Papers Materials Screening
“Materials Screening” 태그가 달린 논문 11편 · 필터 해제
Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials
Data science and artificial intelligence are playing an increasingly important role in the physical sciences. Unfortunately, in the field of energetic materials data scarcity limits the accuracy and even applicability of…
Materials ScreeningET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data
Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied …
Active LearningDiversityMaterials ScreeningAccelerating Material Design with the Generative Toolkit for Scientific Discovery
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed …
Drug DiscoveryMaterials Screeningscientific discoveryPhysics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping
Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific…
Inductive BiasMaterials ScreeningPredicting Lattice Phonon Vibrational Frequencies Using Deep Graph Neural Networks
Lattice vibration frequencies are related to many important materials properties such as thermal and electrical conductivity as well as superconductivity. However, computational calculation of vibration frequencies using…
Graph Neural NetworkMaterials ScreeningMaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art
The availability and easy access of large scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for materials property prediction, structure pr…
Band GapMaterials ScreeningPredictionProperty PredictionModel Uncertainty and Correctability for Directed Graphical Models
Probabilistic graphical models are a fundamental tool in probabilistic modeling, machine learning and artificial intelligence. They allow us to integrate in a natural way expert knowledge, physical modeling, heterogeneou…
BIG-bench Machine LearningMaterials ScreeningmodelUncertainty QuantificationBenchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. Ho…
Active LearningBayesian OptimisationBayesian OptimizationBenchmarking+3Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a key stumbling block. Current methods typi…
BIG-bench Machine LearningMaterials ScreeningRepresentation LearningCrystal Graph Neural Networks for Data Mining in Materials Science
Machine learning methods have been employed for materials prediction in various ways. It has recently been proposed that a crystalline material is represented by a multigraph called a crystal graph. Convolutional neural …
Band GapFormation EnergyGraph Neural NetworkMaterials Screening+1Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors
Computational materials screening studies require fast calculation of the properties of thousands of materials. The calculations are often performed with Density Functional Theory (DFT), but the necessary computer time s…
BIG-bench Machine LearningFormation EnergyMaterials ScreeningPosition+2