A5010660375- Machine Learning in Materials Science
- Ionic liquids properties and applications
- Advanced Battery Materials and Technologies
- Advancements in Battery Materials
- Electrochemical Analysis and Applications
- Fuel Cells and Related Materials
- nanoparticles nucleation surface interactions
- Protein Structure and Dynamics
- Conducting polymers and applications
- Advanced Battery Technologies Research
- Catalysis and Oxidation Reactions
- Catalytic Processes in Materials Science
- Block Copolymer Self-Assembly
- Advanced Sensor and Energy Harvesting Materials
- Extraction and Separation Processes
- Pickering emulsions and particle stabilization
- Hydrogels: synthesis, properties, applications
- Asphalt Pavement Performance Evaluation
- Advanced Chemical Physics Studies
- Polymer Surface Interaction Studies
- Analytical Chemistry and Sensors
- Tribology and Lubrication Engineering
- Polymer crystallization and properties
- Neuroscience and Neural Engineering
- Topic Modeling
Harvard University
2018-2024
Harvard University Press
2018-2024
Robert Bosch (United States)
2018-2024
Imperial College London
2016-2021
Thomas Young Centre
2016-2021
Robert Bosch (Australia)
2018
Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs interactions of geometric tensors, resulting in a more information-rich faithful representation atomic environments. The method achieves state-of-the-art accuracy...
Currently available solid polymer electrolytes for Li-ion cells require deeper understanding and significant improvement in ionic transport properties to enable their use high-power batteries. We molecular dynamics simulations model the amorphous electrolyte system comprising poly(ethylene) oxide (PEO), lithium, bis(trifluoromethane)sulfonimide anion (TFSI), exploring effects of high salt concentrations relevant battery applications. Using statistical analysis ion distribution transport, we...
We show that strong cation-anion interactions in a wide range of lithium-salt/ionic liquid mixtures result negative lithium transference number, using molecular dynamics simulations and rigorous concentrated solution theory. This behavior fundamentally deviates from obtained self-diffusion coefficient analysis explains well recent experimental electrophoretic nuclear magnetic resonance measurements, which account for ion correlations. extend these findings to several ionic compositions....
Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), a SE(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs interactions of geometric tensors, resulting in more information-rich faithful representation atomic environments. The method achieves state-of-the-art accuracy...
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as "designer solvents" can be mixed precisely tailor the physiochemical properties. As using machine learning interatomic potentials (MLIPs) simulate ILs is still relatively unexplored, several questions need answered see if MLIPs transformative for ILs. Since often not pure, but either together or contain additives, we first demonstrate...
The restructuring of interfaces plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, particular, often adopt very different compositions morphologies at surfaces compared to the bulk. For first time, we reveal detailed atomistic picture long-time scale Pd deposited on Ag using microscopy, spectroscopy, novel simulation methods. By developing performing accelerated machine-learning molecular dynamics followed by an automated analysis method, discover...
Abstract Electrochemical stability windows of electrolytes largely determine the limitations operating regimes lithium-ion batteries, but degradation mechanisms are difficult to characterize and poorly understood. Using computational quantum chemistry investigate oxidative decomposition that govern voltage multi-component organic electrolytes, we find electrolyte is a process involving solvent salt anion requires explicit treatment their coupling. We ionization potential solvent-anion system...
Abstract Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency the underlying all-atom models. In this direction, machine learning approaches hold great promise fitting complex many-body data. However, training may require collection amounts expensive Moreover, quantifying trained model accuracy is challenging, especially...
Incorporation of elastomers into bioelectronics that reduces the mechanical mismatch between electronics and biological systems could potentially improve long-term electronics-tissue interface. However, chronic stability in physiological conditions has not been systematically studied. Here, using electrochemical impedance spectrum we find dielectric degrades over time environments. Both experimental computational results reveal this phenomenon is due to diffusion ions from solution time....
Salt-in-ionic liquid electrolytes have attracted significant attention as potential for next generation batteries largely due to their safety enhancements over typical organic electrolytes. However, recent experimental and computational studies shown that under certain conditions alkali cations can migrate in electric fields if they carried a net negative effective charge. In particular, were observed transference numbers at small mole fractions of alkali-metal salt revert the expected...
Strong anion-cation interaction in lithium-salt/ionic liquid electrolytes leads to ionic association that decreases the Li transference number, even causing it be negative. We show these interactions can greatly reduced by adding cyclic ethylene oxide molecules, and we quantitatively examine effect using rigorous multispecies concentrated solution theory coupled with molecular dynamics simulations. The added primarily lithium ionophore V also known as 12-crown-4, have high affinity lithium,...
The next-generation semiconductors and devices, such as halide perovskites flexible electronics, are extremely sensitive to water, thus demanding highly effective protection that not only seals out water in all forms (vapor, droplet, ice), but simultaneously provides mechanical flexibility, durability, transparency, self-cleaning. Although various solid-state encapsulation methods have been developed, no strategy is available can fully meet the above requirements. Here, we report a...
Ionic liquids (ILs) are a promising class of electrolytes owing to unique combination properties, such as extremely low vapour pressures, non-flammability and being universal solvents. Doping ILs with alkali metal salts creates an electrolyte that is interest for batteries, among others. These salt-in-ionic (SiILs) super-concentrated, strongly correlated asymmetric electrolytes. The transference number the cations has been found be negative, small but highly negatively charged aggregates...
Understanding the factors that govern gas absorption in ionic liquids is critical to development of high-capacity solvents for catalysis, electrochemistry, and separations. Here, we report experimental probes liquid structure provide insights into how free volume impacts O2 properties liquids. Specifically, establish isothermal compressibility─measured rapidly accurately through small-angle X-ray scattering─reports on size distribution transient voids within a representative series...
Coarse-grained molecular dynamics simulations are used to elucidate mechanisms responsible for different mechanical behaviours of elastomers containing spherical particles with volume fractions. We observe that filler fractions result in qualitatively responses the polymer nanocomposite tensile strain. At relatively low fraction a yield drop appears stress-strain curve. As increases there is reduction rate plastic hardening, becoming softening at sufficiently high fraction. demonstrate these...
We introduce a chemically inspired, all-atom model of hydrogenated nitrile butadiene rubber (HNBR) and assess its performance by computing the mass density glass-transition temperature as function cross-link in structure. Our HNBR structures are created procedure that mimics real process used to produce HNBR, is, saturation carbon-carbon double bonds NBR, either hydrogenation or cross-linking. The atomic interactions described "Optimized Potentials for Liquid Simulations" (OPLS-AA). In this...
Surface restructuring in bimetallic systems has recently been shown to play a crucial role heterogeneous catalysis. In particular, the segregation binary alloys can be reversed presence of strongly bound adsorbates. Mechanistic characterization such phenomena at atomic level remains scarce and challenging because large configurational space that must explored. To this end, we propose an automated method discover elementary surface processes unbiased fashion using Pd/Ag as example. We employ...
Nanoparticles have been recently shown to act as universal glues for both synthetic and biological gels, providing a tunable, cheap, general solution the centuries-old problem of sticking soft materials together. The design new adhesive solutions based on this platform, however, requires an understanding how nanoparticles' parameters concur determine final adhesion strength. Here, we use coarse-grained modeling molecular dynamics simulations investigate such links. Our main aim is show that,...
Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive, as one cannot rely on affordable mean square displacement approach. We use spectral decomposition short-time covariance to learn a set diffusion eigenmodes that encode correlation structure and form basis for analyzing trajectories. This allows systematic reduction uncertainty accelerate computations conductivity systems with steady-state structure. provide mathematical...
Restructuring of interfaces plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, particular, often adopt very different composition morphology at surfaces compared to the bulk. For first time, we reveal detailed atomistic picture long-timescale restructuring Pd deposited on Ag, using microscopy, spectroscopy, novel simulation methods. By developing performing accelerated machine-learning molecular dynamics followed by an automated analysis method,...
Recent experimental efforts have investigated nanoparticle solution as glues for hydrogels, focusing on the role of size and shape in influencing adhesion. Here, we use molecular dynamics simulations together with a coarse-grained model consider variety different morphologies to expand these experiments. Our main result is show that while nanoparticles' can play an important role, whether how much two factors affect adhesion strongly depend amount nanoparticles loaded at interface, well...
Recent works on ionic liquid electrolyte systems motivate the present study of transport regimes where strong species interactions result in significant correlations and deviations from ideal solution behaviour. In order to obtain a complete description these we use rigorous concentrated theory coupled with molecular dynamics simulations, beyond commonly used uncorrelated Nernst-Einstein equation. As case study, investigate NaFSI - Pyr 13 \FSI room temperature electrolyte. When fully...