arXiv subjects

Gerbrand Ceder

Publications and source records attributed to Gerbrand Ceder.

At least 73 records · Page 4Linked to original sources

arXiv2021

Sparse expansions of multicomponent oxide configuration energy using coherency & redundancy

Compressed sensing has become a widely accepted paradigm to construct high dimensional cluster expansion models used for statistical mechanical studies of atomic configuration in complex multicomponent crystalline materials. However, strict sampling requirements necessary to obtain minimal coherence measurements for compressed sensing to guarantee accurate estimation of model parameters are difficult and in some cases impossible to satisfy due to the inability of physical systems to access certain configurations. Nevertheless, the dependence of energy on atomic configuration can still be adequately learned without these strict requirements by using compressed sensing by way of coherent measurements using redundant function sets known as frames. We develop a particular frame constructed from the union of all occupancy-based cluster expansion basis sets. We illustrate how using this highly redundant frame yields sparse expansions of the configuration energy of complex oxide materials that are competitive and often surpass the prediction accuracy and sparsity of models obtained from standard cluster expansions.

cond-mat.mtrl-sci↗

arXiv2021

Dataset of Solution-based Inorganic Materials Synthesis Recipes Extracted from the Scientific Literature

The development of a materials synthesis route is usually based on heuristics and experience. A possible new approach would be to apply data-driven approaches to learn the patterns of synthesis from past experience and use them to predict the syntheses of novel materials. However, this route is impeded by the lack of a large-scale database of synthesis formulations. In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis "recipes" extracted from the scientific literature. Each recipe contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every recipe is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis recipes.

cond-mat.mtrl-sci↗

arXiv2021

Synthetic accessibility and stability rules of NASICONs

In this paper we develop the stability rules for NASICON structured materials, as an example of compounds with complex bond topology and composition. By applying machine learning to the ab-initio computed phase stability of 3881 potential NASICONs we can extract a simple two-dimensional descriptor that is extremely good at separating stable from unstable NASICONS. This machine-learned "tolerance factor" contains information on the Na content, the radii and electronegativities of the elements, and the Madelung energy. We test the predictive capability of this approach by selecting six predicted NASICON compositions. Five out of the six resulted in a phase pure NASICON while the sixth composition led to a NASICON that coexisted with other phases, validating the efficacy of this approach. This work not only provide tools to understand synthetic accessibility of NASICON-type materials, but also demonstrate an efficient paradigm for discovering new materials with complicate composition and atomic structure.

cond-mat.mtrl-sci↗

arXiv2021

A probabilistic deep learning approach to automate the interpretation of multi-phase diffraction spectra

Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. Larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.

cond-mat.mtrl-sci↗

arXiv2021

Observing and modeling the sequential pairwise reactions that drive solid-state ceramic synthesis

Solid-state synthesis from powder precursors is the primary processing route to advanced multicomponent ceramic materials. Designing ceramic synthesis routes is usually a laborious, trial-and-error process, as heterogeneous mixtures of powder precursors often evolve through a complicated series of reaction intermediates. Here, we show that phase evolution from multiple precursors can be modeled as a sequence of pairwise interfacial reactions, with thermodynamic driving forces that can be efficiently calculated using ab initio methods. Using the synthesis of the classic high-temperature superconductor YBa$_2$Cu$_3$O$_{6+x}$ (YBCO) as a representative system, we rationalize how replacing the common BaCO$_3$ precursor with BaO$_2$ redirects phase evolution through a kinetically-facile pathway. Our model is validated from in situ X-ray diffraction and in situ microscopy observations, which show rapid YBCO formation from BaO$_2$ in only 30 minutes. By combining thermodynamic modeling with in situ characterization, we introduce a new computable framework to interpret and ultimately design synthesis pathways to complex ceramic materials.

cond-mat.mtrl-sci↗

arXiv2020

COVIDScholar: An automated COVID-19 research aggregation and analysis platform

The ongoing COVID-19 pandemic has had far-reaching effects throughout society, and science is no exception. The scale, speed, and breadth of the scientific community's COVID-19 response has lead to the emergence of new research literature on a remarkable scale -- as of October 2020, over 81,000 COVID-19 related scientific papers have been released, at a rate of over 250 per day. This has created a challenge to traditional methods of engagement with the research literature; the volume of new research is far beyond the ability of any human to read, and the urgency of response has lead to an increasingly prominent role for pre-print servers and a diffusion of relevant research across sources. These factors have created a need for new tools to change the way scientific literature is disseminated. COVIDScholar is a knowledge portal designed with the unique needs of the COVID-19 research community in mind, utilizing NLP to aid researchers in synthesizing the information spread across thousands of emergent research articles, patents, and clinical trials into actionable insights and new knowledge. The search interface for this corpus, https://covidscholar.org, now serves over 2000 unique users weekly. We present also an analysis of trends in COVID-19 research over the course of 2020.

cs.DL↗

arXiv2020

Similarity of Precursors in Solid-state Synthesis as Text-Mined from Scientific Literature

Collecting and analyzing the vast amount of information available in the solid-state chemistry literature may accelerate our understanding of materials synthesis. However, one major problem is the difficulty of identifying which materials from a synthesis paragraph are precursors or are target materials. In this study, we developed a two-step Chemical Named Entity Recognition (CNER) model to identify precursors and targets, based on information from the context around material entities. Using the extracted data, we conducted a meta-analysis to study the similarities and differences between precursors in the context of solid-state synthesis. To quantify precursor similarity, we built a substitution model to calculate the viability of substituting one precursor with another while retaining the target. From a hierarchical clustering of the precursors, we demonstrate that "chemical similarity" of precursors can be extracted from text data. Quantifying the similarity of precursors helps provide a foundation for suggesting candidate reactants in a predictive synthesis model.

cond-mat.mtrl-sci↗

arXiv2020

Modeling of Electro-deposition and Mechanical Stability at Li Metal/Solid Electrolyte Interface during Plating in Solid-State Batteries

Interfacial deposition stability between Li metal and a solid electrolyte (SE) is important in preventing interfacial contact loss, mechanical fracture, and dendrite growth in Li-metal solid-state batteries (SSB). In this work, we investigate the deposition and mechanical stability at the Li metal/SE interface and its consequences (such as SE fracture and contact loss). A wide range of contributing factors are investigated, such as charge and mass transfer kinetics, the plasticity of Li metal and fracture of the SE, and the applied stack pressure. We quantify the effect of the ionic conductivity of the SE, the exchange current density of the interfacial charge-transfer reaction and SE surface roughness on the Li deposition stability at the Li metal/SE interface. We also propose a mechanical stability window for the applied stack pressure that can prevent both contact loss and SE fracture, which can be extended to other metal-electrode (such as Sodium) SSB systems.

physics.app-ph↗

arXiv2020

A critical examination of compound stability predictions from machine-learned formation energies

Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The non-incremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.

cond-mat.mtrl-sci↗

arXiv2019

Evaluation of Mg compounds as coating materials in Mg batteries

Mg batteries utilizing a Mg metal anode with a high-voltage intercalation cathode define a potential pathway toward energy storage with high energy density. However, the realization of Mg batteries is plagued by the instability of existing electrolytes against the Mg-metal anode and high-voltage cathode materials. One viable solution to this problem is the identification of protective coating materials that could effectively separate the distinct chemistries of the metal-anode and the cathode materials from the electrolyte. Using first-principles calculations we map the electrochemical stability windows for non-redox-active Mg binary and ternary compounds in order to identify potential coating materials for Mg batteries. Our results identify Mg-halides and Mg(BH$_4$)$_2$ as promising anode coating materials based on their significant reductive stability. On the cathode side, we single out MgF$_2$, Mg(PO$_3$)$_2$ and MgP$_4$O$_11$ as effective passivating agents.

cond-mat.mtrl-sci↗

arXiv2019

Atomic-scale factors that control the rate capability of nanostructured amorphous Si for high-energy-density batteries

Nanostructured Si is the most promising high-capacity anode material to substantially increase the energy density of Li-ion batteries. Among the remaining challenges is its low rate capability as compared to conventional materials. To understand better what controls the diffusion of Li in the amorphous Li-Si alloy, we use a novel machine-learning potential trained on more than 40,000 ab-initio calculations and nanosecond-scale molecular dynamics simulations, to visualize for the first time the delithiation of entire LiSi nanoparticles. Our results show that the Si host is not static but undergoes a dynamic rearrangement from isolated atoms, to chains, and clusters, with the Li diffusion strongly governed by this Si rearrangement. We find that the Li diffusivity is highest when Si segregates into clusters, so that Li diffusion proceeds via hopping between the Si clusters. The average size of Si clusters and the concentration range over which Si clustering occurs can thus function as design criteria for the development of rate-improved anodes based on modified Si.

cond-mat.dis-nn↗

arXiv2018

Ternary Nitride Semiconductors in the Rocksalt Crystal Structure

Inorganic nitrides with wurtzite crystal structures are well-known semiconductors used in optoelectronic devices. In contrast, rocksalt-based nitrides are known for their metallic and refractory properties. Breaking this dichotomy, here we report on ternary nitride semiconductors with rocksalt crystal structures, remarkable optoelectronic properties, and the general chemical formula Mg$_{x}$TM$_{1-x}$N (TM=Ti, Zr, Hf, Nb). These compounds form over a broad metal composition range and our experiments show that Mg-rich compositions are nondegenerate semiconductors with visible-range optical absorption onsets (1.8-2.1 eV). Lattice parameters are compatible with growth on a variety of substrates, and epitaxially grown MgZrN$_{2}$ exhibits remarkable electron mobilities approaching 100 cm$^{2}$V$^{-1}$s$^{-1}$. Ab initio calculations reveal that these compounds have disorder-tunable optical properties, large dielectric constants and low carrier effective masses that are insensitive to disorder. Overall, these experimental and theoretical results highlight Mg$_{G-3}$TMN$_{G-2}$ rocksalts as a new class of semiconductor materials with promising properties for optoelectronic applications.

cond-mat.mtrl-sci↗

arXiv2018

A Map of the Inorganic Ternary Metal Nitrides

Exploratory synthesis in novel chemical spaces is the essence of solid-state chemistry. However, uncharted chemical spaces can be difficult to navigate, especially when materials synthesis is challenging. Nitrides represent one such space, where stringent synthesis constraints have limited the exploration of this important class of functional materials. Here, we employ a suite of computational materials discovery and informatics tools to construct a large stability map of the inorganic ternary metal nitrides. Our map clusters the ternary nitrides into chemical families with distinct stability and metastability, and highlights hundreds of promising new ternary nitride spaces for experimental investigation--from which we experimentally realized 7 new Zn- and Mg-based ternary nitrides. By extracting the mixed metallicity, ionicity, and covalency of solid-state bonding from the DFT-computed electron density, we reveal the complex interplay between chemistry, composition, and electronic structure in governing large-scale stability trends in ternary nitride materials.

cond-mat.mtrl-sci↗

arXiv2018

An L$_0$L$_1$-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.

physics.comp-ph↗

arXiv2018

Hidden structural order controls Li-ion transport in cation-disordered oxides for rechargeable lithium batteries

Crystal structures play a vital role in determining materials properties. In Li-ion cathodes, the crystal structure defines the dimensionality and connectivity of interstitial sites, thus determining Li-ion diffusion kinetics. While a perfect crystal has infinite structural coherence, a class of recently discovered high-capacity cathodes, Li-excess cation-disordered rocksalts, falls on the other end of the spectrum: Their cation sublattices are assumed to be randomly populated by Li and transition metal ions with zero configurational coherence based on conventional X-ray diffraction, such that the Li transport is purely determined by statistical effects. In contrast to this prevailing view, we reveal that cation short-range order, hidden in diffraction, is ubiquitous in these long-range disordered materials and controls the local and macroscopic environments for Li-ion transport. Our work not only discovers a crucial property that has previously been overlooked, but also provides new guidelines for designing and engineering disordered rocksalts cathode materials.

cond-mat.mtrl-sci↗

arXiv2018

On the Balance of Intercalation and Conversion Reactions in Battery Cathodes

We present a thermodynamic analysis of the driving forces for intercalation and conversion reactions in battery cathodes across a range of possible working ion, transition metal, and anion chemistries. Using this body of results, we analyze the importance of polymorph selection as well as chemical composition on the ability of a host cathode to support intercalation reactions. We find that the accessibility of high energy charged polymorphs in oxides generally leads to larger intercalation voltages favoring intercalation reactions, whereas sulfides and selenides tend to favor conversion reactions. Furthermore, we observe that Cr-containing cathodes favor intercalation more strongly than those with other transition metals. Finally, we conclude that two-electron reduction of transition metals (as is possible with the intercalation of a $2+$ ion) will favor conversion reactions in the compositions we studied.

cond-mat.mtrl-sci↗

arXiv2018

Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm

The atomistic modeling of amorphous materials requires structure sizes and sampling statistics that are challenging to achieve with first-principles methods. Here, we propose a methodology to speed up the sampling of amorphous and disordered materials using a combination of a genetic algorithm and a specialized machine-learning potential based on artificial neural networks (ANN). We show for the example of the amorphous LiSi alloy that around 1,000 first-principles calculations are sufficient for the ANN potential assisted sampling of low-energy atomic configurations in the entire amorphous LixSi phase space. The obtained phase diagram is validated by comparison with the results from an extensive sampling of LixSi configurations using molecular dynamics simulations and a general ANN potential trained to ~45,000 first-principles calculations. This demonstrates the utility of the approach for the first-principles modeling of amorphous materials.

cond-mat.dis-nn↗

arXiv2017

Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species

Machine-learning potentials (MLPs) for atomistic simulations are a promising alternative to conventional classical potentials. Current approaches rely on descriptors of the local atomic environment with dimensions that increase quadratically with the number of chemical species. In this article, we demonstrate that such a scaling can be avoided in practice. We show that a mathematically simple and computationally efficient descriptor with constant complexity is sufficient to represent transition-metal oxide compositions and biomolecules containing 11 chemical species with a precision of around 3 meV/atom. This insight removes a perceived bound on the utility of MLPs and paves the way to investigate the physics of previously inaccessible materials with more than ten chemical species.

cond-mat.mtrl-sci↗