CAMD
Materials optimizer
Software framework for designing and executing sequential learning experiments in materials discovery
Agent-based sequential learning software for materials discovery
60 stars
9 watching
28 forks
Language: Python
last commit: 10 months ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
tri-amdd/piro | A software tool that plans synthesis pathways for inorganic materials based on thermodynamic conditions and precursor libraries. | 21 |
bamresearch/webslamd | An AI-powered web application designed to facilitate materials discovery and optimization in sustainable concrete and binder formulations | 21 |
tri-amdd/mpet | A software framework simulating battery dynamics using a multiscale approach to capture the coupled behavior of electrolyte and active material within electrodes. | 23 |
materialsvirtuallab/maml | A toolkit for machine learning in materials science, enabling the development of predictive models and simulations. | 369 |
materialsintelligence/mat2vec | Unsupervised word embeddings capture latent knowledge from materials science literature | 619 |
drorlab/atom3d | Enables machine learning on three-dimensional molecular structure by providing tools and datasets for working with 3D molecular data | 303 |
janosh/pymatviz | A toolkit for creating visualizations and analyzing data in materials science | 173 |
rahulkidambi/accsgd | An implementation of the Accelerated SGD algorithm in PyTorch | 215 |
google-deepmind/meltingpot | Assesses generalization of multi-agent reinforcement learning algorithms to novel social situations | 620 |
comprhys/aviary | A framework for building and deploying machine learning models for materials discovery | 48 |
janosh/matbench-discovery | An evaluation framework for machine learning models simulating high-throughput materials discovery. | 107 |
multimodal-art-projection/omnibench | Evaluates and benchmarks multimodal language models' ability to process visual, acoustic, and textual inputs simultaneously. | 14 |
ns-phd-research/haccs | Improves federated learning by accounting for device and data differences during training | 4 |
mhlee0903/multi_channels_pinn | Investigating neural networks for drug discovery using multiple chemical descriptors. | 3 |
materialsproject/matbench | Provides tools and resources for testing machine learning performance on materials science data | 122 |