Simulation– tag –
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Ansys: Inverse Design Revolutionizes Materials R&D, Automating Design from Desired Performance to Drastically Cut Time and Cost
Ansys USA Overview Ansys details how inverse design, a computational approach, reverses traditional trial-and-error, automating material design from desired specifications. Powered by machine learning and deep learning optimization, this... -
Energy & Environmental Science: AI Optimizes Li-Ion Battery Electrolytes via Data-Driven Methods, Accelerating Discovery of Novel Solvents, Salts, and Solid Electrolytes
Energy & Environmental Science UK Overview A review in Energy & Environmental Science highlights AI's transformative impact on Li-ion battery electrolyte research, leveraging data-driven prediction, mechanistic analysis, and intellig... -
AIP Publishing Reveals New Vibrational Entropy Metric Dramatically Improves Empirical Interatomic Potential Accuracy by 80%, Revolutionizing MLIP Validation
AIP Publishing USA Overview A new study published by AIP Publishing introduces a novel method for developing and validating empirical interatomic potentials, achieving an 80% error reduction in dynamic properties compared to existing MOF... -
Argonne National Lab’s AI Discovers N2116 Electrolyte in 80 Hours, Enabling Faster Charging, Higher Energy Density Batteries
Argonne National Laboratory USA Overview Argonne National Laboratory researchers have used an AI-driven method to drastically accelerate materials discovery for battery development. The AI identified 18 stable material candidates from 50... -
AI-Driven Hyperdynamics Method Revolutionizes Atomic Event Simulation for Material Durability and Aging Research
Nature Communications Unknown Overview A novel machine learning-based hyperdynamics method dramatically accelerates atomic motion simulations, transforming computational materials discovery. This automated approach for developing interat... -
Lawrence Berkeley National Laboratory Unveils Quantum Accuracy Scale Transition from DFT to MLIP and Plans for Two Autonomous Labs at 2026 Annual Meeting
Lawrence Berkeley National Laboratory USA Overview Lawrence Berkeley National Laboratory's Molecular Foundry highlighted the quantum accuracy scale transition from DFT to MLIP in a keynote at its 2026 Annual Meeting. They also announced ... -
DeepH-pack Unites Ab Initio Calculations and Deep Learning to Accelerate AI-Driven Electronic Structure Modeling
Facebook (reposting about DeepH-pack) USA Overview DeepH-pack has been introduced as a general-purpose neural network package that combines ab initio calculations with deep learning to accelerate electronic structure modeling. This tool ... -
FSU Alumnus Launches AI Company FindWhatMatters.ai to Revolutionize Physics Research with AI-Driven Data Analysis and Material Discovery
Facebook USA Overview An alumnus of Florida State University (FSU) has launched FindWhatMatters.ai, an AI company aiming to accelerate scientific discovery in physics research through AI integration. The company focuses on maximizing AI'... -
AI Revolutionizes Digital Discovery of MOFs: Integrating Computational Modeling and High-Throughput Screening to Slash Development Times
ACS Publications USA Overview Artificial intelligence (AI) and machine learning are revolutionizing the discovery and optimization of metal-organic framework (MOF)-based multifunctional materials. AI enables rapid exploration of MOFs' va... -
Accelerated Understanding to Revolutionize Chip Design, Robotics, and Energy with Physics-Based AI Models
Facebook USA Overview Accelerated Understanding, a new startup, has announced the development of physics-based AI models capable of processing massive datasets across space and time. These models are planned for groundbreaking applicatio...
