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Found 23 papers, 13 papers with code
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights.
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Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Dendrite Suppression with Li Metal Anode
We predict over 20 mechanically anisotropic interfaces between Li metal and 6 solid electrolytes which can be used to suppress dendrite growth.
Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks
We demonstrate the potential for such a visualization approach by showing that patterns emerge automatically that reflect similarities at different scales in three representative classes of materials: perovskites, elemental boron, and general inorganic crystals, covering material spaces of different compositions, structures, and both.
Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials
Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges.
Domain Representation for Knowledge Graph Embedding
Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning.
Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs
Existing techniques either cannot be scaled to large-scale bipartite graphs that have limited labels or cannot exploit the unique structure of bipartite graphs, which have distinct node features in two domains.
Boosting Retailer Revenue by Generated Optimized Combined Multiple Digital Marketing Campaigns
Secondly, based on the sub-modular optimization theory and the DMC pool by DMCNet, the generated combined multiple DMCs are ranked with respect to their revenue generation strength then the top three ranked campaigns are returned to the sellers' back-end management system, so that retailers can set combined multiple DMCs for their online shops just in one-shot.
L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis
This paper tackles forecast combination with many forecasts or minimum variance portfolio selection with many assets.
GraphHop: An Enhanced Label Propagation Method for Node Classification
In Step 2, a new label vector is predicted for each node based on the label of the node itself and the aggregated label information obtained in Step 1.
Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties
Polymer electrolytes are promising candidates for the next generation lithium-ion battery technology.
Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Generating the periodic structure of stable materials is a long-standing challenge for the material design community.
Converse: A Tree-Based Modular Task-Oriented Dialogue System
At the core of the struggle is the need to script every single turn of interactions between the bot and the human user.
Label Efficient Regularization and Propagation for Graph Node Classification
In this paper, we propose a label efficient regularization and propagation (LERP) framework for graph node classification, and present an alternate optimization procedure for its solution.
Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks
Molecular dynamics (MD) simulation is essential for various scientific domains but computationally expensive.
A cloud platform for automating and sharing analysis of raw simulation data from high throughput polymer molecular dynamics simulations
We create a public analysis library at https://github. com/TRI-AMDD/htp_md to extract multiple properties from the raw data, using both expert designed functions and machine learning models.
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
We benchmark a collection of state-of-the-art (SOTA) ML FF models and illustrate, in particular, how the commonly benchmarked force accuracy is not well aligned with relevant simulation metrics.
SelFLoc: Selective Feature Fusion for Large-scale Point Cloud-based Place Recognition
To enhance message passing along particular axes, Stacked Asymmetric Convolution Block (SACB) is designed, which is one of the main contributions in this paper.
XGen-7B Technical Report
Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context.
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry.
Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems
Making big purchases requires consumers to research or consult a salesperson to gain domain expertise.
MatterGen: a generative model for inorganic materials design
We further introduce adapter modules to enable fine-tuning towards any given property constraints with a labeled dataset.
Optimizing Medication Decisions for Patients with Atrial Fibrillation through Path Development Network
This study introduces a machine learning algorithm that predicts whether patients with AF should be recommended anticoagulant therapy using 12-lead ECG data.
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint
Solving image inverse problems (e. g., super-resolution and inpainting) requires generating a high fidelity image that matches the given input (the low-resolution image or the masked image).
