jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Benjamin Peyton | Connor Briggs | Ruhee D’Cunha | Johannes T. Margraf | Thomas O. Crawford
Source: ChemRxiv (preprint, 2020-04-02)
Field: Materials Science — Materials Chemistry

Machine-Learning Coupled Cluster Properties through a Density Tensor Representation &lt;div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;The introduction of machine-learning (ML) algorithms to quantum mechanics enables rapid evaluation of otherwise intractable expressions at the cost of prior training on appropriate benchmarks. Many computational bottlenecks in the evaluation of accurate electronic structure theory could potentially benefit from the application of such models, from reducing the complexity of the underlying wave function parameter space to circumventing the complications of solving the electronic Schrödinger equation entirely. Applications of ML to electronic structure have thus far been focused on learning molecular properties (mainly the energy) from geometric representations. While this line of study has been quite successful, highly accurate models typically require a “big data” approach with thousands of train- ing data points. Herein, we propose a general, systematically improvable scheme for wave function-based ML of arbitrary molecular properties, inspired by the underlying equations that govern the canonical approach to computing the properties. To this end, we combine the established ML machinery of the t-amplitude tensor representation with a new reduced density matrix representation. The resulting model provides quantitative accuracy in both the electronic energy and dipoles of small molecules using only a few dozen training points per system. &lt;/p&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt; Representation (politics) ; Wave function ; Tensor (intrinsic definition) ; Computer science ; Coupled cluster ; Function (biology) ; Artificial neural network ; Space (punctuation) ; Function representation ; Artificial intelligence ; Theoretical computer science ; Physics ; Mathematics ; Quantum mechanics ; Pure mathematics ; Object (grammar) Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Advanced Chemical Physics Studies
Authors: Anh Tran | Timothy Wildey
Source: arXiv (Cornell University) (preprint, 2020-10-07)
Field: Materials Science — Materials Chemistry

Solving stochastic inverse problems for property-structure linkages\n using data-consistent inversion and machine learning Determining process-structure-property linkages is one of the key objectives\nin material science, and uncertainty quantification plays a critical role in\nunderstanding both process-structure and structure-property linkages. In this\nwork, we seek to learn a distribution of microstructure parameters that are\nconsistent in the sense that the forward propagation of this distribution\nthrough a crystal plasticity finite element model (CPFEM) matches a target\ndistribution on materials properties. This stochastic inversion formulation\ninfers a distribution of acceptable/consistent microstructures, as opposed to a\ndeterministic solution, which expands the range of feasible designs in a\nprobabilistic manner. To solve this stochastic inverse problem, we employ a\nrecently developed uncertainty quantification (UQ) framework based on\npush-forward probability measures, which combines techniques from measure\ntheory and Bayes rule to define a unique and numerically stable solution. This\napproach requires making an initial prediction using an initial guess for the\ndistribution on model inputs and solving a stochastic forward problem. To\nreduce the computational burden in solving both stochastic forward and\nstochastic inverse problems, we combine this approach with a machine learning\n(ML) Bayesian regression model based on Gaussian processes and demonstrate the\nproposed methodology on two representative case studies in structure-property\nlinkages.\n Computer science ; Probabilistic logic ; Inverse problem ; Mathematical optimization ; Uncertainty quantification ; Property (philosophy) ; Bayes' theorem ; Bayesian probability ; Stochastic process ; Range (aeronautics) ; Probability distribution ; Inversion (geology) ; Bayesian inference ; Gaussian process ; Inverse ; Machine learning ; Gaussian ; Artificial intelligence ; Mathematics ; Engineering ; Statistics Machine Learning in Materials Science ; Probabilistic and Robust Engineering Design ; Computational Drug Discovery Methods
Authors: Jianhang Xu
Source: TUScholarShare (Temple University) (article, 2020-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Modeling of liquid water and ionic solutions by first-principles simulations Water is one of the most important materials and has enormous impacts on life. Due to its delicate Hydrogen bond (H-bond) network, water shows various anomalous properties which has not been fully illuminated. Advanced experimental methods, such as scattering experiments and various spectroscopy techniques, have been developed and applied to study the nature of H-bond in liquid water. On the other hand, ab initio molecular dynamics (AIMD) have been widely adopted as an important theoretical tool to provide microscopic information of water on a sub-picosecond timescale. Recent AIMD studies based on the strongly constrained and appropriately normed (SCAN) exchange correlation functional yield an excellent description of the structural, electronic, and dynamic properties of liquid water. In this dissertation, we will focus on studying the structural, electronic and dynamic properties of liquid water as well as the modeling of the hydration structures of ions in aqueous solutions, using AIMD with potential energy surface provided by the novel SCAN functional. In the first work we represent an accurately predicted infrared spectrum of liquid water and show how the improvements are connected to the description of the underlying H-bond network. The second work mainly focusing on modeling the nuclear quantum effects (NQEs) and isotope effect of liquid water with a force field model based on artificial neural network, where qualitative agreements with experimental observations are achieved. In the third work, we study the isotope effect on the x-ray absorption spectra of liquid and attribute observed differences to the structural distinctions between light and heavy water as mentioned in the previous work. And in the last two projects, we systematically show the necessity of including NQEs of the hydrogen atom when modeling chloride ionic solution. Prominent changes in the hydration structure as well as electronic structure can be identified when NQEs are taken into consideration. Environmental science ; Computer science ; Thermodynamics ; Process engineering ; Chemistry ; Physics ; Engineering Electrostatics and Colloid Interactions
Authors: Tim Albrecht
Source: ePrints Soton (University of Southampton) (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

AI3SD Video: When charge transport data are a worm â a transfer learning approach for unsupervised data classification Advanced data analysis methodologies, and in particular dimensionality reduction techniques, are now used more and more widely in the single-molecule charge transport community. They allow for comprehensive exploration of large datasets, where data display significant variance and sometimes contain (unknown) sub-populations. To this end, unsupervised approaches, which do not rely on class labels or pre-defined expectations can be advantageous. Multi-Parameter Vector Classification (MPVC) is one example and PCA-based methods have also been employed in this context [1,2,3]. We have recently shown how Transfer Learning may be employed to identify and quantify hidden features in single-molecule charge transport data [3]. Using open-access neural networks such as AlexNet, trained on millions of seemingly unrelated image data, feature recognition then does not require network training with application-specific data. Instead, the network recognises features in the input that it had learned in other contexts and, for example, identifies different shapes in conductance-distance traces as images of different worm species. Thus, our results show how Deep Learning methodologies can readily be employed for unsupervised data classification, even if the amount of problem-specific, âownâ data is limited.<br><br>[1] M Lemmer, MS Inkpen, K Kornysheva, NJ Long, T Albrecht, âUnsupervised vector-based classification of single-molecule charge transport dataâ, Nat. Comm. 2016, 7, 12922.<br>[2] T Albrecht, G Slabaugh, E Alonso, SMMR Al-Arif, âDeep learning for single-molecule scienceâ, Nanotechnology 2017, 28 (42), 423001.<br>[3] A Vladyka, T Albrecht, âUnsupervised classification of single-molecule data with autoencoders and transfer learningâ, Mach. Learn.: Sci. Technol. 2020, 1, 035013. Unsupervised learning ; Transfer of learning ; Computer science ; Artificial intelligence ; Context (archaeology) ; Pattern recognition (psychology) ; Dimensionality reduction ; Curse of dimensionality ; Machine learning ; Artificial neural network ; Deep learning Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electrochemical Analysis and Applications
Authors: James A Crowe | Edik U. Rafailov
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2020-04-12)
Field: Materials Science — Materials Chemistry

Development of two-photon polymerised scaffolds for optical interrogation and neurite guidance of human iPSC-derived cortical neuronal networks Recent progress in the field of human induced pluripotent stem cells (iPSCs) has led to the efficient production of human neuronal cell models for in vitro study. This has the potential to enable the understanding of live human cellular and network function which is otherwise not possible. However, a major challenge is the generation of reproducible neural networks together with the ability to interrogate and record at the single cell level. A promising aid is the use of biomaterial scaffolds that would enable the development and guidance of neuronal networks in physiologically relevant architectures and dimensionality. The optimal scaffold material would need to be precisely fabricated with submicron resolution, be optically transparent, and biocompatible. Two-photon polymerisation (2PP) enables precise microfabrication of three-dimensional structures. In this study, we report the identification of two biomaterials that support the growth and differentiation of human iPSC-derived neural progenitors into functional neuronal networks. Furthermore, these materials can be patterned to induce alignment of neuronal processes and enable the optical interrogation of individual cells. 2PP scaffolds with tailored topographies therefore provide an effective method of producing defined in vitro human neural networks for application in influencing neurite guidance and complex network activity Interrogation ; Neurite ; Two-photon excitation microscopy ; Cortical neurons ; Neuroscience ; Chemistry ; Physics ; Biology ; Optics ; Geography Photochromic and Fluorescence Chemistry ; Neuroscience and Neural Engineering ; Optical Imaging and Spectroscopy Techniques
Authors: Yunan Li
Source: SPE Annual Technical Conference and Exhibition (article, 2020-10-14)
Field: Chemistry — Analytical Chemistry

An Evaluation of Ignition Criteria for Crude Oil Combustion in Porous Media Abstract The objective of this study is to evaluate and compare the performance of different ignition criteria. This is critical to the ignition envelope prediction under various operational conditions. There are four ignition criteria proposed, and the uncertainty quantification is conducted to discuss their performance. A base case simulation model is constructed to represent the kinetic cell in lab. The chemical reaction model is calibrated based on the Ramped Temperature Oxidation (RTO) measurements with the Xinjiang oil sample. The next step is to do ignition tests under various operational conditions with this calibrated simulation model. It is necessary and important to define ignition with appropriate criteria. Method 1 takes advantage of a pre-defined temperature threshold value as a criterion. Method 2 and Method 3 make improvements to define the threshold value based on distribution of the temperature and reaction energy, respectively. Ideally, the histogram will show a bi-modal distribution of the selected indicators, separating ignition and extinction into different groups. The threshold values based on histograms enhance the accuracy of ignition potential characterization. An unsupervised machine learning algorithm is applied to further improve ignition criteria without defining a threshold value. K-Means algorithm can learn the patterns and features of the simulation results and identify ignition or extinction status. The results show that the predicted ignition envelopes from all criteria follow a similar trend. The air flow rate window to ignite the oil shrinks with increasing heat loss rate. Although Method 1 provides a straightforward criterion, it over estimates the ignition capability of the oil. This means Method 1 can incorrectly recognize the extinction cases to be ignition. Method 2 and Method 3 show comparable performance on ignition envelope prediction. However, Method 2 can slightly over estimate the ignition potential when the air flow rate is low. The uncertainty regions of ignition envelopes based on Method 2 and Method 3 are reduced compared to Method 1. The ignition envelope from Method 4 is very close to Method 2 and Method 3. It indicates Method 4 can successfully separate the ignition and no ignition cases using this machine learning algorithm. Method 4 further improves the ignition criteria because it is not necessary to determine a particular threshold value. The novelty of this work is the application of the machine learning algorithm (K-Means) to identify ignition status. The performance of new ignition criteria is introduced, discussed, and evaluated. Ignition system ; Histogram ; Computer science ; Combustion ; Simulation ; Artificial intelligence ; Engineering Petroleum Processing and Analysis ; Atmospheric and Environmental Gas Dynamics ; Combustion and flame dynamics
Authors: Onur Çaylak | O. Anatole von Lilienfeld | Björn Baumeier
Source: Machine Learning Science and Technology (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Wasserstein metric for improved QML with adjacency matrix representations We study the Wasserstein metric to measure distances between molecules represented by the atom index dependent adjacency 'Coulomb' matrix, used in kernel ridge regression based supervised learning. Resulting machine learning models of quantum properties, a.k.a. quantum machine learning models exhibit improved training efficiency and result in smoother predictions of energies related to molecular distortions. We first illustrate smoothness for the continuous extraction of an atom from some organic molecule. Learning curves, quantifying the decay of the atomization energy's prediction error as a function of training set size, have been obtained for tens of thousands of organic molecules drawn from the QM9 data set. In comparison to conventionally used metrics (L1 and L2 norm), our numerical results indicate systematic improvement in terms of learning curve off-set for random as well as sorted (by norms of row) atom indexing in Coulomb matrices. Our findings suggest that this metric corresponds to a favorable similarity measure which introduces index-invariance in any kernel based model relying on adjacency matrix representations. Adjacency matrix ; Measure (data warehouse) ; Metric (unit) ; Matrix norm ; Mathematics ; Distance matrix ; Matrix (chemical analysis) ; Algorithm ; Artificial intelligence ; Applied mathematics ; Computer science ; Discrete mathematics ; Eigenvalues and eigenvectors ; Quantum mechanics ; Graph ; Physics ; Data mining ; Chemistry Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Rose K. Cersonsky | Benjamin A. Helfrecht | Edgar A. Engel | Michele Ceriotti
Source: arXiv (Cornell University) (preprint, 2020-12-22)
Field: Materials Science — Materials Chemistry

Improving Sample and Feature Selection with Principal Covariates\n Regression Selecting the most relevant features and samples out of a large set of\ncandidates is a task that occurs very often in the context of automated data\nanalysis, where it can be used to improve the computational performance, and\nalso often the transferability, of a model. Here we focus on two popular\nsub-selection schemes which have been applied to this end: CUR decomposition,\nthat is based on a low-rank approximation of the feature matrix and Farthest\nPoint Sampling, that relies on the iterative identification of the most diverse\nsamples and discriminating features. We modify these unsupervised approaches,\nincorporating a supervised component following the same spirit as the Principal\nCovariates Regression (PCovR) method. We show that incorporating target\ninformation provides selections that perform better in supervised tasks, which\nwe demonstrate with ridge regression, kernel ridge regression, and sparse\nkernel regression. We also show that incorporating aspects of simple supervised\nlearning models can improve the accuracy of more complex models, such as\nfeed-forward neural networks. We present adjustments to minimize the impact\nthat any subselection may incur when performing unsupervised tasks. We\ndemonstrate the significant improvements associated with the use of PCov-CUR\nand PCov-FPS selections for applications to chemistry and materials science,\ntypically reducing by a factor of two the number of features and samples which\nare required to achieve a given level of regression accuracy.\n Computer science ; Artificial intelligence ; Principal component regression ; Feature selection ; Regression ; Machine learning ; Covariate ; Kernel (algebra) ; Context (archaeology) ; Elastic net regularization ; Kernel regression ; Feature (linguistics) ; Supervised learning ; Pattern recognition (psychology) ; Principal component analysis ; Artificial neural network ; Statistics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Chemometric Analyses
Authors: 康伸 安藤 | Yasunobu Ando
Source: Institutional Repositories DataBase (IRDB) (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

Ensemble Learning Approach with LASSO for Predicting Catalytic Reaction Rates The prediction of the initial reaction rate in the tungsten-catalyzed epoxidation of alkenes is demonstrated using a machine learning approach. The ensemble learning framework used in this study consists of random sampling with replacement from the training dataset, the construction of several predictive models (weak learners), and the combination of their outputs. This approach enables us to obtain a reasonable prediction model that avoids the problem of overfitting, even when analyzing a small dataset. Ensemble learning ; Training set ; Lasso (programming language) ; Sampling (signal processing) ; Ensemble forecasting ; Statistical learning Machine Learning in Materials Science ; Polyoxometalates: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Ruifeng Yao | Jinzhui Gao | Wenbo Yan | Zhixin He | Yan Wang | Jingzhe Xu | Ming Wu | Lisheng Zhong
Source:  (article, 2020-10-18)
Field: Materials Science — Materials Chemistry

The Characterization of Complex Polarization State in Ferroelectric Materials using Scanning Convergent Beam Electron Diffraction Ferroelectric materials in various applications can be ascribed to its functional properties, which originates from the spontaneous polarization forming the ferroelectric domain configuration. Hence, it is of great importance to characterize the complex polarization state to interpret the associated phenomena in ferroelectric materials. In particular, recent progress on highperformance materials with exotic nanoscale polarization heterogeneity requires advanced technology on probing local structure for ferroelectrics. In this work, we employed a high-throughput scanning convergent beam electron diffraction (SCBED) method to investigate the local structure of (Ba <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.78</sub> Ca <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.22</sub> )(Ti <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.88</sub> Sn <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.12</sub> )O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> ceramics by using a transmission electron microscope. The obtained patterns were further analyzed by the Principal Components Analysis (PCA) and Bayesian machine-learning algorithm to recognize polar axes and their spatial distribution. The results show the coexistence of cubic, tetragonal, orthorhombic and rhombohedral crystal symmetries among nanodomains, which suggest a reduced polarization anisotropy. Our work may provide an effective approach to characterize the complex polarization state in ferroelectric materials, which enables the establishment of structure-property relationship for high-property functional dielectric materials. Ferroelectricity ; Polarization (electrochemistry) ; Materials science ; Tetragonal crystal system ; Crystallography ; Crystal structure ; Physics ; Dielectric ; Optoelectronics ; Chemistry ; Physical chemistry Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies