Modeling the behavior of materials composed of elements with different bonding and electronic structure character for large spatial and temporal scales and over a large compositional range, is a challenging problem. A case in point are amorphous alloys of Si, a prototypical covalent material, and Li, a prototypical metal, which are being considered as anodes for high-energydensity batteries. To address this challenge, we develop a methodology based on neural networks, that extends the conventional training approach to incorporate pre-trained parts that capture the character of different components, into the overall network; we refer to this model as the "implanted neural network" method. We show that this approach works well for the Si-Li amorphous alloys for a wide range of compositions, giving good results for key quantities like the diffusion coefficients. The method is readily generalizable to more complicated situations that involve two or more different elements.
Note: This paper is part of the JCP Special Topic on Machine Learning Meets Chemical Physics.
Many structural and mechanical properties of crystals, glasses, and biological macromolecules can be modeled from the local interactions between atoms. These interactions ultimately derive from the quantum nature of electrons, which can be prohibitively expensive to simulate. Machine learning has the potential to revolutionize materials modeling due to its ability to efficiently approximate complex functions. For example, neural networks can be trained to reproduce results of density functional theory calculations at a much lower cost. However, how neural networks reach their predictions is not well understood, which has led to them being used as a "black box" tool. This lack of understanding is not desirable especially for applications of neural networks in scientific inquiry. We argue that machine learning models trained on physical systems can be used as more than just approximations since they had to "learn" physical concepts in order to reproduce the labels they were trained on. We use dimensionality reduction techniques to study in detail the representation of silicon atoms at different stages in a neural network, which provides insight into how a neural network learns to model atomic interactions.
We have developed a semi-empirical and many-body type model potential using a modified charge density profile for Cu-Ni alloys based on the embedded-atom method (EAM) formalism with an improved optimization technique. The potential is determined by fitting to experimental and first-principles data for Cu, Ni and Cu-Ni binary compounds, such as lattice constants, cohesive energies, bulk modulus, elastic constants, diatomic bond lengths and bond energies. The generated potentials were tested by computing a variety of properties of pure elements and the alloy of Cu, Ni: the melting points, alloy mixing enthalpy, lattice specific heat, equilibrium lattice structures, vacancy formation and interstitial formation energies, and various diffusion barriers on the (100) and (111) surfaces of Cu and Ni.
The structural evolution of supercooled liquid water as we approach the glass transition temperature continues to be an active area of research. Here, we use molecular dynamics simulations of TIP4P/Ice to study the changes in the connected regions of empty space within the liquid which we interrogate using the Voronoi-voids network.We observe two important features: supercooling enhances the fraction of non-spherical voids and different sizes of voids tend to cluster forming a percolating network. By examining order parameters such as the local structure index (LSI), tetrahedrality and topological defects, we show that water molecules near large void clusters tend to be slightly more tetrahedral than those near small voids, with a lower population of underand over-coordinated defects. We show further that the distribution of closed rings of water molecules around small and large void clusters maintain a balance between 6 1 and 7 membered rings. Our results highlight the changes of the dual voids and water network as a structural hallmark of supercooling which provides insights into the molecular origins of cooperative effects that underlie density fluctuations occurring on the sub-nanometer and nanometer length scale. The percolation of the voids and the hydrogen bond network around the voids, may serve as useful order parameters to investigate density fluctuations in supercooled water.
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