Density Functional Theory– tag –
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Unconstrained MLIPs Scaled to Large Datasets Outperform Constrained Models in Static Simulations for Accuracy and Speed
ResearchGate International Overview Unconstrained Machine Learning Interatomic Potentials (MLIPs), scaled to large datasets, have demonstrated superior performance in both accuracy and speed for static simulation workflows like geometric... -
Chemistry World Reports AI Agents and MLIPs Accelerating Catalyst Discovery from Simulation to Scale-Up
Chemistry World UK Overview Chemistry World reported on the forefront of AI agents and Machine Learning Interatomic Potentials (MLIPs) accelerating the catalyst discovery process from simulation to scale-up. MLIPs replace computationally... -
ML SNAP Outperforms MEAM in Liquid (U,Zr) Thermophysical and Structural Predictions, Unveiling Viscosity Anomalies and Icosahedral Short-Range Order
PubMed International Overview In predicting the thermophysical and structural properties of liquid Uranium-Zirconium (U,Zr) mixtures, the machine learning-based Spectral Neighbor Analysis Potential (SNAP) demonstrated superior predictive... -
Virial-Matching in ML Coarse-Grained Potential for Multilayer hBN Addresses Mesoscale Problems in 2D Materials
The Journal of Physical Chemistry C - ACS Publications International Overview A bottom-up virial-matching coarse-graining method, based on machine learning potentials, has been developed for multi-component 2D materials like multilayer h... -
MLIPs Tackle Electronic Entropy Challenge: Charge State Embedding Boosts Battery Material Prediction Accuracy
arXiv International Overview Traditional Machine Learning Interatomic Potentials (MLIPs) have struggled to capture electronic entropy in mixed-valence materials, leading to prediction inaccuracies. To address this, a new approach embeds ... -
DP-EVA Framework Maximizes Pre-Trained Knowledge of Large Atomistic Models to Develop Data-Efficient MLIPs
Clean Energy | Oxford Academic International Overview A new data-efficient fine-tuning framework, DP-EVA, has been introduced, enabling the development of domain-specific Machine Learning Interatomic Potentials (MLIPs) by maximizing the ... -
Brown and Michigan Universities Stabilize Previously Hidden Intermediate Phase of Matter in Metals, Offering New Quantum Computing Insights
SciTechDaily USA Overview Researchers at Brown University and the University of Michigan have successfully stabilized a previously elusive intermediate phase of matter existing between two common metallic crystal arrangements, after deca... -
ACS Paper Introduces Chemistry-Informed ML Framework for High-Accuracy Prediction of Osmabenzene Complex Structural Properties
ACS Publications International Overview Research published in ACS Publications developed a chemistry-informed machine learning (ML) framework for predicting the structural non-planarity of osmabenzene complexes with high accuracy. Utiliz... -
InvDesMobility Framework Accelerates Materials Discovery with Reliability-Gated First-Principles Feedback Based on Carrier Mobility
arXiv International Overview InvDesMobility is a closed-loop inverse materials design framework utilizing reliability-gated first-principles feedback for carrier mobility. This framework integrates automated DFT, generative structural pr... -
LLM-Based Autonomous Agent PhyNex Achieves Automated Discovery in Computational Physics, Including Semiconductor Dielectric Spectra Prediction
arXiv International Overview An autonomous agent, PhyNex, has been developed to accelerate scientific discovery in computational physics. By combining LLM-guided search with domain-specific computational tools, PhyNex systematically expl...
