Materials Informatics– category –
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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 ... -
Sparsity-Promoting Fine-Tuning Enhances Domain Adaptability of Pre-Trained Equivariant Materials Foundation Models
arXiv International Overview A sparsity-promoting fine-tuning method has been proposed for robust and interpretable adaptation of pre-trained equivariant materials foundation models (MLIPs) to domain-specific applications. This technique... -
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... -
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... -
PhononBench Unveils Large-Scale Benchmark for Evaluating Dynamic Stability of AI-Generated Crystal Structures
arXiv International Overview PhononBench, a large-scale phonon-based benchmark, has been released to evaluate the dynamic stability of crystal structures generated by AI models. This benchmark highlights the limitations of current crysta... -
XRDiff: A New Diffusion Model for Crystal Structure Prediction from Powder X-Ray Diffraction Data
arXiv International Overview A new diffusion model, XRDiff, has been developed to predict crystal structures directly from powder X-ray diffraction (PXRD) data. XRDiff functions with partial chemical composition input and learns the spec...
