MatterGen
PulseAugur coverage of MatterGen — every cluster mentioning MatterGen across labs, papers, and developer communities, ranked by signal.
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New framework AtomBench standardizes AI model evaluation for crystal reconstruction
Researchers have developed AtomBench, a new framework for evaluating generative crystal reconstruction models, particularly for conventional superconductors. The framework allows for standardized comparisons by ensuring…
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New AI workflow speeds up materials discovery with surrogate-guided generation
Researchers have developed a new workflow for materials design that uses a Gaussian process surrogate to efficiently guide generative models. This approach significantly reduces the need for costly property evaluations …
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New Benchmark Reveals AI Models Struggle with Crystal Stability
Researchers have introduced PhononBench, a new benchmark designed to evaluate the dynamical stability of AI-generated crystalline materials. This benchmark utilizes the MatterSim interatomic potential for efficient phon…
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New benchmark RADII measures generative model limits in materials science
Researchers have developed a new benchmark called RADII to systematically measure the extrapolation frontier of graph generative models used in materials science. This benchmark evaluates how reliably these models gener…
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AI interprets protein models to detect biological risks
Researchers have developed a new method called SAEBER, utilizing Sparse Autoencoders (SAEs) to analyze protein design models like RFDiffusion3 and RoseTTAFold3. This technique identifies features within the models that …