Shyue Ping Ong

Bio: Shyue Ping Ong is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Fast ion conductor & Density functional theory. The author has an hindex of 61, co-authored 188 publications receiving 21705 citations. Previous affiliations of Shyue Ping Ong include Massachusetts Institute of Technology & Samsung.

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Abstract: Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design. © 2013 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.

Voltage, stability and diffusion barrier differences between sodium-ion and lithium-ion intercalation materials

Abstract: To evaluate the potential of Na-ion batteries, we contrast in this work the difference between Na-ion and Li-ion based intercalation chemistries in terms of three key battery properties—voltage, phase stability and diffusion barriers. The compounds investigated comprise the layered AMO2 and AMS2 structures, the olivine and maricite AMPO4 structures, and the NASICON A3V2(PO4)3 structures. The calculated Na voltages for the compounds investigated are 0.18–0.57 V lower than that of the corresponding Li voltages, in agreement with previous experimental data. We believe the observed lower voltages for Na compounds are predominantly a cathodic effect related to the much smaller energy gain from inserting Na into the host structure compared to inserting Li. We also found a relatively strong dependence of battery properties on structural features. In general, the difference between the Na and Li voltage of the same structure, ΔVNa–Li, is less negative for the maricite structures preferred by Na, and more negative for the olivine structures preferred by Li. The layered compounds have the most negative ΔVNa–Li. In terms of phase stability, we found that open structures, such as the layered and NASICON structures, that are better able to accommodate the larger Na+ ion generally have both Na and Li versions of the same compound. For the close-packed AMPO4 structures, our results show that Na generally prefers the maricite structure, while Li prefers the olivine structure, in agreement with previous experimental work. We also found surprising evidence that the barriers for Na+ migration can potentially be lower than that for Li+ migration in the layered structures. Overall, our findings indicate that Na-ion systems can be competitive with Li-ion systems.

Voltage, Stability and Diffusion Barrier Differences between Sodium-ion and Lithium-ion intercalation Materials

Abstract: To evaluate the potential of Na-ion batteries, we contrast in this work the difference between Na-ion and Li-ion based intercalation chemistries in terms of three key battery properties—voltage, phase stability and diffusion barriers. The compounds investigated comprise the layered AMO2 and AMS2 structures, the olivine and maricite AMPO4 structures, and the NASICON A3V2(PO4)3 structures. The calculated Na voltages for the compounds investigated are 0.18–0.57 V lower than that of the corresponding Li voltages, in agreement with previous experimental data. We believe the observed lower voltages for Na compounds are predominantly a cathodic effect related to the much smaller energy gain from inserting Na into the host structure compared to inserting Li. We also found a relatively strong dependence of battery properties on structural features. In general, the difference between the Na and Li voltage of the same structure, DVNa–Li, is less negative for the maricite structures preferred by Na, and more negative for the olivine structures preferred by Li. The layered compounds have the most negative DVNa–Li. In terms of phase stability, we found that open structures, such as the layered and NASICON structures, that are better able to accommodate the larger Na+ ion generally have both Na and Li versions of the same compound. For the close-packed AMPO4 structures, our results show that Na generally prefers the maricite structure, while Li prefers the olivine structure, in agreement with previous experimental work. We also found surprising evidence that the barriers for Na+ migration can potentially be lower than that for Li+ migration in the layered structures. Overall, our findings indicate that Na-ion systems can be competitive with Li-ion systems.

Design principles for solid-state lithium superionic conductors

Abstract: Achieving a Li-ion conductivity in the solid state comparable to existing liquid electrolytes is challenging. A fundamental relationship between anion packing and ionic transport now reveals desirable structural attributes for Li-ion conductors.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Abstract: Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ‘‘rapid-prototyping’’ of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design. © 2013 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.