Simulation– tag –
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Scilight Press Publishes Review on AI-Driven Rational Design of Solid-State Electrolytes: Paving the Way for Next-Gen Batteries
Scilight Press Unknown Overview Scilight Press published a review paper on AI's role in advancing the rational design of solid-state electrolytes (SSEs) for high energy density and safety in next-generation rechargeable batteries. The pa... -
IIT KANPUR Elucidates Material Structure-Property Relationships with Computational Materials Science: Leveraging DFT, MD/MC, and PFM
MSE | IIT KANPUR India Overview IIT KANPUR emphasizes computational materials science as a powerful toolkit for solving material-related problems, citing Density Functional Theory (DFT) at the electronic level, Molecular Dynamics (MD) an... -
Silicon Quantum Computing Achieves ‘Quantum Twins’ with 15,000 Qubit 2D Array on Pure Silicon
Falling Walls Science Summit Germany Overview Michelle Simmons' Silicon Quantum Computing (SQC) demonstrated the world's first 2D array quantum simulator with 15,000 precisely placed qubit registers on pure silicon. This breakthrough ena... -
Quantum Computing Reshapes Aircraft Design and Materials Science: Airbus and Rolls-Royce Embrace Technology for Unprecedented Simulation Results
TechJournal.uk UK Overview Quantum computing is fundamentally reshaping aircraft design and materials science, yielding superior results unattainable by classical software, particularly in engineering simulations. Airbus is exploring qua... -
IBM and Algorithmiq Demonstrate Quantum Advantage in Materials Simulation Beyond Classical Verification, Leveraging QESEM on Quantum Heron
IBM Newsroom USA Overview IBM and Algorithmiq demonstrated quantum advantage in simulating heterogeneous quantum materials using a new framework that establishes trust in quantum computations beyond classical verification. This breakthro... -
DOE Basic Energy Sciences Supports Software Development for New Materials and Chemical Processes Towards Next-Gen Exascale Computing
Department of Energy (DOE) USA Overview The U.S. Department of Energy's (DOE) Basic Energy Sciences (BES) division is supporting research teams developing software and databases to accelerate the design of new materials and chemical proc... -
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... -
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...
