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Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery [article]

Yexiang Xue, Junwen Bai, Ronan Le Bras, Brendan Rappazzo, Richard Bernstein, Johan Bjorck, Liane Longpre, Santosh K. Suram, Robert B. van Dover, John Gregoire, Carla P. Gomes
2016 arXiv   pre-print
A key problem in materials discovery, the phase map identification problem, involves the determination of the crystal phase diagram from the materials' composition and structural characterization data.  ...  Phase-Mapper affords incorporation of any spectral demixing algorithm, including our novel solver, AgileFD, which is based on a convolutive non-negative matrix factorization algorithm.  ...  In this paper we address the phase mapping problem, a central issue in high-throughput materials discovery, and provide an efficient method of solving it which is currently a critically missing component  ... 
arXiv:1610.00689v2 fatcat:mpcetwwddvgddn3kree3m44pfa

Phase Mapper: Accelerating Materials Discovery with AI

Junwen Bai, Yexiang Xue, Johan Bjorck, Ronan Le Bras, Brendan Rappazzo, Richard Bernstein, Santosh K. Suram, Robert Bruce Van Dover, John M. Gregoire, Carla P. Gomes
2018 The AI Magazine  
Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization.  ...  A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization  ...  In this article, we address the phase-mapping problem, a central problem in high-throughput materials discovery, which has critically lacked an efficient solution method.  ... 
doi:10.1609/aimag.v39i1.2785 fatcat:kvkx3bi4ffhjdpldtemh7hjn7y

An accurate and efficient scheme for wave propagation in linear viscoelastic media

H. Tal‐Ezer, J. M. Carcione, D. Kosloff
1990 Geophysics  
The algorithm is tested for the problem of wave propagation in a homogeneous medium and compared with second-order temporal differencing and the spectral Chebychev method.  ...  We propose an optimal polynomial approximation of e M' based on the powerful method of interpolation in the complex plane, in a domain which includes the eigenvalues of the matrix M.  ...  Behle for useful comments on the original manuscript, and Dr. B. Kummer for helpful discussions on the anticlinal trap model.  ... 
doi:10.1190/1.1442784 fatcat:ram2bksqxjgjvauznvzrl2xj7i

Information Assisted Dictionary Learning for fMRI data analysis

Manuel Morante, Yannis Kopsinis, Sergios Theodoridis, Athanassios Protopapas
2020 IEEE Access  
In this paper, the task-related fMRI problem is treated in its matrix factorization form, focusing on the Dictionary Learning (DL) approach.  ...  Moreover, it can cope efficiently with uncertainties in the modeling of the hemodynamic response function.  ...  ACKNOWLEDGMENT The authors would like to thank Prof. E. Kofidis, Dept. of Statistics and Insurance Science, University of Piraeus (Greece), and C.  ... 
doi:10.1109/access.2020.2994276 fatcat:pkaqo2xixngmpec6ydyzam5kse

Epicycle method for elasticity limit calculations

Axel van de Walle, Sara Kadkhodaei, Ruoshi Sun, Qi-Jun Hong
2017 Physical review B  
The task of finding the smallest energy needed to bring a solid to its onset of mechanical instability arises in many problems in materials science, from the determination of the elasticity limit to the  ...  We propose a method that is inspired by the well-known dimer method for saddle point searches but that adds the necessary ingredients to solve for the lowest onset of mechanical instability.  ...  Acknowledgments This work is supported by the US Office of Naval Research via grant N00014-14-1-0055 and by Brown University through the use of the facilities of its Center for Computation and Visualization  ... 
doi:10.1103/physrevb.95.144113 fatcat:jkndg55wgna3xk43kjb25e63yu

Tomographic image reconstruction using training images

Sara Soltani, Martin S. Andersen, Per Christian Hansen
2017 Journal of Computational and Applied Mathematics  
The dictionary learning problem is formulated as a regularized non-negative matrix factorization in order to compute a nonnegative dictionary.  ...  More studies are however needed for implementing the proposed algorithm in a routine use for clinical applications and materials testing.  ...  Numerous methods are proposed in literature for solving the non-negative matrix factorization problem.  ... 
doi:10.1016/j.cam.2016.09.019 fatcat:o5b6je3strcybirrgxkifdk3w4

Comparison of the deflated preconditioned conjugate gradient method and algebraic multigrid for composite materials

T. B. Jönsthövel, M. B. van Gijzen, S. MacLachlan, C. Vuik, A. Scarpas
2011 Computational Mechanics  
Our test problems are derived from real asphalt core samples, obtained using CT scans. We show that the DPCG method is an efficient and robust technique for solving these challenging linear systems.  ...  Many applications in computational science and engineering concern composite materials, which are characterized by large discontinuities in the material properties.  ...  In the relaxation phase, a simple stationary iteration, such as the Jacobi or Gauss-Seidel iterations, is used to efficiently damp largeenergy errors.  ... 
doi:10.1007/s00466-011-0661-y fatcat:tnqqwaxypnch3fzykxq23gaqpe

A review of recent advances in global optimization

C. A. Floudas, C. E. Gounaris
2008 Journal of Global Optimization  
This paper presents an overview of the research progress in deterministic global optimization during the last decade (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006) (2007) (2008).  ...  It covers the areas of twice continuously differentiable nonlinear optimization, mixed-integer nonlinear optimization, optimization with differential-algebraic models, semi-infinite programming, optimization  ...  Floudas gratefully acknowledges support from the National Science Foundation, the National Institutes of Health (R01 GM52032 and R24 GM069736), the Environmental Protection Agency (GAD R 832721-010), AspenTech  ... 
doi:10.1007/s10898-008-9332-8 fatcat:72fpfq72hrdzhf6mxqyc6ssezm

Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing

Ryo Yoshida, Mike West
2010 Journal of machine learning research  
A mean-field variational technique coupled with annealing is developed to successively generate "artificial" posterior distributions that, at the limiting temperature in the annealing schedule, define  ...  Linear, Gaussian GFMs have sparse, orthogonal factor loadings matrices, that, in addition to sparsity of the implied covariance matrices, also induce conditional independence structures via zeros in the  ...  Acknowledgments The authors are grateful to the Action Editor and three anonymous referees for their detailed and most constructive comments on the original version of this paper.  ... 
pmid:20890391 pmcid:PMC2947451 fatcat:iocvr7d6irhlpnycqtce4ztzoq

Controlling singular values with semidefinite programming

Shahar Z. Kovalsky, Noam Aigerman, Ronen Basri, Yaron Lipman
2014 ACM Transactions on Graphics  
We experiment with this new framework in several applications: volumetric mesh deformations, extremal quasi-conformal mappings in three dimensions, non-rigid shape registration and averaging of rotations  ...  The algorithm is shown to be monotonically decreasing and optimal in the sense that each iteration considers the "largest" convex sub-problem of the non-convex meta-problem.  ...  More generally, one could attempt to solve constrained minimization problems using gradient projection methods, which alternate between a gradient descent step that reduces the functional, and a projection  ... 
doi:10.1145/2601097.2601142 fatcat:gkh47sbbkbhbtblrh7oofbjise

Improved Estimation of High-dimensional Ising Models [article]

M. Kolar, E. P. Xing
2008 arXiv   pre-print
We analyze the method in the high-dimensional setting, where the number of dimensions p is allowed to grow with the number of observations n.  ...  We consider the problem of jointly estimating the parameters as well as the structure of binary valued Markov Random Fields, in contrast to earlier work that focus on one of the two problems.  ...  Acknowledgements This material is based upon work supported by an NSF CAREER Award to EPX under grant No. DBI-0546594, and NSF grant IIS-0713379. EPX is also supported by an Alfred P.  ... 
arXiv:0811.1239v1 fatcat:jjmxapv77zcw7dsxhzl6pzmo2i

Application of fermionic marginal constraints to hybrid quantum algorithms [article]

Nicholas C. Rubin, Ryan Babbush, Jarrod McClean
2018 arXiv   pre-print
In this work, we introduce these conditions in the language of quantum computation, and utilize them to develop several techniques to accelerate and improve practical applications for quantum chemistry  ...  We also demonstrate an efficient restoration of the physicality of energy curves for the dilation of a four qubit diatomic hydrogen system in the presence of three distinct one qubit error channels, providing  ...  measurement variance, and Sergio Boixo for helpful conversations about concentration of measure in random states.  ... 
arXiv:1801.03524v1 fatcat:zzm4tm2knjfkddntjvvhajovyq

Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics

Rama K. Vasudevan, Kamal Choudhary, Apurva Mehta, Ryan Smith, Gilad Kusne, Francesca Tavazza, Lukas Vlcek, Maxim Ziatdinov, Sergei V. Kalinin, Jason Hattrick-Simpers
2019 MRS Communications  
The use of statistical/machine learning (ML) approaches to materials science is experiencing explosive growth.  ...  These developments point toward a data-driven future wherein knowledge can be aggregated and synthesized, accelerating the advancement of materials science.  ...  [125] or by attempting to deconvolve peak shift through the application of convolution non-negative matrix factorization  ... 
doi:10.1557/mrc.2019.95 pmid:32166045 pmcid:PMC7067066 fatcat:3vct5bubtnclvdjenus6pcysni

Pulse Sequences for Interventional MRI [chapter]

Walter F. Block, Benjamin P. Grabow
2012 Medical Radiology  
These systems also provide standard methods for periodically computing B 0 maps that are necessary for robust imaging with many non-Cartesian methods.  ...  The last example is the XTC datadumper, written in C#, which can be used as an example project for other standalone prototype applications.  ... 
doi:10.1007/174_2012_586 fatcat:e5wjuz7g5zh6zphato77k4eu64

A Novel Constrained Non-negative Matrix Factorization Method for Group Functional Magnetic Resonance Imaging Data Analysis of Adult Attention-Deficit/Hyperactivity Disorder

Ying Li, Weiming Zeng, Yuhu Shi, Jin Deng, Weifang Nie, Sizhe Luo, Jiajun Yang
2022 Frontiers in Neuroscience  
Hence, this study proposed a spatial constrained non-negative matrix factorization (SCNMF) method based on the fMRI real reference signal.  ...  First, non-negative matrix factorization analysis was carried out on each subject to select the brain network components of interest.  ...  Support Projects of the Shanghai Science and Technology Committee (Grant No. 19411971400).  ... 
doi:10.3389/fnins.2022.756938 pmid:35250441 pmcid:PMC8891574 fatcat:f6fn7ndve5a53kqh4lyg5tchnq
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