Theoretical Insights into the Effects of Oxidation and Mo-Doping on the Structure and Stability of Pt–Ni Nanoparticles

Pt–Ni nanoparticles are promising catalysts for the oxygen reduction reaction but they suffer from Ni dissolution in oxidizing conditions. It has recently been shown that it is possible to stabilize octahedral Pt–Ni nanoparticles by doping them with a small amount of Mo. Using ab initio calculations and a quaternary cluster expansion, we provide atomic-scale explanations for the enhanced stability of Mo-doped Pt–Ni nanoparticles. We predict that for Mo-doped Pt₃Ni nanoparticles with only a small amount of Mo doping (around 1.6% mole fraction) the equilibrium concentration of Ni atoms on the particle surface is greatly reduced, limiting the rate at which Ni atoms dissolve from the particles. Mo doping also increases Pt/Ni vacancy formation energies in the surface layer, which further stabilizes the nanoparticles against Ni dissolution and helps preserve the nanoparticle shape. Our calculations also reveal insights into the shape evolution of Pt–Ni nanoparticles: the preferential oxidation of edges can make (111) face sites more vulnerable to dissolution than edge sites, which may contribute to the observed formation of Pt–Ni nanoframes and nanoparticles with concave surfaces

Catalytic Activity Maps for Alloy Nanoparticles

To enable rational design of alloy nanoparticle catalysts, we develop an approach to generate catalytic activity maps of alloy nanoparticles on a grid of particle size and composition. The catalytic activity maps are created by using a quaternary cluster expansion to explicitly predict adsorbate binding energies on alloy nanoparticles of varying shape, size, and atomic order while accounting for interactions among the adsorbates. This cluster expansion is used in kinetic Monte Carlo simulations to predict activated nanoparticle structures and turnover frequencies on all surface sites. We demonstrate our approach on Pt–Ni octahedral nanoparticle catalysts for the oxygen reduction reaction (ORR), revealing that the specific activity is predicted to be optimized at an edge length of larger than 5.5 nm and a composition of about Pt0.85Ni0.15 and the mass activity is predicted to be optimized at an edge length of 3.3–3.8 nm and a composition of about Pt0.8Ni0.2

Rational Design of Pt₃Ni Surface Structures for the Oxygen Reduction Reaction

A cluster expansion approach has been used to investigate the relationship between surface structures of Pt₃Ni alloy catalysts and their catalytic activity. With the help of this approach, we have constructed a direct bridge between the atomic structure and catalytic properties of Pt–Ni catalysts at a variety of compositions and chemical environments. We predict that Pt₃Ni­(111) surfaces have substantial subsurface disorder, and as a result, the ORR activity of different surface sites varies by approximately 3 orders of magnitude. Using this model, we identify a Pt₃Ni­(111) surface with a multilayer Pt skin that is predicted to maximize catalytic activity and predict the conditions under which a Pt₃Ni surface should be synthesized to realize high catalytic activity

Nature of the Amorphous–Amorphous Interfaces in Solid-State Batteries Revealed Using Machine-Learned Interatomic Potentials

Non-crystalline solid materials have attracted growing attention in energy storage for their desirable properties such as ionic conductivity, stability, and processability. However, compared to bulk crystalline materials, fundamental understanding of these highly complex metastable systems is hindered by the scale limitations of density functional theory (DFT) calculations and resolution limitations of experimental methods. To fill the knowledge gap and guide the rational design of amorphous battery materials and interfaces, we present a molecular dynamics (MD) framework based on machine-learned interatomic potentials trained on the fly to study the amorphous solid electrolyte Li3PS4 and its protective coating, amorphous Li3B11O18. The use of machine-learned potentials allows us to simulate the materials at time and length scales that are not accessible to DFT while maintaining a near-DFT level of accuracy. This approach allows us to calculate amorphization energies, amorphous–amorphous interface energies, and the impact of the interface on lithium ion conductivity. This study demonstrates the promising role of actively learned interatomic potentials in extending the application of ab initio modeling to more complex and realistic systems such as amorphous materials and interfaces

Ionic Dynamics of the Charge Carrier in Layered Solid Materials for Mg Rechargeable Batteries

Multivalent-ion batteries have attracted growing attention due to their high theoretical energy density that potentially outperforms Li-ion batteries. One of the critical challenges of realizing a multivalent-ion battery is the strong polarization that results in the sluggish intercalation of ions in the host lattice, which motivates a fundamental understanding of multivalent-ion dynamics in solid-state materials. In this contribution, we investigate the diffusion mechanisms of divalent ions in a novel Mg anode coating, BiOCl, using first-principles informed learning-on-the-fly molecular dynamics. Based on nanosecond-scale dynamics observations, we gained insights into the concerted diffusion mechanism of Mg cation site-to-site hopping facilitated by synchronous anion rotational motion. Furthermore, we compute the Mg-ion diffusion in additional candidate host structures screened from available layered materials space. The results suggest the co-operative divalent cation–anion motion is likely a common phenomenon in layered oxyhalide structures. Our findings provide a new perspective on how to enhance multivalent-ion diffusion in layered materials

Evaluation of Tavorite-Structured Cathode Materials for Lithium-Ion Batteries Using High-Throughput Computing

Cathode materials with structure similar to the mineral tavorite have shown promise for use in lithium-ion batteries, but this class of materials is relatively unexplored. We use high-throughput density-functional-theory calculations to evaluate tavorite-structured oxyphosphates, fluorophosphates, oxysulfates, and fluorosulfates for use as cathode materials in lithium-ion batteries. For each material we consider the insertion of both one and two lithium ions per redox-active metal, calculating average voltages and stability relative to a database of nearly 100,000 previously calculated compounds. To evaluate lithium mobility, we calculate the activation energies for lithium diffusion through the known tavorite cathode materials LiVO(PO4), LiV(PO4)F, and LiFe(SO4)F. Our calculations indicate that tavorite-structured materials are capable of very high rates of one-dimensional lithium diffusion, and several tavorite-structured materials may be capable of reversibly inserting two lithium ions per redox-active metal

Evaluation of Tavorite-Structured Cathode Materials for Lithium-Ion Batteries Using High-Throughput Computing

Cathode materials with structure similar to the mineral tavorite have shown promise for use in lithium-ion batteries, but this class of materials is relatively unexplored. We use high-throughput density-functional-theory calculations to evaluate tavorite-structured oxyphosphates, fluorophosphates, oxysulfates, and fluorosulfates for use as cathode materials in lithium-ion batteries. For each material we consider the insertion of both one and two lithium ions per redox-active metal, calculating average voltages and stability relative to a database of nearly 100,000 previously calculated compounds. To evaluate lithium mobility, we calculate the activation energies for lithium diffusion through the known tavorite cathode materials LiVO(PO4), LiV(PO4)F, and LiFe(SO4)F. Our calculations indicate that tavorite-structured materials are capable of very high rates of one-dimensional lithium diffusion, and several tavorite-structured materials may be capable of reversibly inserting two lithium ions per redox-active metal

Evaluation of Tavorite-Structured Cathode Materials for Lithium-Ion Batteries Using High-Throughput Computing

Cathode materials with structure similar to the mineral tavorite have shown promise for use in lithium-ion batteries, but this class of materials is relatively unexplored. We use high-throughput density-functional-theory calculations to evaluate tavorite-structured oxyphosphates, fluorophosphates, oxysulfates, and fluorosulfates for use as cathode materials in lithium-ion batteries. For each material we consider the insertion of both one and two lithium ions per redox-active metal, calculating average voltages and stability relative to a database of nearly 100,000 previously calculated compounds. To evaluate lithium mobility, we calculate the activation energies for lithium diffusion through the known tavorite cathode materials LiVO(PO4), LiV(PO4)F, and LiFe(SO4)F. Our calculations indicate that tavorite-structured materials are capable of very high rates of one-dimensional lithium diffusion, and several tavorite-structured materials may be capable of reversibly inserting two lithium ions per redox-active metal

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

Finding new compounds and their crystal structures is an essential step to new materials discoveries. We demonstrate how this search can be accelerated using a combination of machine learning techniques and high-throughput ab initio computations. Using a probabilistic model built on an experimental crystal structure database, novel compositions that are most likely to form a compound, and their most-probable crystal structures, are identified and tested for stability by ab initio computations. We performed such a large-scale search for new ternary oxides, discovering 209 new compounds with a limited computational budget. A list of these predicted compounds is provided, and we discuss the chemistries in which high discovery rates can be expected

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

Finding new compounds and their crystal structures is an essential step to new materials discoveries. We demonstrate how this search can be accelerated using a combination of machine learning techniques and high-throughput ab initio computations. Using a probabilistic model built on an experimental crystal structure database, novel compositions that are most likely to form a compound, and their most-probable crystal structures, are identified and tested for stability by ab initio computations. We performed such a large-scale search for new ternary oxides, discovering 209 new compounds with a limited computational budget. A list of these predicted compounds is provided, and we discuss the chemistries in which high discovery rates can be expected