41 research outputs found
Iterative X-ray Spectroscopic Ptychography
Spectroscopic ptychography is a powerful technique to determine the chemical
composition of a sample with high spatial resolution. In spectro-ptychography,
a sample is rastered through a focused x-ray beam with varying photon energy so
that a series of phaseless diffraction data are recorded. Each chemical
component in the material under investigation has a characteristic absorption
and phase contrast as a function of photon energy. Using a dictionary formed by
the set of contrast functions of each energy for each chemical component, it is
possible to obtain the chemical composition of the material from high
resolution multi-spectral images. This paper presents SPA (Spectroscopic
Ptychography with ADMM), a novel algorithm to iteratively solve the
spectroscopic blind ptychography problem. We design first a nonlinear
spectro-ptychography model based on Poisson maximum likelihood, and construct
then the proposed method based on fast iterative splitting operators. SPA can
be used to retrieve spectral contrast when considering both a known or an
incomplete (partially known) dictionary of reference spectra. By coupling the
redundancy across different spectral measurements, the proposed algorithm can
achieve higher reconstruction quality when compared to standard
state-of-the-art two-step methods. We demonstrate how SPA can recover accurate
chemical maps from Poisson-noised measurements, and also show its enhanced
robustness when reconstructing reduced redundancy ptychography data using large
scanning stepsizes
Understanding the Initial Stages of Reversible Mg Deposition and Stripping in Inorganic Non-Aqueous Electrolytes
Multi-valent (MV) battery architectures based on pairing a Mg metal anode
with a high-voltage ( 3 V) intercalation cathode offer a realistic design
pathway toward significantly surpassing the energy storage performance of
traditional Li-ion based batteries, but there are currently only few
electrolyte systems that support reversible Mg deposition. Using both static
first-principles calculations and molecular dynamics, we perform
a comprehensive adsorption study of several salt and solvent species at the
interface of Mg metal with an electrolyte of Mg and Cl dissolved in
liquid tetrahydrofuran (THF). Our findings not only provide a picture of the
stable species at the interface, but also explain how this system can support
reversible Mg deposition and as such we provide insights in how to design other
electrolytes for Mg plating and stripping. The active depositing species are
identified to be (MgCl) monomers coordinated by THF, which exhibit
preferential adsorption on Mg compared to possible passivating species (such as
THF solvent or neutral MgCl complexes). Upon deposition, the energy to
desolvate these adsorbed complexes and facilitate charge-transfer is shown to
be small ( 61 46.2 kJ mol to remove 3 THF from the strongest
adsorbing complex), and the stable orientations of the adsorbed but desolvated
(MgCl) complexes appear favorable for charge-transfer. Finally,
observations of Mg-Cl dissociation at the Mg surface at very low THF
coordinations (0 and 1) suggest that deleterious Cl incorporation in the anode
may occur upon plating. In the stripping process, this is beneficial by further
facilitating the Mg removal reaction
Unsupervised word embeddings capture latent knowledge from materials science literature.
The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases1,2, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing3-10, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings11-13 (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature
Constructing and proving the ground state of a generalized Ising model by the cluster tree optimization algorithm
Generalized Ising models, also known as cluster expansions, are an important
tool in many areas of condensed-matter physics and materials science, as they
are often used in the study of lattice thermodynamics, solid-solid phase
transitions, magnetic and thermal properties of solids, and fluid mechanics.
However, the problem of finding the global ground state of generalized Ising
model has remained unresolved, with only a limited number of results for simple
systems known. We propose a method to efficiently find the periodic ground
state of a generalized Ising model of arbitrary complexity by a new algorithm
which we term cluster tree optimization. Importantly, we are able to show that
even in the case of an aperiodic ground state, our algorithm produces a
sequence of states with energy converging to the true ground state energy, with
a provable bound on error. Compared to the current state-of-the-art polytope
method, this algorithm eliminates the necessity of introducing an exponential
number of variables to counter frustration, and thus significantly improves
tractability. We believe that the cluster tree algorithm offers an intuitive
and efficient approach to finding and proving ground states of generalized
Ising Hamiltonians of arbitrary complexity, which will help validate
assumptions regarding local vs. global optimality in lattice models, as well as
offer insights into the low-energy behavior of highly frustrated systems
Construction of ground-state preserving sparse lattice models for predictive materials simulations
This work was supported primarily by the US Department of Energy (DOE) under Contract No. DE-FG02-96ER45571.First-principles based cluster expansion models are the dominant approach in ab initio thermodynamics of crystalline mixtures enabling the prediction of phase diagrams and novel ground states. However, despite recent advances, the construction of accurate models still requires a careful and time-consuming manual parameter tuning process for ground-state preservation, since this property is not guaranteed by default. In this paper, we present a systematic and mathematically sound method to obtain cluster expansion models that are guaranteed to preserve the ground states of their reference data. The method builds on the recently introduced compressive sensing paradigm for cluster expansion and employs quadratic programming to impose constraints on the model parameters. The robustness of our methodology is illustrated for two lithium transition metal oxides with relevance for Li-ion battery cathodes, i.e., Li2x Fe2(1-x)O2 and Li2x Ti2(1-x)O2, for which the construction of cluster expansion models with compressive sensing alone has proven to be challenging. We demonstrate that our method not only guarantees ground-state preservation on the set of reference structures used for the model construction, but also show that out-of-sample ground-state preservation up to relatively large supercell size is achievable through a rapidly converging iterative refinement. This method provides a general tool for building robust, compressed and constrained physical models with predictive power.Publisher PDFPeer reviewe
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Author Correction: Text-mined dataset of inorganic materials synthesis recipes.
An amendment to this paper has been published and can be accessed via a link at the top of the paper
An LL-norm compressive sensing paradigm for the construction of sparse predictive lattice models using mixed integer quadratic programming
First-principles based lattice models allow the modeling of ab initio
thermodynamics of crystalline mixtures for applications such as the
construction of phase diagrams and the identification of ground state atomic
orderings. The recent development of compressive sensing approaches for the
construction of lattice models has further enabled the systematic construction
of sparse physical models without the need for human intuition other than
requiring the compactness of effective cluster interactions. However,
conventional compressive sensing based on L1-norm regularization is strictly
only applicable to certain classes of optimization problems and is otherwise
not guaranteed to generate optimally sparse and transferable results, so that
the method can only be applied to some materials science applications. In this
paper, we illustrate a more robust L0L1-norm compressive-sensing method that
removes the limitations of conventional compressive sensing and generally
results in sparser lattice models that are at least as predictive as those
obtained from L1-norm compressive sensing. Apart from the theory, a practical
implementation based on state-of-the-art mixed-integer quadratic programming
(MIQP) is proposed. The robustness of our methodology is illustrated for four
different transition-metal oxides with relevance as battery cathode materials:
Li2xTi2(1-x)O2, Li2xNi2yO2, MgxCr2O4, and NaxCrO2. This method provides a
practical and robust approach for the construction of sparser and more
predictive lattice models, improving on the compressive sensing paradigm and
making it applicable to a much broader range of applications.Comment: 25 pages, 3 figure
Finding and proving the exact ground state of a generalized Ising model by convex optimization and MAX-SAT
This paper was supported primarily by the US Department of Energy (DOE) under Contract No. DE-FG02-96ER45571. In addition, some of the test cases for ground states were supported by the Office of Naval Research under contract N00014-14-1-0444.Lattice models, also known as generalized Ising models or cluster expansions, are widely used in many areas of science and are routinely applied to the study of alloy thermodynamics, solid-solid phase transitions, magnetic and thermal properties of solids, fluid mechanics, and others. However, the problem of finding and proving the global ground state of a lattice model, which is essential for all of the aforementioned applications, has remained unresolved for relatively complex practical systems, with only a limited number of results for highly simplified systems known. In this paper, we present a practical and general algorithm that provides a provable periodically constrained ground state of a complex lattice model up to a given unit cell size and in many cases is able to prove global optimality over all other choices of unit cell. We transform the infinite-discrete-optimization problem into a pair of combinatorial optimization (MAX-SAT) and nonsmooth convex optimization (MAX-MIN) problems, which provide upper and lower bounds on the ground state energy, respectively. By systematically converging these bounds to each other, we may find and prove the exact ground state of realistic Hamiltonians whose exact solutions are difficult, if not impossible, to obtain via traditional methods. Considering that currently such practical Hamiltonians are solved using simulated annealing and genetic algorithms that are often unable to find the true global energy minimum and inherently cannot prove the optimality of their result, our paper opens the door to resolving longstanding uncertainties in lattice models of physical phenomena. An implementation of the algorithm is available at https://github.com/dkitch/maxsat-isingPublisher PDFPeer reviewe