jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gennady M. Verkhivker | Steve Agajanian | Guang Hu | Peng Tao
Source: Frontiers in Molecular Biosciences (review, 2020-07-09)
Field: Materials Science — Materials Chemistry

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets. Allosteric regulation ; Artificial intelligence ; Computer science ; Machine learning ; Computational biology ; Chemistry ; Biology ; Biochemistry ; Enzyme Machine Learning in Materials Science
Authors: Mauricio Fernández | Shahed Rezaei | Jaber Rezaei Mianroodi | Felix Fritzen | Stefanie Reese
Source: Advanced Modeling and Simulation in Engineering Sciences (article, 2020-01-28)
Field: Materials Science — Materials Chemistry

Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior Abstract The present work aims at the identification of the effective constitutive behavior of $$\Sigma 5$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Σ</mml:mi><mml:mn>5</mml:mn></mml:mrow></mml:math> aluminum grain boundaries (GB) for proportional loading by using machine learning (ML) techniques. The input for the ML approach is high accuracy data gathered in challenging molecular dynamics (MD) simulations at the atomic scale for varying temperatures and loading conditions. The effective traction-separation relation is recorded during the MD simulations. The raw MD data then serves for the training of an artificial neural network (ANN) as a surrogate model of the constitutive behavior at the grain boundary. Despite the extremely fluctuating nature of the MD data and its inhomogeneous distribution in the traction-separation space, the ANN surrogate trained on the raw MD data shows a very good agreement in the average behavior without any data-smoothing or pre-processing. Further, it is shown that the trained traction-separation ANN captures important physical properties and is able to predict traction values for given separations not contained in the training data. For example, MD simulations show a transition in traction-separation behaviour from pure sliding mode under shear load to combined GB sliding and decohesion with intermediate hardening regime at mixed load directions. These changes in GB behaviour are fully captured in the ANN predictions. Furthermore, by construction, the ANN surrogate is differentiable for arbitrary separation and also temperature, such that a thermo-mechanical tangent stiffness operator can always be evaluated. The trained ANN can then serve for large-scale FE simulation as an alternative to direct MD-FE coupling which is often infeasible in practical applications. Artificial neural network ; Smoothing ; Constitutive equation ; Artificial intelligence ; Materials science ; Computer science ; Machine learning ; Algorithm ; Thermodynamics ; Physics ; Finite element method Microstructure and mechanical properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Simone Venturi | Richard L. Jaffe | Marco Panesi
Source: The Journal of Physical Chemistry A (article, 2020-05-28)
Field: Materials Science — Materials Chemistry

Bayesian Machine Learning Approach to the Quantification of Uncertainties on Ab Initio Potential Energy Surfaces This work introduces a novel methodology for the quantification of uncertainties associated with potential energy surfaces (PESs) computed from first-principles quantum mechanical calculations. The methodology relies on Bayesian inference and machine learning techniques to construct a stochastic PES and to express the inadequacies associated with the ab initio data points and their fit. By combining high fidelity calculations and reduced-order modeling, the resulting stochastic surface is efficiently forward propagated via quasi-classical trajectory and master equation calculations. In this way, the PES contribution to the uncertainty on predefined quantities of interest (QoIs) is explicitly determined. This study is done at both microscopic (e.g., rovibrational-specific rate coefficients) and macroscopic (e.g., thermal and chemical relaxation properties) levels. A correlation analysis is finally applied to identify the PES regions that require further refinement, based on their effects on the QoI reliability. The methodology is applied to the study of singlet (1<sup>1</sup>A') and quintet (2<sup>5</sup>A') PESs describing the interaction between O<sub>2</sub> molecules and O atoms in their ground electronic state. The investigation of the singlet surface reveals a negligible uncertainty on the kinetic properties and relaxation times, which are found to be in excellent agreement with the ones previously published in the literature. On the other hand, the methodology demonstrated significant uncertainty on the quintet surface, due to inaccuracies in the description of the exchange barrier and the repulsive wall. When forward propagated, this uncertainty is responsible for the variability of 1 order of magnitude in the vibrational relaxation time and of factor four in the exchange reaction rate coefficient, both at 2500 K. Ab initio ; Rotational–vibrational spectroscopy ; Statistical physics ; Relaxation (psychology) ; Potential energy surface ; Computer science ; Physics ; Chemistry ; Quantum mechanics ; Excited state Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Fuel Cells and Related Materials
Authors: Xinming Qin | Jielan Li | Wei Hu | Jinlong Yang
Source: The Journal of Physical Chemistry A (article, 2020-11-17)
Field: Materials Science — Materials Chemistry

Machine Learning K-Means Clustering Algorithm for Interpolative Separable Density Fitting to Accelerate Hybrid Functional Calculations with Numerical Atomic Orbitals The interpolative separable density fitting (ISDF) is an efficient and accurate low-rank decomposition method to reduce the high computational cost and memory usage of the Hartree-Fock exchange (HFX) calculations with numerical atomic orbitals (NAOs). In this work, we present a machine learning K-means clustering algorithm to select the interpolation points in ISDF, which offers a much cheaper alternative to the expensive QR factorization with column pivoting (QRCP) procedure. We implement this K-means-based ISDF decomposition to accelerate hybrid functional calculations with NAOs in the HONPAS package. We demonstrate that this method can yield a similar accuracy for both molecules and solids at a much lower computational cost. In particular, K-means can remarkably reduce the computational cost of selecting the interpolation points by nearly two orders of magnitude compared to QRCP, resulting in a speedup of ∼10 times for ISDF-based HFX calculations. Interpolation (computer graphics) ; Cluster analysis ; Separable space ; Algorithm ; Speedup ; Computer science ; QR decomposition ; Atomic orbital ; Hybrid functional ; Mathematics ; Density functional theory ; Computational chemistry ; Physics ; Artificial intelligence ; Quantum mechanics ; Chemistry ; Parallel computing ; Mathematical analysis Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Dakota Folmsbee | Geoffrey Hutchison
Source: International Journal of Quantum Chemistry (article, 2020-07-09)
Field: Materials Science — Materials Chemistry

Assessing conformer energies using electronic structure and machine learning methods Abstract We have performed a large‐scale evaluation of current computational methods, including conventional small‐molecule force fields; semiempirical, density functional, ab initio electronic structure methods; and current machine learning (ML) techniques to evaluate relative single‐point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP‐D3BJ with single‐point DLPNO‐CCSD(T) triple‐zeta energies, we consider over 6500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find that the current ML methods have potential and recommend methods at each tier of the accuracy‐time tradeoff, particularly the recent GFN2 semiempirical method, the B97‐3c density functional approximation, and RI‐MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI‐1ccx variant trained in part on coupled‐cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy. Maxima and minima ; Density functional theory ; Ab initio ; Coupled cluster ; Conformational isomerism ; Electronic structure ; Single point ; Point (geometry) ; Cluster (spacecraft) ; Molecule ; Statistical physics ; Molecular physics ; Physics ; Chemistry ; Computational chemistry ; Computer science ; Quantum mechanics ; Mathematics ; Geometry ; Computer simulation ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Aron Deneyer | Quanli Ke | Julien Devos | Michiel Dusselier
Source: Chemistry of Materials (article, 2020-04-30)
Field: Chemistry — Inorganic Chemistry

Zeolite Synthesis under Nonconventional Conditions: Reagents, Reactors, and<i>Modi Operandi</i> A myriad of tetrahedral molecular sieve frameworks, often siliceous, can be calculated in silico. Only a tiny fraction (<0.1%) of these can be synthesized on purpose. Only a small fraction of these available frameworks, mostly those composed of only Si and Al as T atoms, i.e., true zeolites, are used commercially. A gap thus exists between what should be possible (thermodynamically) and what can be produced (kinetically) and used in real life. Even if a synthesis is successful (in industry or academia), flexibility with regard to synthesis parameters—in terms of time, amount of unit operations, OSDA efficiency, etc.—as well as the obtained material properties—in terms of Si/Al ratio, Al distribution, T atom variety, crystal size, etc.—remains limited. These limitations are not surprising, since conventional zeolite syntheses, i.e., hydrothermal synthesis in batch from amorphous or soluble Si and Al sources, have limited degrees of freedom (DOFs). Typically, the type of ingredients, their ratios, a constant temperature, the synthesis time, and the absence or presence of agitation are varied. In order to take new steps toward more cost-competitive syntheses, and more importantly zeolites with a greater flexibility in terms of structural properties, this Review highlights all DOFs that can be introduced in addition to or on top of the conventional way of synthesis. By doing this, a distinction is made between nonconventional DOFs that influence the chemistry of the system (e.g., interzeolite conversion, charge density mismatch, ionothermal or free-radical assisted synthesis) and nonconventional DOFs that influence the physical environment (e.g., ultrasounds, alternative energy via microwaves, or continuous setups). The Review concludes with learnings, practical insights, and future opportunities. In other words, which zeolite synthesis strategies really make a difference and which ones are just tweaking around the edges? Zeolite ; Molecular sieve ; Hydrothermal synthesis ; Flexibility (engineering) ; Atom (system on chip) ; Degrees of freedom (physics and chemistry) ; Materials science ; Nanotechnology ; Chemical engineering ; Amorphous solid ; Chemistry ; Catalysis ; Crystallography ; Computer science ; Organic chemistry ; Hydrothermal circulation ; Thermodynamics ; Physics ; Mathematics Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalytic Processes in Materials Science
Authors: Jinnuo Zhang | Yong Yang | Xuping Feng | Hongxia Xu | Jianping Chen | Yong He
Source: Frontiers in Plant Science (article, 2020-06-24)
Field: Chemistry — Analytical Chemistry

Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network Because bacterial blight (BB) disease seriously affects the yield and quality of rice, breeding BB resistant rice is an important priority for plant breeders but the process is time-consuming. The feasibility of using terahertz imaging technology and near-infrared hyperspectral imaging technology to identify BB resistant seeds has therefore been studied. The two-dimensional (2D) spectral images and one-dimensional (1D) spectra provided by both imaging methods were used to build discriminant models based on a deep learning method, the convolutional neural network (CNN), and traditional machine learning methods, support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The highest classification accuracy was achieved by the discriminate model based on CNN using the terahertz absorption spectra. Confusion matrixes were pictured to show the identification details. The t-distributed stochastic neighbor embedding (t-SNE) method was used to visualize the process of CNN data processing. Terahertz imaging technology combined with CNN has great potential to quickly identify BB resistant rice seeds and is more accurate than using near-infrared hyperspectral imaging. Hyperspectral imaging ; Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Support vector machine ; Computer science ; Linear discriminant analysis ; Multispectral image ; Random forest ; Terahertz radiation ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Terahertz technology and applications
Authors: Mohsen Ghaferi | Samar Amari | Bhalchandra Vivek Mohrir | Aun Raza | Hasan Ebrahimi Shahmabadi | Seyed Ebrahim Alavi
Source: Pharmaceuticals (article, 2020-03-11)
Field: Materials Science — Biomaterials

Preparation, Characterization, and Evaluation of Cisplatin-Loaded Polybutylcyanoacrylate Nanoparticles with Improved In Vitro and In Vivo Anticancer Activities This study aimed to evaluate the therapeutic efficacy of the cisplatin encapsulated into polybutylcyanoacrylate (PBCA) nanoparticles for the treatment of kidney cancer. The nanoformulation was successfully developed using the miniemulsion polymerization method and characterized in terms of size, size distribution, drug loading and encapsulation efficiencies, drug release behavior, in vitro cytotoxicity effects, in vivo toxicity, and therapeutic effects. Cisplatin-loaded PBCA nanoparticles were confirmed to be in nanoscale with the drug entrapment efficiency of 23% and controlled drug release profile, in which only 9% of the loaded drug was released after 48 h. The nanoparticles caused an increase in the cytotoxicity effects of cisplatin against renal cell adenocarcinoma cells (ACHN) (2.3-fold) and considerably decreased blood urea nitrogen and creatinine concentrations when compared to the standard cisplatin (1.6-fold and 1.5-fold, respectively). The nanoformulation also caused an increase in the therapeutic effects of cisplatin by 1.8-fold, in which a reduction in the mean tumor size was seen (3.5 mm vs. 6.5 mm) when compared to the standard cisplatin receiver rats. Overall, cisplatin-loaded PBCA nanoparticles can be considered as a promising drug candidate for the treatment of kidney cancer due to its potency to reduce the side effects of cisplatin and its toxicity and therapeutic effects on cancer-bearing Wistar rats. Cisplatin ; In vivo ; Pharmacology ; Cytotoxicity ; Toxicity ; Nephrotoxicity ; Chemistry ; Drug ; In vitro ; Potency ; Medicine ; Chemotherapy ; Biochemistry ; Surgery ; Biology Nanoparticle-Based Drug Delivery ; Chemotherapy-induced organ toxicity mitigation ; Lanthanide and Transition Metal Complexes
Authors: Jelena Munćan | Kyoko Tei | Roumiana Tsenkova
Source: Sensors (article, 2020-12-29)
Field: Chemistry — Analytical Chemistry

Real-Time Monitoring of Yogurt Fermentation Process by Aquaphotomics Near-Infrared Spectroscopy Automated quality control could have a substantial economic impact on the dairy industry. At present, monitoring of yogurt production is performed by sampling for microbiological and physicochemical measurements. In this study, Near-Infrared Spectroscopy (NIRS) is proposed for non-invasive automated control of yogurt production and better understanding of lactic acid bacteria (LAB) fermentation. UHT (ultra-high temperature) sterilized milk was inoculated with Bulgarian yogurt and placed into a quartz cuvette (1 mm pathlength) and test-tubes. Yogurt absorbance spectra (830-2500 nm) were acquired every 15 min, and pH, in the respective test-tubes, was measured every 30 min, during 8 h of fermentation. Spectral data showed substantial baseline and slope changes with acidification. These variations corresponded to respective features of the microbiological growth curve showing water structural changes, protein denaturation, and coagulation of milk. Moving Window Principal Component Analysis (MWPCA) was applied in the spectral range of 954-1880 nm to detect absorbance bands where most variations in the loading curves were caused by LAB fermentation. Characteristic wavelength regions related to the observed physical and multiple chemical changes were identified. The results proved that NIRS is a valuable tool for real-time monitoring and better understanding of the yogurt fermentation process. Cuvette ; Fermentation ; Absorbance ; Food science ; Lactic acid ; Chemistry ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Bacteria ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Holly M. Lane | Seth C. Murray | Osval A. Montesinos‑López | Abelardo Montesinos‐López | José Crossa | David K. Rooney | Ivan D. Barrero‐Farfan | Gerald N. De La Fuente | Cristine L.S. Morgan
Source: The Plant Phenome Journal (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Phenomic selection and prediction of maize grain yield from near‐infrared reflectance spectroscopy of kernels Abstract High‐throughput phenotyping technologies, which can generate large volumes of data at low costs, may be used to indirectly predict yield. We explore this concept, using high‐throughput phenotype information from Fourier transformed near‐infrared reflectance spectroscopy (NIRS) of harvested kernels to predict parental grain yield in maize ( Zea mays L.), and demonstrate a proof of concept for phenomic‐based models in maize breeding. A dataset of 2,563 whole‐kernel samples from a diversity panel of 346 hybrid testcrosses were scanned on a plot basis using NIRS. Scans consisted of 3,076 wavenumbers (bands) in the range of 4,000–10,000 cm −1 . Corresponding grain yield for each sample was used to train phenomic prediction and selection models using three types of statistical learning: (a) partial least square regression (PLSR), (b) NIRS best linear unbiased predictor (NIRS BLUP), and (c) functional regression. Our results found that NIRS data were a useful tool to predict maize grain yield and showed promising results for evaluating genetically independent breeding populations. All model types were successful; functional regression followed by the PLSR model resulted in the best predictions. Pearson's correlations between predicted and observed grain yields exceeded .7 in many cases within random cross validation. Partial least squares regression also showed promise on independent breeding trials. More research on predicting phenotypic traits from spectra will provide better understanding how NIRS and other phenomic technology can be used in predicting phenotypes of breeding programs. Partial least squares regression ; Best linear unbiased prediction ; Regression ; Linear regression ; Statistics ; Near infrared reflectance spectroscopy ; Regression analysis ; Selection (genetic algorithm) ; Grain yield ; Mathematics ; Biology ; Near-infrared spectroscopy ; Agronomy ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Genetics and Plant Breeding
Authors: Yun Zhang | Xiaojie Xu
Source: Journal of Materials Engineering and Performance (article, 2020-10-01)
Field: Materials Science — Materials Chemistry

Machine Learning Decomposition Onset Temperature of Lubricant Additives Lubricant ; Materials science ; Thermal decomposition ; Decomposition ; Thermal stability ; Thermal ; Chemical engineering ; Thermodynamics ; Composite material ; Organic chemistry ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure