Machine Learning– tag –
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UniFFBench Evaluates Universal Machine Learning Force Fields (UMLFFs) Against Experimental Measurements, Assessing Simulation Stability, Structural Fidelity, and Elastic Properties
arXiv International Overview A new benchmark framework, UniFFBench, has been released to evaluate Universal Machine Learning Force Fields (UMLFFs) against experimental measurements for diverse mineral systems. UniFFBench rigorously asses... -
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 ... -
MDPI Review Proposes Integrated Framework for ML-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry
MDPI International Overview A review published in MDPI deeply examines the role of machine learning (ML) in pharmaceutical chemistry, proposing a framework that integrates molecular design, synthetic feasibility, and structure-property-p... -
Tech Science Press Journal Features Paradigm Evolution in Materials Science Driven by AI, ML, and Generative Models
Tech Science Press International Overview The latest issue of Tech Science Press's journal 'CMC' (Vol. 88, No. 2, 2026) highlights how AI, machine learning (ML), and generative models are evolving the scientific paradigm of materials sci... -
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...
