 job.
” I selected Survivor by Destiny’s Child and also I likewise included a little bit of Gloria Gaynor I Will Survive.
” There’s a culture in the songs sector,” she proceeds. “When I was 20 or 21, I was told by a manager of mine that I needed to be a lot more sex-related in meetings with A&R. I required them to assume that they might copulate me.
I required to be more frisky and more sexual. It was said to me, ‘that author is doing far better than you because she’s making them assume that they can copulate her’. That’s not my vibe.
While she doesn’t think the music market will certainly have its ‘Weinstein minute’, she thinks a change is coming.
I’ve already had writers ask me ‘have I ever before made you seem like that? I would dislike to assume I would have made you feel like that.’ Individuals are beginning to be a lot much more respectful in sessions. We just need to maintain it relocating as well as make it concerning the art form.
” Hopefully all these vile predators will not have the power anymore. It’s actually a power struggle, isn’t it? Specific things require to be conquered, however it’s coming,” she reckons.
AtomSets -- A Hierarchical Transfer Learning Framework for Small and Large Materials Datasets

Predicting materials properties from composition or structure is of great interest to the materials science community. Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when dealing with large materials data. However, deep learning models suffer in the small data regime that is common in materials science. Here we leverage the transfer learning concept and the graph network deep learning framework and develop the AtomSets machine learning framework for consistent high model accuracy at both small and large materials data. The AtomSets models can work with both compositional and structural materials data. By combining with transfer learned features from graph networks, they can achieve state-of-the-art accuracy from using small compositional data (<400) to large structural data (>130,000). The AtomSets models show much lower errors than the state-of-the-art graph network models at small data limits and the classical machine learning models at large data limits. They also transfer better in the simulated materials discovery process where the targeted materials have property values out of the training data limits. The models require minimal domain knowledge inputs and are free from feature engineering. The presented AtomSets model framework opens new routes for machine learning-assisted materials design and discovery.


Introduction
Machine learning (ML) has garnered substantial interest as an effective method for developing surrogate models for materials property predictions in recent years. 1,2 However, a critical bottleneck is that materials datasets are often small and inhomogeneous, making it challenging to train reliable models. While large density functional theory (DFT) databases such as the Materials Project, 3 Open Quantum Materials Database 4 and AFLOWLIB 5 have ∼O(10 6 ) relaxed structures and computed energies, data on other computed properties such as band gaps, elastic constants, dielectric constants, etc. tend to be several times or even orders of magnitude fewer. 2 In general, deep learning models based on neural networks tend to require much more data to train, resulting in lower performance in small datasets relative to non-deep learning models. For example, Dunn et al. 6 have found that while deep learning models such as the MatErials Graph Networks (MEGNet) 7 and Crystal Graph Convolutional Neural Network (CGCNN) 8 achieve state-of-the-art performance for datasets with > O(10 4 ) data points, ensembles of non-deep-learning models (using AutoMatminer) outperform these deep learning models when the data set size is < O(10 4 ), and especially when the data set is < O(10 3 ).
Several approaches have been explored to address the data bottleneck. The most popular approach is transfer learning (TL), wherein the weights from models trained on a property with a large data size are "transferred" to a model on smaller data size. Most TL studies were performed on the same property. For example, Hutchinson et al. 9 developed three TL approaches that reduced the model errors in predicting experimental band gaps by including DFT band gaps. Similarly, Jha et al. 10 trained models on the formation energies in the large OQMD database and demonstrated that transferring the model weights from OQMD can improve the models on the small DFT-computed and even experimental formation energy data. TL has also been demonstrated between different properties in some cases. For example, the present authors 7 found that transferring the weights from large data-size formation energy MEGNet models to smaller-data-size band gap and elastic moduli models improved convergence rate and accuracy. Another approach uses multi-fidelity models, where datasets of multiple fidelities (e.g., band gaps computed with different functionals or measured experimentally) are used to improve prediction performance on the more valuable, high fidelity properties. For example, two-fidelity co-kriging methods have demonstrated successes in improving the predictions of the Heyd-Scuseria-Ernzerhof (HSE) 11 band gaps of perovskites, 12 defect energies in hafnia 13 and DFT bulk moduli. 14 In a recently published work, the present authors also developed multi-fidelity MEGNet models that utilize band gap data from four DFT functionals (Perdew-Burke-Ernzerhof 15 or PBE, Gritsenko-Leeuwen-Lenthe-Baerends with solid correction 16,17 or GLLB-SC, strongly constrained and appropriately normed 18 or SCAN and HSE 11 ) and experimental measurements to significant improve the prediction of experimental band gaps. 19 In this work, we develop "AtomSets", a hierarchical framework to TL using MEGNet models that can achieve uniformly excellent performance across diverse datasets with different sizes. The AtomSets framework unifies compositional and structural features under one umbrella. We show, for the first time, TL from structural models to compositional models.
Using 13 MatBench datasets, 6 we show that the AtomSets models can achieve excellent performance even when the inputs are compositional and the data size is small (∼ 300), while retaining MEGNet's state-of-the-art performance on properties with large data sizes. Furthermore, the model construction requires minimal domain knowledge and no feature engineering.

MatErials Graph Network
The MEGNet formalism has been described extensively in previous works 7, 19 and interested readers are referred to those publications for details. Briefly, the MEGNet framework featurizes a material into a graph G = (V, E, u), where v i ∈ V are the atom or node features, e k ∈ E are the edges or bonds, and u are state features. The features matrices/vectors are MLP Readout v r Atom embedding AE Figure 1: Graph networks and AtomSets schematics. a, The graph convolution (GC) takes an input graph with labeled atom (V), bond (E) and state (u) attributes and outputs a new computed graph with updated attributes. b, The graph network model architecture. The input to the model is the structure graph (S) with atomic number as the atom attributes. Then the graph is passed to an atom embedding (AE) layer, followed by three GC layers. After the GC, the graph is readout to a structure-wise vector f, and f is further passed to multi-layer perceptron (MLP) models. Within the model, each layer output is captured for later use. c, The AtomSets model takes a site-wise/element-wise feature matrix and passes to MLP layers. After the MLP, the a readout function is applied to derive a structurewise/formula-wise vector, followed by final MLP layers. Figure 1a, by updating the the atom, bond and state features as follows: where i is an index indicating the layer of the GC, e In the initial structural graph (S), the atom attributes are simply the atomic number of the element embedded into a vector space via an atom embedding (AE) layer (AE : Figure 1b. The bonds are constructed by considering atom pairs within certain cutoff radius R c. With each GC layer, information is exchanged between atoms, bonds and state. As more GC layers are stacked (e.g., GC 2 and GC 3 in Figure 1b), information on each atom can be propagated to further distances.
In this work, a MEGNet model with three GC layers was first trained on the formation energies of more than 130,000 Materials Project crystals as of Jun 1 2019, henceforth referred to as the "parent" model. The training procedures and hyperparameter settings of the MEGNet models are similar as the previous work. 7

AtomSets Framework
In our proposed AtomSets framework, the output atom Na ] features after each GC layer are extracted from the parent model and transferred to develop models for other properties. Bond features are not considered in TL since the number of bonds depends on the graph construction settings and parameters, such as cutoff radius. As shown in Figure  1c, an AtomSets model takes the atom-wise features V i matrix of shape N a × N f as inputs to an MLP model. These features can either be compositional, e.g., elemental properties, or structural, e.g., local environment descriptor. Afterwards, the output feature matrix is readout to a vector, compressing the atom number dimension.
The purpose of the readout function is to reduce the feature matrices with different number of atoms to structure-wise vectors subject to permutational invariance. Simple functions to calculate the statistics along