VASP
PulseAugur coverage of VASP — every cluster mentioning VASP across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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CoLVR framework boosts latent visual reasoning in MLLMs
Researchers have developed CoLVR, a novel framework designed to enhance exploratory reasoning in Multimodal Large Language Models (MLLMs). CoLVR utilizes a contrastive training approach to expand the semantic latent spa…
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New dataset SoLiD26 advances MLIPs for solid-liquid interface simulations
Researchers have introduced SoLiD26, a new dataset designed to train machine-learned interatomic potentials (MLIPs) for simulating solid-liquid interfaces. This dataset comprises 15.4 million first-principles structures…
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LatticeMind framework improves reliability of AI-assisted scientific computing
Researchers have developed LatticeMind, a novel agentic framework designed to enhance the reliability of complex scientific computing workflows, particularly in density functional theory (DFT). This framework uses natur…
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Small language model fine-tuned for VASP INCAR generation outperforms larger LLMs
Researchers have developed INCAR-SLM, a fine-tuned small language model (SLM) capable of reliably generating VASP INCAR files from natural language requests. This model, when paired with a post-processor called VASPGuar…
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New benchmark evaluates LLMs on scientific configuration for VASP calculations
Researchers have introduced INCARBench, a new benchmark designed to evaluate the capabilities of large language models (LLMs) in configuring scientific calculations for the Vienna Ab initio Simulation Package (VASP). Th…
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Microsoft Research enhances Skala deep-learning DFT with improved accuracy and broader access
Microsoft Research has released Skala 1.1, an updated version of its deep-learning density functional theory (DFT) approach. This new version is trained on 2.5 times more data than its predecessor, leading to significan…
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New agent system automates complex atomistic simulations
Researchers have developed an agent-based system, integrated into the URSA framework, designed to automate complex, multi-step atomistic simulations. This system can autonomously select interatomic potentials, construct…
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EndoCoT framework enhances diffusion models' reasoning with MLLMs
Researchers have introduced EndoCoT, a new framework designed to enhance the reasoning capabilities of diffusion models when integrated with Multimodal Large Language Models (MLLMs). The framework addresses limitations …
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AdsMind system uses AI agents to accelerate catalyst discovery
Researchers have developed AdsMind, a novel multi-agent system designed to accelerate the discovery of adsorption configurations on heterogeneous catalyst surfaces. This closed-loop framework integrates machine learning…