2022 Accepted Works
Behind the Scenes
Hyperparameter Optimization of Graph Neural Networks for the OpenCatalyst Dataset: A Case Study
Human-in-the-Loop Approaches For Task Guidance In Manufacturing Settings
Geometric Considerations for Normalization Layers in Equivariant Neural Networks
Papers
A deep learning and data archaeology approach for mosquito repellent discovery
Conformer Search Using SE3-Transformers and Imitation Learning
AI-assisted chemical reaction impurity prediction and propagation
Neural Structure Fields with Application to Crystal Structure Autoencoders
Group SELFIES: A Robust Fragment-Based Molecular String Representation
A Generalized Framework for Microstructural Optimization using Neural Networks
A Data-efficient Multiobjective Machine Learning Method For 3D-printed Architected Materials Design
Transfer Learning Lithium and Electrolyte Potential Energy Surfaces from Pure and Hybrid DFT
Accelerating the Discovery of Rare Materials with Bounded Optimization Techniques
Deep Reinforcement Learning for Inverse Inorganic Materials Design
Robust design of semi-automated clustering models for 4D-STEM datasets
Actively Learning Costly Reward Functions for Reinforcement Learning
More trustworthy Bayesian optimization of materials properties by adding human into the loop
Differential top-k learning for template-based single-step retrosynthesis
PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design
Information Recovery via Matrix Completion for Piezoresponse Force Microscopy Data
A Self-driving Laboratory Optimizes A Scalable Process For Making Functional Coatings
Generative Design of Material Microstructures for Organic Solar Cells using Diffusion Models
Proposals
Software & Tutorials
Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
MolPAL: Software for Sample Efficient High-Throughput Virtual Screening