Papers Formation Energy
“Formation Energy” 태그가 달린 논문 59편 · 필터 해제
Evolutionary Extreme Learning Machine of ab-initio Energy Landscapes for Crystal Structure Prediction using Manta Ray Optimization with Levy Flight
The Manta Ray Foraging Optimization algorithm (MRFO) has proven to be a powerful heuristic strategy in the optimal solution of a large number of engineering problems. In this paper, an improvement of MRFO with Levy Fligh…
Formation EnergyInterpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks
Characterizing crystalline energy landscapes is essential to predicting thermodynamic stability, electronic structure, and functional behavior. While machine learning (ML) enables rapid property predictions, the "black-b…
Formation EnergyMACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry
Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on local atomic descriptors that cannot captur…
Computational EfficiencyFormation EnergyDrug DiscoveryWhen Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem
Efficiently and meaningfully estimating prediction uncertainty is important for exploration in active learning campaigns in materials discovery, where samples with high uncertainty are interpreted as containing informati…
Formation EnergyActive LearningExtended Factorization Machine Annealing for Rapid Discovery of Transparent Conducting Materials
The development of novel transparent conducting materials (TCMs) is essential for enhancing the performance and reducing the cost of next-generation devices such as solar cells and displays. In this research, we focus on…
Formation EnergyAdvancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction
Accurately predicting magnetic behavior across diverse materials systems remains a longstanding challenge due to the complex interplay of structural and electronic factors and is pivotal for the accelerated discovery and…
Feature EngineeringFormation EnergyAutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use
Machine learning-based interatomic potentials and force fields depend critically on accurate atomic structures, yet such data are scarce due to the limited availability of experimentally resolved crystals. Although atomi…
DenoisingFormation EnergyProperty PredictionInvDesFlow-AL: Active Learning-based Workflow for Inverse Design of Functional Materials
Developing inverse design methods for functional materials with specific properties is critical to advancing fields like renewable energy, catalysis, energy storage, and carbon capture. Generative models based on diffusi…
Active LearningFormation EnergyMatMMFuse: Multi-Modal Fusion model for Material Property Prediction
The recent progress of using graph based encoding of crystal structures for high throughput material property prediction has been quite successful. However, using a single modality model prevents us from exploiting the a…
Band GapFormation EnergyProperty PredictionThe Vendiscope: An Algorithmic Microscope For Data Collections
The evolution of microscopy, beginning with its invention in the late 16th century, has continuously enhanced our ability to explore and understand the microscopic world, enabling increasingly detailed observations of st…
DiversityFormation EnergyMemorizationA Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
In diffraction-based crystal structure analysis, thermal ellipsoids, quantified via Anisotropic Displacement Parameters (ADPs), are critical yet challenging to determine. ADPs capture atomic vibrations, reflecting therma…
ADP PredictionBand GapData AugmentationFormation Energy+2SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction
Material discovery is a cornerstone of modern science, driving advancements in diverse disciplines from biomedical technology to climate solutions. Predicting synthesizability, a critical factor in realizing novel materi…
Computational EfficiencyFormation EnergyMaterial Property Prediction with Element Attribute Knowledge Graphs and Multimodal Representation Learning
Machine learning has become a crucial tool for predicting the properties of crystalline materials. However, existing methods primarily represent material information by constructing multi-edge graphs of crystal structure…
AttributeFormation EnergyKnowledge GraphsProperty Prediction+1Unleashing the power of novel conditional generative approaches for new materials discovery
For a very long time, computational approaches to the design of new materials have relied on an iterative process of finding a candidate material and modeling its properties. AI has played a crucial role in this regard, …
Formation EnergyGenerative Hierarchical Materials Search
Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials science, and in particular to crystal s…
Formation EnergyGraph Neural NetworkCrysAtom: Distributed Representation of Atoms for Crystal Property Prediction
Application of artificial intelligence (AI) has been ubiquitous in the growth of research in the areas of basic sciences. Frequent use of machine learning (ML) and deep learning (DL) based methodologies by researchers ha…
Formation EnergyGraph Neural NetworkMolecular Property PredictionPrediction+2Enhancing material property prediction with ensemble deep graph convolutional networks
Machine learning (ML) models have emerged as powerful tools for accelerating materials discovery and design by enabling accurate predictions of properties from compositional and structural data. These capabilities are vi…
Band GapFormation EnergyGraph Neural NetworkPrediction+1Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics
The scarcity of property labels remains a key challenge in materials informatics, whereas materials data without property labels are abundant in comparison. By pretraining supervised property prediction models on self-su…
Band GapFormation EnergyPredictionProperty Prediction+3Crystal-LSBO: Automated Design of De Novo Crystals with Latent Space Bayesian Optimization
Generative modeling of crystal structures is significantly challenged by the complexity of input data, which constrains the ability of these models to explore and discover novel crystals. This complexity often confines d…
Bayesian OptimizationFormation EnergyPushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
Materials with high-dielectric constant easily polarize under external electric fields, allowing them to perform essential functions in many modern electronic devices. Their practical utility is determined by two conflic…
Band GapDielectric ConstantFormation Energy