Materials Screening

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (11 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
  1 Oct 2019 3 repositories listed Syntology ran 0 of 12 samples · 12 unverified
  Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost.
• Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors
  15 May 2019 2 repositories listed
  The possibilities for prediction in a realistic computational screening setting is investigated on a dataset of 5976 ABSe₃ selenides with very limited overlap with the OQMD training set.
• ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data
  15 Nov 2022 1 repository listed
  To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems.
• Accelerating Material Design with the Generative Toolkit for Scientific Discovery
  8 Jul 2022 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
• Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
  23 May 2021 1 repository listed
  In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems.
• Crystal Graph Neural Networks for Data Mining in Materials Science
  27 May 2019 1 repository listed
  This paper proposes crystal graph neural networks (CGNNs) that use no bond distances, and introduces a scale-invariant graph coordinator that makes up crystal graphs for the CGNN models to be trained on the dataset…

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.