Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (9 results)

  • Article
  • Peer Reviewed
Proceedings of the Annual Meeting of the Cognitive Science Society, Volume 44 (2022)
People collaborate daily to achieve joint goals, negotiating sidewalks, preparing meals, constructing furniture. Here we analyzed how strangers coordinate a complex novel task, assembling a TV cart. Before beginning and before major actions, partners used a photo of the completed cart, speech, and gesture to establish a joint model of the structure of the completed cart. Most coordination was implicit, a conversation of actions that could be understood in the context of the current state of the object and the shared mental representation. Actions served a dual purpose, both to communicate and to advance the task. The basic unit of coordination was the dyad, not the individual.
    Cover page: Explicit and Implicit Coordination of Joint ActionCreative Commons 'BY' version 4.0 license
    • Thesis
    • Peer Reviewed
    UC San Diego Electronic Theses and Dissertations (2019)

    In the past decades, theoretical calculations of materials properties have become more accurate and accessible due to the successful development of ab initio codes, as well as advances in computational power. With the booming development of high-throughput computational materials repositories, opportunities have emerged in the area of data-driven discovery of new materials guided by machine learning. However, the interpretation of large materials data sets needs to be performed from an integrated perspective of statistics and materials science intuition. In this thesis, we will address this challenge by demonstrating how the integration of high-throughput software workflows, automated data generation, and machine learning can yield powerful new approaches to materials analysis and optimization. This thesis is broadly divided into two topics.

    In the first topic (Chapters 2 and 3), we present comprehensive first-principle investigations of the effect of transition metal mixing on layered P2 oxides, using P2 NaxCo0.2Mn0.2Ti0.2Ni0.2Ru0.2O2 as model systems. Our results show that transition metal mixing significantly suppresses the formation of strongly ordered intermediates. Using ab initio molecular dynamics simulations and the climbing image nudged elastic band method, we reveal that transition metal substitution has a pronounced effect on the Na site occupancy energy and Na diffusion energy barriers. By employing a site percolation model, we derive theoretical upper and lower bounds on the concentration of transition metal species in the layered P2 oxides based on their effects on Na diffusion energy barriers. Another key innovation is the use of the MatErials Graph Network (MEGNet) model, a graph-based deep learning approach recently developed in our group, on layered P2 oxides for accurate energy prediction, which we will apply to study mixing energies in a “high-entropy” P2 NaxCo0.2Mn0.2Ti0.2Ni0.2Ru0.2O2.

    In the second topic (Chapters 4, 5, and 6), we present the development of a first-of-its-kind computational reference XAS database (XASdb). More importantly, we have developed a novel Ensemble-Learned Spectra IdEntification (ELSIE) algorithm that leverages on ensemble learning techniques to match an unknown target K-edge XANES spectra with computed spectra in XASdb. We will also discuss the development of general machine learning approaches to rapidly and efficiently identify the coordination environment of absorbing atoms from K-edge XANES.

      Cover page: Machine Learning of Big Materials Data
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2020)
      Analyzing coordination environments using X-ray absorption spectroscopy has broad applications in solid-state physics and material chemistry. Here, we show that random forest models trained on 190,000 K-edge X-ray absorption near-edge structure (XANES) spectra can identify the main atomic coordination environment with a high accuracy of 85.4% and all associated coordination environments with a high Jaccard score of 81.8% for 33 cation elements in oxides, significantly outperforming other machine-learning models. In a departure from prior works, the coordination environment is described as a distribution over 25 distinct coordination motifs with coordination numbers ranging from 1 to 12. More importantly, we show that the random forest models can be used to predict coordination environments from experimental K-edge XANES with minimal loss in accuracy. A drop-variable feature importance analysis highlights the key roles that the pre-edge and main-peak regions play in coordination environment identification.
        Cover page: Random Forest Models for Accurate Identification of Coordination Environments from X-Ray Absorption Near-Edge Structure
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2019)
        Graph networks are a new machine learning (ML) paradigm that supports both relational reasoning and combinatorial generalization. Here, we develop universal MatErials Graph Network (MEGNet) models for accurate property prediction in both molecules and crystals. We demonstrate that the MEGNet models outperform prior ML models such as the SchNet in 11 out of 13 properties of the QM9 molecule data set. Similarly, we show that MEGNet models trained on ?60 000 crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps, and elastic moduli of crystals, achieving better than density functional theory accuracy over a much larger data set. We present two new strategies to address data limitations common in materials science and chemistry. First, we demonstrate a physically intuitive approach to unify four separate molecular MEGNet models for the internal energy at 0 K and room temperature, enthalpy, and Gibbs free energy into a single free energy MEGNet model by incorporating the temperature, pressure, and entropy as global state inputs. Second, we show that the learned element embeddings in MEGNet models encode periodic chemical trends and can be transfer-learned from a property model trained on a larger data set (formation energies) to improve property models with smaller amounts of data (band gaps and elastic moduli).
          Cover page: Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2018)
          X-ray absorption spectroscopy (XAS) is a widely-used materials characterization technique. In this work we present a database of computed XAS spectra, using the Green's formulation of the multiple scattering theory implemented in the FEFF code. With more than 500,000 K-edge X-ray absorption near edge (XANES) spectra for more than 40,000 unique materials, this database constitutes the largest existing collection of computed XAS spectra to date. The data is openly distributed via the Materials Project, enabling researchers across the world to access it for free and use it for comparisons with experiments and further analysis.
            Cover page: High-throughput computational X-ray absorption spectroscopy
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2021)
            The L-edge X-ray Absorption Near Edge Structure (XANES) is widely used in the characterization of transition metal compounds. Here, we report the development of a database of computed L-edge XANES using the multiple scattering theory-based FEFF9 code. The initial release of the database contains more than 140,000 L-edge spectra for more than 22,000 structures generated using a high-throughput computational workflow. The data is disseminated through the Materials Project and addresses a critical need for L-edge XANES spectra among the research community.
              Cover page: Database of ab initio L-edge X-ray absorption near edge structure
              • Article
              • Peer Reviewed
              UC San Diego Previously Published Works (2021)
              Mixing transition metal cations in nearly equiatomic proportions in layered oxide cathode materials is a new strategy for improving the performances of Na-ion batteries. The mixing of cations not only offers entropic stabilization of the crystal structure but also benefits the diffusion of Na ions with tuned diffusion activation energy barriers. In light of this strategy, a high-rate Na0.6(Ti0.2Mn0.2Co0.2Ni0.2Ru0.2)O2 cathode was designed, synthesized, and investigated, combining graph-based deep learning calculations and complementary experimental characterizations. This new cathode material delivers high discharge capacities of 164 mA g-1 at 0.1 C and 68 mAh g-1 at a very high rate of 86 C, demonstrating an outstanding high rate capability. Ex situ and operando synchrotron X-ray diffraction were used to reveal the detailed structural evolution of the cathode upon cycling. Using the climbing-image nudged elastic-band calculation and Ab initio molecular dynamics simulations, we show that the optimal transition metal composition enables a percolating network of low barrier pathways for fast, macroscopic Na diffusion, resulting in the observed high rate performance.
                Cover page: Multiprincipal Component P2-Na0.6(Ti0.2Mn0.2Co0.2Ni0.2Ru0.2)O2 as a High-Rate Cathode for Sodium-Ion Batteries
                • Article
                • Peer Reviewed
                UC San Diego Previously Published Works (2018)
                X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique to determine oxidation states, coordination environment, and other local atomic structure information. Analysis of XAS relies on comparison of measured spectra to reliable reference spectra. However, existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage. In this work, we report the development of XASdb, a large database of computed reference XAS, and an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra (XANES) for over 40,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. FEFF is a computer program uses a real-space Green's function approach to calculate X-ray absorption spectra. We will demonstrate that the ELSIE algorithm, which combines 33 weak "learners" comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures. The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of veidt, an open source machine-learning library for materials science.
                  • Article
                  • Peer Reviewed
                  UC San Diego Previously Published Works (2018)
                  The following text has been added to the Acknowledgements section: “L. F. J. P. acknowledges support from the National Science Foundation (DMREF-1627583)”
                    Top