New Technology– category –
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AI-Driven Material Discovery Workflows Accelerate Nanotechnology R&D, CuspAI Demonstrates Autonomous Synthesis Pipeline
nano-matter.com International Overview AI-driven material discovery workflows are dramatically accelerating nanotechnology R&D, replacing traditional trial-and-error processes with predictive intelligence and significantly shortening... -
Princeton University and Collaborators Awarded $19.5 Million by NSF to Establish Open-Access Autonomous Chemistry Laboratory
UCLA Chemistry USA Overview Professor Abigail Doyle of Princeton University and collaborators have secured a $19.5 million National Science Foundation (NSF) award to establish an open-access, AI-enabled autonomous chemistry laboratory. T... -
U.S. Department of Energy Leverages AI and Supercomputing for Critical Mineral Recovery from Lithium-Ion Battery Recycling
GovCIO USA Overview The U.S. Department of Energy (DOE) is advancing a project at SLAC National Accelerator Laboratory, led by Stanford University, utilizing an AI agent team to efficiently recover critical minerals like nickel and cobal... -
Autonomous Materials Discovery Labs Slash Development Time from Decades to Weeks through Closed-Loop ML and Robotic Experimentation
Scifiniti International Overview The paper 'AI-Guided Self-Driving Laboratories for Advanced Materials Discovery' proposes that autonomous materials discovery platforms, integrating closed-loop machine learning and robotic experimentatio... -
Physics-Informed Pairwise Charge Transfer GGNN Framework Achieves Fast, Universal Prediction of Dynamic Properties Under Electric Fields
ACS Publications USA Overview An ACS Publications paper reports a novel framework combining physics-informed Pairwise Charge Transfer (PQT) theory with a Generalized Global Neural Network (GGNN) for rapid and accurate prediction of dynam... -
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... -
Integrated Active Learning and Knowledge Distillation in MLMD Achieves 1/3 Data Efficiency with MACE Model, Outperforming DeePMD
ACS Publications USA Overview This study developed data-efficient and fast Machine Learning Molecular Dynamics (MLMD) interatomic potentials (MLIPs) by combining DeePMD and MACE models within an active learning and knowledge distillation... -
Machine Learning Interatomic Potentials (MLIPs) Accelerate Catalyst Discovery, Achieving DFT-Level Accuracy at Low Cost
nano-matter.com International Overview As of August 2026, Machine Learning Interatomic Potentials (MLIPs) have become a mainstream technology in computational catalyst research, enabling rapid screening of catalyst candidates, efficient ... -
Review Paper on AI-Driven Inverse Design of Functional Materials Forecasts Broad Applications from Energy Catalysis to Structural Materials
Scientific Research Publishing (SCIRP) International Overview A Scientific Research Publishing (SCIRP) paper provides a systematic review of progress and challenges in 'AI-enabled reverse design' of functional materials. The study analyz...
