaging over a large number of images.I will present a third alternative which is agnostic to the lineshape of Bragg peaks and works with a small number of images.This method uses a statistical technique known as variational inference combined with deep learning to jointly learn structure factors along with a scaling model.In this way, the model chooses the optimal corrections for each particular experiment.Though it is applicable to conventional data as well, I anticipate this technique will be most valuable for time-resolved and serial experiments with both mono and polychromatic X-rays. Inference ; Scaling ; Diffraction ; Statistical physics ; Materials science ; Computer science ; Physics ; Mathematics ; Geometry ; Artificial intelligence ; Optics X-ray Diffraction in Crystallography
Authors: James Goff | Yu Zhang | Christian F. A. Negre | Andrew Rohskopf | Anders M. N. Niklasson
Source: arXiv (Cornell University) (preprint, 2023-03-22)
Field: Materials Science — Materials Chemistry

Shadow molecular dynamics and atomic cluster expansions for flexible charge models A shadow molecular dynamics scheme for flexible charge models is presented, where the shadow Born-Oppenheimer potential is derived from a coarse-grained approximation of range-separated density functional theory. The interatomic potential, including the atomic electronegativities and the charge-independent short-range part of the potential and force terms, are modeled by the linear atomic cluster expansion (ACE), which provides a computationally efficient alternative to many machine learning methods. The shadow molecular dynamics scheme is based on extended Lagrangian (XL) Born-Oppenheimer molecular dynamics (BOMD) [Eur. Phys. J. B 94, 164 (2021)]. XL-BOMD provides a stable dynamics, while avoiding the costly computational overhead associated with solving an all-to-all system of equations, which normally is required to determine the relaxed electronic ground state prior to each force evaluation. To demonstrate the proposed shadow molecular dynamics scheme for flexible charge models using the atomic cluster expansion, we emulate the dynamics generated from self-consistent charge density functional tight-binding (SCC-DFTB) theory using a second-order charge equilibration (QEq) model. The charge-independent potentials and electronegativities of the QEq model are trained for a supercell of uranium oxide (UO2) and a molecular system of liquid water. The combined ACE + XL-QEq dynamics are stable over a wide range of temperatures both for the oxide and the molecular systems, and provide a precise sampling of the Born-Oppenheimer potential energy surfaces. Accurate ground Coulomb energies are produced by the ACE-based electronegativity model during an NVE simulation of UO2, predicted to be within 1 meV of those from SCC-DFTB on average during comparable simulations. Molecular dynamics ; Chemistry ; Density functional theory ; Charge (physics) ; Electronegativity ; Supercell ; Cluster (spacecraft) ; Statistical physics ; Coupled cluster ; Computational chemistry ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Chemical Physics Studies
Authors: Ziyue Wang
Source:  (article, 2023-11-01)
Field: Chemistry — Analytical Chemistry

Pesticide residue detection technology based on hyperspectral With the development of the times, due to the pursuit of healthy diet, people are paying more and more attention to the safety of vegetables and fruits. In recent years, there have been frequent safety issues in fruits and vegetables in China. The abuse of pesticides has made it difficult to clean residual pesticides on fruits and vegetables, and residual pesticides have a significant impact on physical health. In the past, relying on the experience of fruit farmers to judge has become outdated, and the cost of chemical colorimetric detection technology and other technologies is high and complex. Therefore, the use of hyperspectral technology for vegetable and fruit detection has become the mainstream detection method in recent years. This technology can analyze crops through spectroscopy, and use machine learning algorithms to process the data. This article will introduce the progress and methods achieved in the application of hyperspectral technology in fruit and vegetable detection in the past six years, for the convenience of scientific researchers for reference. Hyperspectral imaging ; Pesticide ; Residual ; Agrochemical ; Pesticide residue ; Environmental science ; Agricultural engineering ; Computer science ; Agriculture ; Engineering ; Agronomy ; Artificial intelligence ; Geography ; Algorithm Spectroscopy and Chemometric Analyses
Authors: Melisa Kiran | Zeynep Pinar Haslak | Halit Ates | Vi̇ktorya Avi̇yente | F. Ahu Akin
Source: Research Square (preprint, 2023-09-02)
Field: Chemistry — Organic Chemistry

Evaluation of the pKa's of Quinazoline Derivatives : Usage of Quantum Mechanical Based Descriptors Abstract In this study, several quantum mechanical-based computational approaches have been used in order to propose accurate protocols for predicting the p K a ’s of quinazoline derivatives, which constitute a very important class of natural and synthetic compounds in organic, pharmaceutical, agricultural and medicinal chemistry areas. Linear relationships between the experimental p K a ’s and nine different DFT descriptors (atomic charge on nitrogen atoms ( Q (N), ionization energy ( I ), electron affinity ( A ), chemical potential (µ), hardness (η), electrophilicity index (ω), fukui functions ( f + , f − ), condensed dual descriptor (Δ f ) and local hypersoftness (\({s}_{k}^{\left(2\right)}\)) were considered. Several DFT methods (a combination of five DFT functionals and two basis sets) in conjunction with two different implicit solvent models were tested, and among them, M06L/6-311 + + G(d,p) level of theory employing the CPCM solvation model was found to give the strongest correlations between the DFT descriptors and the experimental p K a ’s of the quinazoline derivatives. The calculated atomic charge on N 1 atom ( Q (N 1 )) was shown to be the best descriptor to reproduce the experimental p K a ’s (R 2 = 0.927), whereas strong correlations were also derived for A , ω, µ, \({s}_{k}^{\left(2\right)}\) and Δ f . The QM-based protocols presented in this study will enable fast and accurate high-throughput p K a predictions of quinazoline derivatives and the relationships derived can be effectively used in data generation for successful machine learning models for p K a predictions. Quinazoline ; Solvation ; Electrophile ; Ionization energy ; Computational chemistry ; Atom (system on chip) ; Electron affinity (data page) ; Chemistry ; Charge (physics) ; Quantum chemical ; Ionization ; Mathematics ; Solvent ; Computer science ; Physics ; Stereochemistry ; Quantum mechanics ; Molecule ; Ion ; Organic chemistry Free Radicals and Antioxidants ; Nonlinear Optical Materials Research ; Metal complexes synthesis and properties
Authors: Amal Yousfan | Mhd Jawad Al Rahwanji | Abdulsamie Hanano | Hisham Al-Obaidi
Source: Research Square (preprint, 2023-06-05)
Field: Materials Science — Biomaterials

Unraveling the Intricacies of Nanoparticle Targeting of Brain Therapeutics for Enhanced Efficacy Abstract The delivery of drugs to specific target tissues and cells in the brain poses a significant challenge in brain therapeutics, primarily due to a limited understanding of how nanoparticle properties influence drug biodistribution and off-target organ accumulation. This study addresses the limitations of previous research by presenting a novel and innovative approach that utilizes a large dataset incorporating both numerical and categorical features. Unlike previous studies, which often suffer from inadequate data and accuracy, our focus is specifically on brain delivery in vivo testing. Through a systematic analysis of 403 data points, including measurements of brain and plasma area under the curve (AUC) following nanoparticle (NP) administration, we extensively characterized the chemical and physical properties of loaded drugs and NPs as carriers. Utilizing machine learning techniques and comprehensive literature data analysis, we developed accurate models for predicting NP delivery to the brain. Our analysis employed various linear models, with a particular emphasis on the superior performance of linear mixed-effects models. These models demonstrated exceptional accuracy, as indicated by high R 2 and adjusted R 2 scores, in predicting the delivery of NPs to the brain. This breakthrough provides a valuable tool for guiding the design of nanocarriers in clinical applications, resulting in reduced experimental costs and the mitigation of unintended biological outcomes. To validate our models, we synthesised and administered two distinct NP formulations via intranasal (IN) and intravenous (IV) routes, further substantiating the efficacy of our proposed models. Among the various modeling approaches, linear mixed-effects models exhibited superior performance in capturing underlying patterns. Nanoparticle ; Nanotechnology ; Chemistry ; Computer science ; Materials science Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; RNA Interference and Gene Delivery
Source: Agriculture and Natural Resources (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Automatic detection of scratches on chicken carcass in slaughter factory using image processing and machine learning Importance of the work:Ensuring the quality of chicken products is paramount, as low-quality chicken not only impacts consumer health but also results in time and financial losses.Automation can improve production by reducing labor costs and increasing efficiency.Objectives: To develop a software program to process video images acquired on a chicken carcass production line and categorize each carcass based on identified scratches. Materials & Methods:The treatment consisted of counting the scratches on chickens to categorize them in terms of meat quality.The developed software used machine learning and model building to create models to detect scratches on the chicken carcass.The software identified and marked groups of pixels defined by the patterns on the image.Results were compared to those from manual assessment by an experienced operator.Results: The software achieved 95% accuracy and directly processed videos without the need for pre-processing, such as background removal.The carcass categories were determined based on the number of scratches based on calibration with the software.Each of the three mislabeled chickens had a significant scratch, but no large scratches are observed.The grade of the chicken was directly affected by these scratches, with important factors being poor detection and the absence of a large scratch pattern.Main finding: The program could be improved by incorporating calibration from both sides of the carcass.This would improve the adaptability of the developed system.It should be possible to develop a system using visual image data to automatically sort chickens on the production line into different categories. Factory (object-oriented programming) ; Image processing ; Artificial intelligence ; Computer vision ; Computer science ; Pattern recognition (psychology) ; Image (mathematics) Spectroscopy and Chemometric Analyses
Authors: Filip Buric | Sandra Viknander | Xiaozhi Fu | Oliver Lemke | Jan Zrimec | Łukasz Szyrwiel | Michael Mueleder | Markus Ralser | Aleksej Zelezniak
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-09-25)
Field: Materials Science — Materials Chemistry

The amino acid sequence determines protein abundance through its conformational stability and reduced synthesis cost <strong>Understanding what drives protein abundance is essential to biology, medicine, and biotechnology. Driven by evolutionary selection, amino acid sequence is tailored to meet the required abundance of proteomes, underscoring the intricate relationship between sequence and functional demand of proteomes. Yet, the specific role of amino acid sequences in determining proteome abundance remains elusive. Here, we demonstrate that the amino acid sequence is predictive of protein abundance by shaping a protein’s conformational stability. We show that increasing the abundance provides metabolic cost benefits, underscoring the evolutionary advantage of maintaining a highly abundant and stable proteome. Specifically, using a deep learning model (BERT), we predict 56% of protein abundance variation in <em>Saccharomyces cerevisiae </em>solely based on amino acid sequence. The model reveals latent factors linking sequence features to protein stability. To probe these relationships, we introduce MGEM (Mutation Guided by an Embedded Manifold), a methodology for guiding protein abundance through sequence modifications. We find that mutations increasing abundance significantly alter protein polarity and hydrophobicity, underscoring a connection between protein stability and abundance. Through molecular dynamics simulations and <em>in vivo</em> experiments in yeast, we confirm that abundance-enhancing mutations result in longer lasting and more stable protein expression. Importantly, these sequence changes also reduce metabolic costs of protein synthesis, elucidating the evolutionary advantage of cost-effective, high-abundance, stable proteomes. Our findings support the role of amino acid sequence as a pivotal determinant of protein abundance and stability, revealing an evolutionary optimisation for metabolic efficiency.</strong> Sequence (biology) ; Abundance (ecology) ; Chemistry ; Amino acid ; Peptide sequence ; Protein stability ; Biochemistry ; Biology ; Ecology Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced Proteomics Techniques and Applications
Authors: Sergei Manzhos | Manabu Ihara
Source: Machine Learning with Applications (preprint, 2023-07-27)
Field: Materials Science — Materials Chemistry

Rectangularization of Gaussian process regression for optimization of hyperparameters Gaussian process regression (GPR) is a powerful machine learning method which has recently enjoyed wider use, in particular in physical sciences. In its original formulation, GPR uses a square matrix of covariances among training data and can be viewed as linear regression problem with equal numbers of training data and basis functions. When data are sparse, avoidance of overfitting and optimization of hyperparameters of GPR are difficult, in particular in high-dimensional spaces where the data sparsity issue cannot practically be resolved by adding more data. Optimal choice of hyperparameters, however, determines success or failure of the application of the GPR method. We show that parameter optimization is facilitated by rectangularization of the defining equation of GPR. On the example of a 15-dimensional molecular potential energy surface we demonstrate that this approach allows effective hyperparameter tuning even with very sparse data. Hyperparameter ; Kriging ; Gaussian process ; Computer science ; Hyperparameter optimization ; Regression ; Process (computing) ; Gaussian ; Machine learning ; Artificial intelligence ; Mathematical optimization ; Optimization problem ; Algorithm ; Mathematics ; Statistics ; Support vector machine ; Chemistry Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Spectroscopy Techniques in Biomedical and Chemical Research