Fine-tuning– tag –
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Large Language Models Autonomously Generate Chemical Synthesis Protocols, Bridging ‘Lab-to-Fab’ Gap in Materials Manufacturing
MDPI Switzerland Overview This review focuses on AI-driven strategies to bridge the 'lab-to-fab' gap in materials synthesis and manufacturing, moving beyond property prediction to address manufacturability. It discusses the use of Large ... -
Universal MLIPs Face Generalization Challenges, ACS Study Highlights Need for Iterative Fine-Tuning of Material-Specific Models
ACS Publications USA Overview An ACS Publications paper highlights that while Universal Machine Learning Interatomic Potentials (MLIPs) are rapidly becoming general tools for atomic simulations, their role in quantitative material modeli... -
Fine-Tuned MACE Foundation Model Develops Transferable MLP Predicting Water Adsorption in 420 Al-MOFs with DFT Accuracy
ACS Publications USA Overview This paper successfully developed a transferable machine learning potential (MLP) by fine-tuning a MACE foundation model to accurately predict water adsorption behavior in Metal-Organic Frameworks (MOFs). Tr... -
MACE and SevenNet Data Efficiency Evaluated for Material-Specific MLIP Construction: Achieving Ab Initio Accuracy with 2,000 AIMD Configurations
arXiv International Overview The amount of ab initio molecular dynamics (AIMD) data required to fine-tune universal machine-learned interatomic potentials (MLIPs) for material-specific applications has been quantified. Research indicates... -
River AI Secures $1.1 Billion in Funding Across Seed and Series A Rounds from NVIDIA, AMD, and Others to Develop Personal AI Stack
Menlo Times USA Overview River AI, a startup developing a new stack for personal AI, has secured $1.1 billion in funding across Series Seed and Series A rounds, led by General Catalyst and AMP PBC, with strategic investment from NVIDIA a... -
Iterative Fine-Tuning Strategy for Universal Machine Learning Interatomic Potentials (uMLIPs) Yields Stable, High-Accuracy Models for Out-of-Domain Tasks
PubMed Overview Research on universal machine learning interatomic potentials (uMLIPs) demonstrates that "iterative fine-tuning" effectively generates stable molecular dynamics simulations and high-accuracy models for out-of-domain tasks... -
Unlocking Precision: Iterative Fine-Tuning Overcomes Bias in Universal MLIPs for Enhanced Material Simulations
Unknown Source Unknown Overview Universal Machine Learning Interatomic Potentials (uMLIPs) promise broad applicability across the periodic table, yet accurate out-of-domain predictions demand specialized fine-tuning. This study reveals t... -
Open-Source LLMs Like Llama 3 Achieve Performance Parity with Proprietary Models in July 2026, Gaining Traction for Customizability and Flexibility
LLM Stats Unknown Overview In July 2026, open-source Large Language Models (LLMs) demonstrated remarkable progress, with models such as Llama 3, Mistral, Qwen, and DeepSeek matching or exceeding proprietary alternatives across numerous b... -
Meta FAIR and Stanford Researchers Successfully Fine-Tune UMA Model for High-Precision Simulation of WS2 Oxygen Plasma Interactions
arXiv USA Overview Researchers from Meta FAIR and Stanford University successfully fine-tuned the UMA universal machine-learned interatomic potential (MLIP) model specifically for oxygen plasma interactions with WS2. This study addresses... -
Active Learning for MACE-MP-0 Foundation MLFFs Achieves Full Data Accuracy with Significantly Fewer Labeled Training Examples
arXiv International Overview This study demonstrates an innovative active learning strategy for fine-tuning machine-learning force fields (MLFFs), specifically focusing on foundation models like MACE-MP-0, to achieve full-data accuracy w...
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