jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Steven B. Torrisi | Matthew R. Carbone | Brian A. Rohr | Joseph H. Montoya | Yang Ha | Junko Yano | Santosh K. Suram | Linda Hung
Source: ChemRxiv (preprint, 2020-02-20)
Field: Materials Science — Materials Chemistry

Random Forest Machine Learning Models for Interpretable X-Ray Absorption Near-Edge Structure Spectrum-Property Relationships X-ray absorption spectroscopy (XAS) produces a wealth of information about the local structure of materials, but interpretation of spectra often relies on easily accessible trends and prior assumptions about the structure. Recently, researchers have demonstrated that machine learning models can automate this process to predict the coordinating environments of absorbing atoms from their XAS spectra. However, machine learning models are often difficult to interpret, making it challenging to determine when they are valid and whether they are consistent with physical theories. In this work, we present three main advances to the data-driven analysis of XAS spectra: we demonstrate the efficacy of random forests in solving two new property determination tasks (predicting Bader charge and mean nearest neighbor distance), we show that multiscale featurization can elucidate the regions and trends in spectra that encode various local properties, and we address the effect of normalization on model interpretability. The multiscale featurization transforms the spectrum into a vector of polynomial-fit features, and is contrasted with the commonly-used "pointwise" featurization that directly uses the entire spectrum as input. We find that across thousands of transition metal oxide spectra, the relative importance of features describing the curvature of the spectrum can be localized to individual energy ranges, and we can separate the importance of constant, linear, quadratic, and cubic trends, as well as the white line energy. This work has the potential to assist rigorous theoretical interpretations, expedite experimental data collection, and automate analysis of XAS spectra, thus accelerating discovery of new functional materials. Pointwise ; Interpretability ; X-ray absorption spectroscopy ; Computer science ; Spectral line ; Support vector machine ; Artificial intelligence ; Random forest ; Machine learning ; Absorption spectroscopy ; Statistical physics ; Algorithm ; Mathematics ; Physics ; Optics ; Mathematical analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Radiomics and Machine Learning in Medical Imaging
Authors: Vadim Korolev | Artem Mitrofanov | Yaroslav Kucherinenko | Yurii M. Nevolin | Vladimir Krotov | Pavel Protsenko
Source: SSRN Electronic Journal (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Accelerated Modeling of Interfacial Phases in the Ni-Bi System with Machine Learning Interatomic Potential Interatomic potential ; Materials science ; Computer science ; Chemistry ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Theoretical and Computational Physics
Authors: Alex P. Cocco | Kyle N. Grew
Source: Applied Materials Today (article, 2020-06-24)
Field: Materials Science — Materials Chemistry

Analytical transport network theory &amp; structural mechanics: Towards computationally inexpensive multi-functional materials design Formalism (music) ; Computer science ; Topology (electrical circuits) ; Lattice (music) ; Finite element method ; Topology optimization ; Transport theory ; Network topology ; Density functional theory ; Theoretical computer science ; Mathematical optimization ; Statistical physics ; Mathematics ; Engineering ; Structural engineering ; Physics Machine Learning in Materials Science ; Cellular and Composite Structures ; Advanced Memory and Neural Computing
Authors: Alessio Bartocci | florence szczepaniak | Tao Jiang | Natacha Gillet | Élise Dumont
Source: ChemRxiv (preprint, 2020-02-27)
Field: Chemistry — Spectroscopy

Molecular dynamics approach for capturing calixarenes--proteins interactions: the case of cytochrome c Here, we propose a molecular dynamics investigation of the supramolecular association of sulfonatedcalix-[8]-arenes to cytochrome c. The binding sites prone to interactions with sulfonated calixarenescan be identified without prior knowledge of the X-ray structure, and the binding free energiesestimated by molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) post-analysis arefound to be in neat agreement with the isothermal titration calorimetry (ITC) measurements The per-residuedecomposition reveals the detailed picture of this electrostatically-driven association and notably therole of the arginine R13 as a bridge residue between the two main anchoring sites. In addition,the analysis of the residue behavior by means of a supervised machine learning protocol unveils the formation of an hydrogen bond network far from the binding sites, increasing the rigidity of theprotein. Isothermal titration calorimetry ; Molecular dynamics ; Chemistry ; Supramolecular chemistry ; Hydrogen bond ; Cytochrome c ; Binding site ; Biophysics ; Stereochemistry ; Crystallography ; Computational chemistry ; Molecule ; Biochemistry ; Organic chemistry ; Crystal structure ; Biology Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: E. Cardelli | Antonino Laudani | Gabriele Maria Lozito | Valentina Lucaferri | Alessandro Salvini | S. Quondam Antonio | Franesco Riganti Fulginei
Source:  (article, 2020-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural Modelling of Magnetic Materials for Aircraft Power Converters Simulations Power converters often features inductive devices in their architectures. Accurate simulation of the converters requires a well-defined response of the magnetic cores. A computationally efficient approach for the numerical modelling of hysteretic magnetic materials is presented in this work. The approach exploits the simplicity of the identification procedure for the Preisach model of hysteresis and the reduced computational costs of Neural Networks. The model for hysteresis is implemented both in direct and inverse form. Validation is performed against independent dataset, with evident computational speedup, which can be a valuable asset for magnetic cores simulations in the design of complex power systems featuring multiple converters such as the ones used in avionic applications. Converters ; Computer science ; Electronic engineering ; Hysteresis ; Speedup ; Power (physics) ; Magnetic hysteresis ; Control engineering ; Engineering ; Physics ; Magnetic field ; Parallel computing Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Non-Destructive Testing Techniques
Authors: Yang Yu | Yang Jiao | Jacob Fish
Source: International Journal for Multiscale Computational Engineering (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

ATOMISTICALLY INFORMED TEMPERATURE AND RATE-DEPENDENT MECHANICAL RESPONSE OF FUSED SILICA This manuscript studies the mechanical response of amorphous silica based on data mining from molecular dynamics simulations. The temperature- and densification-dependent yield criteria have been established. The proposed modified Drucker-Prager-cap yield criterion adequately captures the temperature effect on the initial yield surface. The main focus of this study has been on understanding the critical state, which defines the onset of an inhomogeneous plastic deformation. The machine learning-based nonparametric regression method has been employed to describe complex shear critical state dependence on temperature, pressure, and strain rate. Amorphous silica ; Materials science ; Yield (engineering) ; Yield surface ; Amorphous solid ; Strain rate ; Nonparametric statistics ; Thermodynamics ; Composite material ; Mechanics ; Econometrics ; Mathematics ; Constitutive equation ; Finite element method ; Engineering ; Crystallography ; Physics ; Chemistry Polymer crystallization and properties ; High-Velocity Impact and Material Behavior ; Granular flow and fluidized beds
Authors: Jennifer R. Hiscock
Source: ePrints Soton (University of Southampton) (article, 2020-01-01)
Field: Materials Science — Biomaterials

AI3SD Video: Supramolecular Antimicrobials - the next target for AI/Machine Learning? Since the 1980s the development of novel antibiotics has dramatically reduced. This, combined with the ever-increasing prevalence of antibiotic resistance in bacteria, means that some bacterial strains have now been identified that are resistant to treatment with all known classes of antibiotic currently available. Supramolecular Self-associating Amphiphiles (SSAs) are a novel class of amphiphilic salts that contain an uneven number of covalently linked hydrogen bond donating and accepting groups, meaning that they are frustrated in nature. The hydrogen-bonded, self-associative properties for members of this class of over 70 compounds synthesised to date have been extensively studied in the gas phase, solution state, solid state and in silico. Through these studies we have shown correlations between certain physicochemical properties that maybe predicted by simple, low-level, high-throughput, easily accessible computational modelling. In addition, members from this class of compound have been shown to kill a variety of different bacteria, including those with known antibiotic resistance (e.g. Methicillin Resistant Staphylococcus aureus (MRSA)). These initial studies have highlighted within the supramolecular chemistry community a vast amount of experimental data, not yet accessed by AI/machine learning. Could data sets such as these be the next targets of interest for this community? Is there room for a consortium or community led approach to solving predictive modelling within this branch of chemistry. Supramolecular chemistry ; In silico ; Antibiotic resistance ; Chemistry ; Antibiotics ; Class (philosophy) ; Antimicrobial ; Hydrogen bond ; Combinatorial chemistry ; Computational biology ; Nanotechnology ; Computer science ; Artificial intelligence ; Biology ; Materials science ; Organic chemistry ; Biochemistry ; Molecule ; Gene Supramolecular Self-Assembly in Materials ; Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Reactions
Authors: Annika Stuke | Patrick Rinke | Milica Todorović
Source: arXiv (Cornell University) (preprint, 2020-04-01)
Field: Materials Science — Materials Chemistry

Efficient hyperparameter tuning for kernel ridge regression with\n Bayesian optimization Machine learning methods usually depend on internal parameters -- so called\nhyperparameters -- that need to be optimized for best performance. Such\noptimization poses a burden on machine learning practitioners, requiring expert\nknowledge, intuition or computationally demanding brute-force parameter\nsearches. We here address the need for more efficient, automated hyperparameter\nselection with Bayesian optimization. We apply this technique to the kernel\nridge regression machine learning method for two different descriptors for the\natomic structure of organic molecules, one of which introduces its own set of\nhyperparameters to the method. We identify optimal hyperparameter\nconfigurations and infer entire prediction error landscapes in hyperparameter\nspace, that serve as visual guides for the hyperparameter dependence. We\nfurther demonstrate that for an increasing number of hyperparameters, Bayesian\noptimization becomes significantly more efficient in computational time than an\nexhaustive grid search -- the current default standard hyperparameter search\nmethod -- while delivering an equivalent or even better accuracy.\n Hyperparameter ; Hyperparameter optimization ; Machine learning ; Bayesian optimization ; Computer science ; Artificial intelligence ; Kernel (algebra) ; Bayesian probability ; Regression ; Mathematics ; Support vector machine ; Statistics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Machine Learning and Data Classification
Authors: Daniela N. Rim | Emmanuel N. Millán | María Belén Planes | Eduardo M. Bringa | Luis G. Moyano
Source: Entre ciencia e ingeniería (article, 2020-12-21)
Field: Materials Science — Materials Chemistry

Cluster analysis for granular mechanics simulations using Machine Learning Algorithms Molecular Dynamics (MD) simulations on grain collisions allow to incorporate complex properties of dust interactions. We performed simulations of collisions of porous grains, each with many particles, using the MD software LAMMPS. The simulations consisted of a projectile grain striking a larger immobile target grain, with different impact velocities. The disadvantage of this method is the large computational cost due to a large number of particles being modeled. Machine Learning (ML) has the power to manipulate large data and build predictive models which could reduce MD simulation times. Using ML algorithms (Support Vector Machine and Random Forest) we are able to predict the outcome of MD simulations regarding fragment formation, after a number of steps smaller than in usual MD simulations. We achieved a time reduction of at least 46%, for 90% accuracy. These results show that SVM and RF can be powerful yet simple tools to reduce computational cost in collision fragmentation simulations. Humanities ; Physics ; Philosophy Machine Learning in Materials Science ; Software Engineering Research
Authors: Yu. V. Pankiv | Kh. V. Pankiv
Source: METHODS AND DEVICES OF QUALITY CONTROL (article, 2020-12-29)
Field: Materials Science — Materials Chemistry

USE OF A ROLLED DEEP NEURAL NETWORK TO DETERMINE THE CHANGE OF THE STRESSED AND DEFORMED STATE OF VERTICAL STEEL CYLINDERAL TANKS BY THE MOVEMENT OF THEIR SURFACE The main place in determining the mechanical characteristics of the RVS material is occupied by methods and means of VAT control, which include the method of coercive force; magnetic anisotropy method; Barkhausen method, magnetic metal memory (MPM) method, strain gauge method. The advantages of magnetic methods are the ability to control without decommissioning the RVS, safety. The disadvantages inherent in almost all magnetic control methods include the need to prepare the controlled surface, the difficulty of determining the position of the sensors in relation to the maximum loads, the dependence of control results on methods and conditions of measurement, the influence of the air layer between the sensor and the controlled surface.It's shown that to determine the further safe operation possibility of vertical steel cylindrical tank it is necessary to know their stress-strain state. The shortcomings of the existing experimental and mathematical methods of its estimation were highlighted. It's proposed to use a convolutional deep neural network to determine the stress state of a vertical steel cylindrical tank. As input data, it's proposed to use data on the movement of its wall obtained, for example, as a result of geometric calibration at two points in time. The input data was presented in the form of an array of dimensions 8x12, then used convolution and max-pulling. The last layer is fully connected. It's proposed to use cross-entropy as a cost function. To increase the amount of training data, it is proposed to use the values of displacements on stresses obtained by modeling different effects on a cylindrical tank with different shape defects using the SolidWorks package. Possible ways to improve the proposed method are proposed.For further research and improvement of the proposed method, you can try to use other hyper parameters in the neural network, in particular to change the number of feature maps, the size of the local receptive field, the size of the shift step of the receptive field and others.You can also try using the source layer with a different number of neurons and softmax as a function of cost. Artificial neural network ; Strain gauge ; Structural engineering ; Calibration ; Position (finance) ; Stress (linguistics) ; Barkhausen stability criterion ; Computer science ; Mechanical engineering ; Engineering ; Artificial intelligence ; Mathematics ; Magnetic field ; Magnetization Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Electric Power Systems and Control
Authors: Yechezkel Barenholz | Aleck Sedykh | Alexander Tropsha | Alexander Golbraikh | Amiram Goldblum | Ahuva Cern
Source: UNC Libraries (article, 2020-11-04)
Field: Materials Science — Biomaterials

Quantitative structure - property relationship modeling of remote liposome loading of drugs Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a dataset including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and five-fold external validation. The external prediction accuracy for binary models was as high as 91–96%; for continuous models the mean coefficient R2 for regression between predicted versus observed values was 0.76–0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments. Liposome ; Property (philosophy) ; Biological system ; Computer science ; Materials science ; Nanotechnology ; Biology ; Epistemology ; Philosophy Nanoparticle-Based Drug Delivery ; Protein purification and stability ; Force Microscopy Techniques and Applications
Authors: Dakota Folmsbee | Geoffrey Hutchison
Source: ChemRxiv (preprint, 2020-03-03)
Field: Materials Science — Materials Chemistry

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods 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 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods 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 ; Conformational isomerism ; Coupled cluster ; Electronic structure ; Point (geometry) ; Single point ; Cluster (spacecraft) ; Statistical physics ; Physics ; Molecule ; Molecular physics ; Computer science ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Geometry ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Mark A. Boyer | Anne McCoy | Jacob M. Finney | Ryan J. DiRisio
Source: Proceedings of the 2020 International Symposium on Molecular Spectroscopy (article, 2020-06-23)
Field: Materials Science — Surfaces, Coatings and Films

DIFFUSION MONTE CARLO USING MACHINE LEARNING POTENTIAL ENERGY SURFACES Diffusion Monte Carlo (DMC) is a technique for obtaining the ground-state solution to the vibrational time-independent Schrödinger equation based on a stochastic sampling of an electronic potential energy surface (PES). To obtain accurate results, one needs a globally-accurate PES. Ideally, the electronic energies used in DMC are calculated at the CCSD(T) level of theory. Recently, neural networks have shown promise to provide accurate post-Hartree corrections to the electronic energy at the cost of a standard Hartree-Fock calculation\\footnote{Welborn, M. , Cheng, L., Miller, T. M. JCTC. 2018 14 (9), 4772-4779}\\footnote{Cheng, L., Kovachki, N. B., Welborn, M., Miller, T. M. JCTC. 2019 15 (12), 6668-6677}. These neural network surfaces, however, have only been validated using a small sampling of the available configurations of the system. DMC, by design, samples the entire region of the PES that is sampled by the vibrational ground state wave function. \n\nDespite the speed of the neural network potential relative to a calculation at a high level of theory such as CCSD(T), the necessity of the evaluation of the Hartree-Fock energy for every one of the thousands of DMC configurations over tens of thousands of time-steps makes this computationally challenging. To this end, we have developed a DMC algorithm to take advantage of massively parallel high performance computing environments. In addition, to further alleviate the computational cost, we have incorporated importance sampling into the algorithm to reduce the overall number of configurations required to adequately sample the PES. Combined, these two optimizations push the calculation back into the realm of computational feasibility. We have benchmarked the calculation on the water monomer and the \\chem{CH_5^+} molecular ion. We plan to extend this work to larger systems, such as large water clusters or a simple Criegee intermediate, where running the DMC with traditional electronic structure would be intractable. Monte Carlo method ; Computer science ; Diffusion ; Statistical physics ; Energy (signal processing) ; Artificial intelligence ; Physics ; Thermodynamics ; Mathematics ; Statistics Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: James L. McDonagh | William C. Swope | R. L. Anderson | Michael Johnston | David J. Bray
Source: ChemRxiv (preprint, 2020-02-03)
Field: Materials Science — Materials Chemistry

What Can Digitization Do For Formulated Product Innovation and Development Digitization oﬀers signiﬁcant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the beneﬁts could be signiﬁcant. Recent developments of base level technologies such as artiﬁcial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could oﬀer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators. Digitization ; Transformative learning ; Exploit ; Computer science ; New product development ; Creativity ; Product (mathematics) ; Quality (philosophy) ; Emerging technologies ; Data science ; Artificial intelligence ; Knowledge management ; Business ; Telecommunications ; Marketing Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation