We discover the chemical composition of over 1000 materials that are likely to exhibit layered and 2D phases but have yet to be synthesized. This includes two materials our calculations indicate can ...exist in distinct structures with different band gaps, expanding the short list of 2D phase-change materials. Whereas databases of over 1000 layered materials have been reported, we provide the first full database of materials that are likely layered but are yet to be synthesized, providing a roadmap for the synthesis community. We accomplish this by combining physics with machine learning on experimentally obtained data and verify a subset of candidates using density functional theory. We find that our model performs five times better than practitioners in the field at identifying layered materials and is comparable to or better than professional solid-state chemists. Finally, we find that semisupervised learning can offer benefits for materials design where labels for some of the materials are unknown.
Despite the fast progress in training specialized models for various tasks, learning a single general model that works well for many tasks is still challenging for computer vision. Here we introduce ...multi-task self-training (MuST), which harnesses the knowledge in independent specialized teacher models (e.g., ImageNet model on classification) to train a single general student model. Our approach has three steps. First, we train specialized teachers independently on labeled datasets. We then use the specialized teachers to label an unlabeled dataset to create a multi-task pseudo labeled dataset. Finally, the dataset, which now contains pseudo labels from teacher models trained on different datasets/tasks, is then used to train a student model with multi-task learning. We evaluate the feature representations of the student model on 6 vision tasks including image recognition (classification, detection, segmentation) and 3D geometry estimation (depth and surface normal estimation). MuST is scalable with unlabeled or partially labeled datasets and outperforms both specialized supervised models and self-supervised models when training on large scale datasets. Lastly, we show MuST can improve upon already strong checkpoints 23 trained with billions of examples. The results suggest self-training is a promising direction to aggregate labeled and unlabeled training data for learning general feature representations.
We report a solid-state Li-ion electrolyte predicted to exhibit simultaneously fast ionic conductivity, wide electrochemical stability, low cost, and low mass density. We report exceptional density ...functional theory (DFT)-based room-temperature single-crystal ionic conductivity values for two phases within the crystalline lithium–boron–sulfur (Li–B–S) system: 62 (+9, −2) mS cm–1 in Li5B7S13 and 80 (−56, −41) mS cm–1 in Li9B19S33. We report significant ionic conductivity values for two additional phases: between 0.0056 and 0.16 mS/cm –1 in Li2B2S5 and between 0.0031 and 9.7 mS cm–1 in Li3BS3 depending on the room-temperature extrapolation scheme used. To our knowledge, our prediction gives Li9B19S33 and Li5B7S13 the second and third highest reported DFT-computed single-crystal ionic conductivities of any crystalline material. We compute the thermodynamic electrochemical stability window widths of these materials to be 0.50 V for Li5B7S13, 0.16 V for Li2B2S5, 0.45 V for Li3BS3, and 0.60 V for Li9B19S33. Individually, these materials exhibit similar or better ionic conductivity and electrochemical stability than the best-known sulfide-based solid-state Li-ion electrolyte materials, including Li10GeP2S12 (LGPS). However, we predict that electrolyte materials synthesized from a range of compositions in the Li–B–S system may exhibit even wider thermodynamic electrochemical stability windows of 0.63 V and possibly as high as 3 V or greater. The Li–B–S system also has a low elemental cost of approximately 0.05 USD/m2 per 10 μm thickness, which is significantly lower than that of germanium-containing LGPS, and a comparable mass density below 2 g/cm3. These fast-conducting phases were initially brought to our attention by a machine learning-based approach to screen over 12,000 solid electrolyte candidates, and the evidence provided here represents an inspiring success for this model.
Abstract
Numerical simulations have revolutionized material design. However, although simulations excel at mapping an input material to its output property, their direct application to inverse design ...has traditionally been limited by their high computing cost and lack of differentiability. Here, taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm, we introduce a computational inverse design framework that addresses these challenges, by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end differentiation. Thanks to its differentiability, the simulation is used to directly train a deep generative model, which outputs an optimal porous matrix based on an arbitrary input sorption isotherm curve. Importantly, this inverse design pipeline leverages the power of tensor processing units (TPU)—an emerging family of dedicated chips, which, although they are specialized in deep learning, are flexible enough for intensive scientific simulations. This approach holds promise to accelerate inverse materials design.
Machine learning (ML) methods have recently been used as forward solvers to predict the mechanical properties of composite materials. Here, we use a supervised autoencoder (SAE) to perform the ...inverse design of graphene kirigami, where predicting the ultimate stress or strain under tensile loading is known to be difficult due to nonlinear effects arising from the out-of-plane buckling. Unlike the standard autoencoder, our SAE is able not only to reconstruct cut configurations but also to predict the mechanical properties of graphene kirigami and classify the kirigami with either parallel or orthogonal cuts. By interpolating in the latent space of kirigami structures, the SAE is able to generate designs that mix parallel and orthogonal cuts, despite being trained independently on parallel or orthogonal cuts. Our method allows us to both identify alternate designs and predict, with reasonable accuracy, their mechanical properties, which is crucial for expanding the search space for materials design.
Polyiodide-Doped Graphene Hoyt, Robert A; Remillard, E. Marielle; Cubuk, Ekin D ...
Journal of physical chemistry. C,
01/2017, Letnik:
121, Številka:
1
Journal Article
Recenzirano
Iodine-doped graphene has recently attracted significant interest as a result of its enhanced conductivity and improved catalytic activity. Using density functional theory calculations, we obtain the ...formation energy, desorption rate, and electronic properties for graphene systems doped with polyiodide chains consisting of 1–6 iodine atoms in the low-concentration limit. We find that I3 and I5 act as p-type surface dopants that shift the Fermi level 0.46 and 0.57 eV below the Dirac point, respectively. For these two molecules, molecular orbital theory and analysis of the charge density show that doping transfers electronic charge to iodine π* molecular orbitals oriented perpendicular to the graphene sheet. For even-length polyiodides, we find that I6 and I4 decompose to I2, which readily desorbs at 300 K. Adsorption energy calculations further show that I3 acts as an effective catalyst for the oxygen reduction reaction on graphene by stabilizing the rate-limiting OOH intermediate.
Silicon is a promising anode material for high-capacity Li-ion batteries. Recent experiments show that lithiation of crystalline silicon nanowires leads to highly anisotropic morphologies. This has ...been interpreted as due to anisotropy in equilibrium interface energies, but this interpretation does not capture the dynamic, nonequilibrium nature of the lithiation process. Here, we provide a comprehensive explanation of experimentally observed morphological changes, based on first-principles multiscale simulations. We identify reaction paths and associated structural transformations for Li insertion into the Si {110} and {111} surfaces and calculate the relevant energy barriers from density functional theory methods. We then perform kinetic Monte Carlo simulations for nanowires with surfaces of different orientations, which reproduce to a remarkable degree the experimentally observed profiles and the relative reaction front rates.
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