Quantum Chemistry– tag –
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Hessian-Based Molecular Conformation Augmentation Improves Scalability and Efficiency of Machine Learning Interatomic Potentials
arXiv (Preprint Server) International Overview This preprint proposes two Hessian-derived data augmentation schemes, isotropic Gaussian displacement (UniAug) and normal mode-weighted displacement (ModeAug), for machine-learning interatom... -
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... -
Mira Proposes “Fifth Paradigm” of AI in Materials Science: Integrating MatterGen, A-Lab, GNoME for Autonomous Discovery
Mira USA Overview Mira has proposed the "Fifth Paradigm" of AI in materials science, asserting that the integration of generative design (MatterGen), quantum chemistry simulations, and closed-loop laboratories (A-Lab) will accelerate aut... -
Chemistry World Reports CuspAI Forms ‘AI Materials Foundry’ with Meta & Nvidia to Accelerate New Functional Material Discovery with AI
Chemistry World UK Overview Chemistry World reports that computational chemistry company CuspAI has formed the "AI Materials Foundry," a network of 45 organizations including Meta and Nvidia, to discover new functional materials using AI... -
ACS Omega: Generative Models & MD Simulations Discover Electrolyte for High-Voltage, Low-Temp Li-ion Batteries
ACS Omega USA Overview Researchers deployed the generative machine learning model G-SchNet, trained on the QM9-GCDQE database, to accelerate electrolyte discovery for lithium-ion batteries operating at high voltages and low temperatures.... -
NVIDIA and Applied Materials Propel Semiconductor Innovation with GPU-Accelerated AI, Achieving Up to 55x Speedup
NVIDIA Technical Blog USA Overview NVIDIA and Applied Materials have announced a strategic partnership to revolutionize semiconductor innovation through the integration of AI and GPU-accelerated simulations. This collaboration leverages ... -
Scalable Isotropic Message Passing ML Accelerates Electronic Structure and Atomistic Property Modeling by Efficiently Describing Non-Local Effects
The Journal of Chemical Physics | AIP Publishing USA Overview A new study demonstrates a scalable machine learning approach for isotropic message passing, leveraging continuous products of external potentials to model electronic structur... -
GPUMD 4.0 Achieves High-Performance Versatile Materials Simulations with Integrated Machine-Learned Potentials
Materials Genome Engineering Advances Global Overview The high-performance molecular dynamics package GPUMD 4.0 has been released, integrating advanced machine-learning potentials (MLPs) based on the NeuroEvolution Potential (NEP) framew... -
D-Wave Unveils Superconducting Gate-Model Quantum Computing Platform to Accelerate Quantum Chemistry and Materials Science
D-Wave Canada Overview D-Wave announced the development of a gate-model quantum computing platform to complement its annealing systems, aiming to expand the range of complex problems solvable. Based on a superconducting architecture, thi... -
Argonne National Laboratory Unveils AI-Driven ‘ChemGraph’ Framework, Accelerating Materials Research with LLMs
Argonne National Laboratory USA Overview Argonne National Laboratory has announced 'ChemGraph,' an open-source, AI-driven framework that automates the setup, execution, and analysis of computational chemistry and materials science simula...
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