Formation Energy
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Benchmarks
QM9
Materials Project
JARVIS-DFT
3BPA
Acetylacetone
Aspirin
Ethanol
GeTe
LiPS
LiPS20
Naphthalene
OQM9HK
OQMD v1.2
Salicylic Acid
Most implemented
Neural Message Passing for Quantum Chemistry
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Directional Message Passing for Molecular Graphs
Papers
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 Energy