New Technology– category –
-
Self-Driving Labs Optimize Nanomaterial Synthesis and Property Prediction, Drastically Cutting Development Lead Times
nano-matter.com International Overview Self-driving labs (SDLs) function as closed-loop systems, combining machine learning algorithms with automated hardware to autonomously design, execute, analyze, and refine nanomaterial experiments ... -
Equivariant Graph Neural Network Interatomic Potentials Accelerate Nanomaterials Simulation by 100x
nano-matter.com International Overview Equivariant Graph Neural Network (GNN) interatomic potentials are accelerating nanomaterials research by predicting atomic forces and energies with DFT-comparable accuracy, while reducing computatio... -
U.S. Department of Energy Unveils Strategy to Accelerate Scientific Discovery and Economic Competitiveness with AI-Driven Autonomous Laboratories
Department of Energy (DOE) USA Overview The U.S. Department of Energy (DOE) announced a critical strategy for AI-driven autonomous laboratories to directly propel American scientific leadership and economic competitiveness. These labs wi... -
Tohoku University Proposes New AI-Integrated Autonomous Framework for Polymer Materials Discovery
EurekAlert! (Advanced Institute for Materials Research (AIMR), Tohoku University) Japan Overview A research team at Tohoku University has proposed a novel framework integrating AI and autonomous laboratories to overcome key bottlenecks i... -
GRACE-OFF Achieves High-Precision Machine-Learned Interatomic Potentials for Organic Liquids via GRACE Architecture
Journal of Chemical Theory and Computation | ACS Publications USA Overview This study introduces GRACE-OFF, a machine-learned interatomic potential (MLIP) built on the Graph Atomic Cluster Expansion (GRACE) neural network architecture, d... -
LAMMPS Integrates DeePMD-kit, NequIP for Quantum-Accurate ML Potentials, Enhancing Molecular Dynamics Simulations
LAMMPS Molecular Dynamics Simulator USA Overview LAMMPS, a widely used open-source molecular dynamics simulator, now supports cross-code frameworks and interoperability tools for machine learning interatomic potentials (MLPs), including ... -
Self-Driving Labs Accelerate Material Discovery with Closed-Loop AI and Robotics, Reducing Weeks to Days
Architect Magazine USA Overview A new paradigm of Self-Driving Labs (SDLs) is automating the material discovery process by combining robotic experimentation with closed-loop AI feedback. Facilities like Lawrence Berkeley National Laborat... -
Predictive First-Principles Simulations and AI Accelerate Co-Design of Next-Gen Energy-Efficient AI Hardware
Applied Physics Letters | AIP Publishing USA Overview Predictive first-principles simulations are playing a crucial role in the co-design of next-generation energy-efficient AI system hardware. Machine learning-based material representat... -
ORNL Develops AI-Guided Autonomous System for Inverse Design of Functional Materials from Single Molecules
Facebook (Oak Ridge National Laboratory) USA Overview Researchers at Oak Ridge National Laboratory (ORNL) have developed an AI-guided system that autonomously arranges individual molecules to construct functional materials. This system a... -
LLM Agents Unleash Autonomous Materials Discovery and Design
Unknown International Overview Large language model (LLM)-powered agents are now autonomously executing complex scientific workflows in computational materials science, significantly enhancing research efficiency and reliability. These a...
