Materials Informatics– category –
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Massive Discovery of 9,139 Low-Dimensional Materials from Materials Project via Universal Computational Strategy, Including 887 Exfoliable 2D Materials
ACS Chemistry of Materials USA Overview This research combined universal machine learning interatomic potentials (UMLIPs) with an advanced force constant (FC)-based dimensionality classification method to massively discover new low-dimen... -
Argonne National Laboratory Accelerates New Materials Discovery from Months to Days Using AI Agents
Argonne National Laboratory USA Overview Researchers at Argonne National Laboratory demonstrated an AI-driven system automating powerful simulation methods for predicting atomic interactions in materials. This system utilizes multiple AI... -
OAE Publishing Unveils AI-eChemist Autonomous Laboratory to Accelerate Electrocatalysis Research
OAE Publishing Inc. Unknown Overview OAE Publishing Inc. announced the development of the 'AI-eChemist Laboratory,' a self-driving lab (SDL) designed to dramatically accelerate electrocatalysis research. This platform integrates intellig... -
SLAC and Four Institutions Demonstrate AI-Enhanced Catalyst Development for Fuel Production, Highlighting Need for Standardization
Facebook (SLAC National Accelerator Laboratory) USA Overview Researchers from SLAC, Stanford, Penn State, and UC Santa Barbara collaborated to demonstrate AI's potential in developing catalysts for fuel production. However, inconsistenci... -
Transferable Machine Learning Interatomic Potential Accurately Predicts Thermodynamics, Structure, and Dynamics of Entangled Polymers from Oligomer Training
arXiv USA Overview This paper investigates machine learning interatomic potential (MLIP) development for polymers, identifying Atomic Cluster Expansion (ACE) as the most effective descriptor for polyethylene. It demonstrates that ACE pot... -
ACS Materials Au Publishes Comprehensive Tutorial on Machine Learning Tools for Electrocatalysis Simulations
ACS Materials Au USA Overview ACS Materials Au has released a tutorial on machine learning tools for electrocatalysis simulations, showcasing instruments like ML exchange-correlation functionals, Gaussian process optimizers, and ML inter... -
Deletion Method Outperforms Generative AI Approaches for Extracting Atomic Environments in MLIP-Driven MD Simulations
arXiv USA Overview A systematic analysis of optimal methods for extracting small atomic environments suitable for DFT calculations from large structures, a challenge in applying machine learning interatomic potentials (MLIPs) to large-sc... -
Atomic Representations from Pretrained MLIPs Prove Effective for Materials Generative Model Evaluation
arXiv USA Overview This paper demonstrates that atomic average features derived from pretrained machine learning interatomic potentials (MLIPs) like MACE are effective for evaluating the outputs of materials generative models. The resear... -
AI-Driven Battery Chemistry Simulators Evaluated for Adversarial Robustness: Output Validity vs. Chemical Sensitivity
OpenReview USA Overview The first systematic evaluation of adversarial robustness for AI-powered battery chemistry simulators, including MLIPs like MACE-MP-0 and CHGNet, revealed that physically constrained models maintain 90-92% physica... -
Quantum Machine Learning Interatomic Potential Achieves Enhanced Molecular Energy Prediction with Variational Quantum Algorithm
arXiv USA Overview This study applied quantum circuit learning to machine learning interatomic potentials (MLIPs), improving molecular dataset energy predictions. Using a quantum transfer learning architecture, the ANI model was retraine...
