AI-for-crystal-materials: models and benchmarks

学术   2024-12-23 15:53   中国台湾  

Here we have collected papers with the theme of "AI for crystalline materials" that have appeared at top machine learning conferences and journals (ICML, ICLR, NeurIPS, AAAI, NPJ, NC, etc.) in recent years. See https://arxiv.org/abs/2408.08044 for details.

Crystalline Material Physicochemical Property Prediction

MethodPaper
SchNetSchnet: A continuous-filter convolutional neural network for modeling quantum interactions (NeurIPS2017) [Paper][Code]
CGCNNCrystal graph convolutional neural networks for an accurate and interpretable prediction of material properties (Physical Review Letters, 2018) [Paper][Code]
MEGNETGraph networks as a universal machine learning framework for molecules and crystals (Chemistry of Materials, 2019) [Paper][Code]
GATGNNGraph convolutional neural networks with global attention for improved materials property prediction (Physical Chemistry Chemical Physics, 2020) [Paper][Code]
ALIGNNAtomistic line graph neural network for improved materials property predictions (npj Computational Materials, 2021) [Paper][Code]
ECNEquivariant networks for crystal structures (NeurIPS2022) [Paper][Code]
PotNetEfficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction (ICML2023) [Paper][Code]
CrysGNNCrysgnn: Distilling pre-trained knowledge to enhance property prediction for crystalline materials (AAAI2023) [Paper][Code]
ETGNNA general tensor prediction framework based on graph neural networks (The Journal of Physical Chemistry Letters, 2023) [Paper]
DTNetDielectric tensor prediction for inorganic materials using latent information from preferred potential (npj Computational Materials, 2024) [Paper][Code]
GMTNetA Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction (ICML2024) [Paper][Code]
CEGANNCEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment (npj Computational Materials, 2023) [Paper][Code]
ComFormerComplete and Efficient Graph Transformers for Crystal Material Property Prediction (ICLR2024) [Paper][Code]
CrystalformerCrystalformer: infinitely connected attention for periodic structure encoding (ICLR2024) [Paper][Code]
CrystalformerConformal Crystal Graph Transformer with Robust Encoding of Periodic Invariance (AAAI2024) [Paper]
E(3)NNDirect prediction of phonon density of states with Euclidean neural networks (Advanced Science, 2021) [Paper][Code]
DOSTransformerDensity of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer (NeurIPS2023) [Paper][Code]
MatformerPeriodic Graph Transformers for Crystal Material Property Prediction (NeurIPS2022) [Paper][Code]
CrysDiffA Diffusion-Based Pre-training Framework for Crystal Property Prediction (AAAI2024) [Paper]
MOFTransformerA multi-modal pre-training transformer for universal transfer learning in metal-organic frameworks (Nature Machine Intelligence, 2023) [Paper][Code]
Uni-MOFA comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks (Nature Communications, 2024) [Paper][Code]
SODNetLearning Superconductivity from Ordered and Disordered Material Structures (NeurIPS2024) [Paper][Code]

Crystalline Material Synthesis

MethodPaper
G-SchNetSymmetry-adapted generation of 3d point sets for the targeted discovery of molecules (NeurIPS2019) [Paper][Code]
CDVAECrystal Diffusion Variational Autoencoder for Periodic Material Generation (ICLR2022) [Paper][Code]
Con-CDVAECon-CDVAE: A method for the conditional generation of crystal structures (Computational Materials Today, 2024) [Paper][Code]
Cond-CDVAEDeep learning generative model for crystal structure prediction (Arxiv, 2024) [Paper][Code]
LCOMsLatent Conservative Objective Models for Data-Driven Crystal Structure Prediction (NeurIPS2023 Workshop) [Paper]
DiffCSPCrystal structure prediction by joint equivariant diffusion on lattices and fractional coordinates (NeurIPS2023) [Paper][Code]
DiffCSP-SCLearning Superconductivity from Ordered and Disordered Material Structures (NeurIPS2024) [Paper][Code]
EquiCSPEquivariant Diffusion for Crystal Structure Prediction (ICML2024) [Paper][Code]
GemsDiffVector Field Oriented Diffusion Model for Crystal Material Generation (AAAI2024) [Paper][Code]
SyMatTowards symmetry-aware generation of periodic materials (NeurIPS2023) [Paper][Code]
EMPNNEquivariant Message Passing Neural Network for Crystal Material Discovery (AAAI2023) [Paper][Code]
UniMatScalable Diffusion for Materials Generation (ICLR2024) [Paper][Code]
MatterGenMattergen: a generative model for inorganic materials design (Arxiv, 2023) [Paper]
PGCGMPhysics guided deep learning for generative design of crystal materials with symmetry constraints (npj Computational Materials, 2023) [Paper][Code]
CubicGANHigh-throughput discovery of novel cubic crystal materials using deep generative neural networks (Advanced Science, 2021) [Paper][Code]
PCVAEPCVAE: A Physics-informed Neural Network for Determining the Symmetry and Geometry of Crystals (IJCNN2023) [Paper][Code]
DiffCSP++Space Group Constrained Crystal Generation (ICLR2024) [Paper][Code]
FlowMMFlowMM: Generating Materials with Riemannian Flow Matching (ICML2024) [Paper][Code]
GovindarajanBehavioral Cloning for Crystal Design (ICLR2023 Workshop) [Paper][Code]
CHGFlowNetHierarchical GFlownet for Crystal Structure Generation (NeurIPS2023 Workshop) [Paper]
LM-CM,LM-ACLanguage models can generate molecules, materials, and protein binding sites directly in three dimensions as xyz, cif, and pdb files (Arxiv, 2023) [Paper][Code]
CrystaLLMCrystal structure generation with autoregressive large language modeling (Arxiv, 2023) [Paper][Code]
CrystalFormerSpace Group Informed Transformer for Crystalline Materials Generation (Arxiv, 2024) [Paper][Code]
SLI2CryAn invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning (Nature Communications, 2023) [Paper][Code]
GruverFine-Tuned Language Models Generate Stable Inorganic Materials as Text (ICLR2024) [Paper][Code]
FlowLLMFlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions (NeurIPS2024) [Paper][Code]
Mat2SeqInvariant Tokenization of Crystalline Materials for Language Model Enabled Generation (NeurIPS2024) [Paper]
GenMSGenerative Hierarchical Materials Search (NeurIPS2024) [Paper]
ChemReasonerCHEMREASONER: Heuristic Search over a Large Language Model’s Knowledge Space using Quantum-Chemical Feedback (ICML2024) [Paper]  [Code]

Aiding Characterization

MethodPaper
-Insightful classification of crystal structures using deep learning  (Nature Communications, 2018) [Paper]
-Advanced steel microstructural classification by deep learning methods  (Scientific Reports, 2018) [Paper]
-Neural network for nanoscience scanning electron microscope image recognition (Scientific Reports, 2017) [Paper]
-Deep Learning-Assisted Quantification of Atomic Dopants and Defects in 2D Materials (Advanced Science, 2021) [Paper]
-Classification of crystal structure using a convolutional neural network  (IUCrJ,2017) [Paper]
-Synthesis, optical imaging, and absorption spectroscopy data for 179072 metal oxides  (Scientific Data, 2019) [Paper]

Accelerating Theoretical Computation

MethodPaper
BPNNGeneralized neural-network representation of high-dimensional potential-energy surfaces  (Physical Review Letters, 2007) [Paper]
-Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons  (Physical Review Letters, 2010) [Paper]
NequIPE (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials  (Nature Communications, 2022) [Paper][Code]
CHGNetCHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling  (Nature Machine Intelligence, 2023) [Paper][Code]
CormorantCormorant: Covariant molecular neural networks  (NeurIPS2019) [Paper][Code]
MACEMACE: Higher order equivariant message passing neural networks for fast and accurate force fields  (NeurIPS2022) [Paper][Code]
DimeNetDirectional Message Passing for Molecular Graphs  (ICLR2020) [Paper][Code]
M3GNetA universal graph deep learning interatomic potential for the periodic table  (Nature Computational Science, 2022) [Paper][Code]
-Injecting domain knowledge from empirical interatomic potentials to neural networks for predicting material properties  (NeurIPS2022) [Paper][Code]
CHGNetCHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling  (Nature Machine Intelligence, 2023) [Paper][Code]
-Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations  (Transactions on Machine Learning Research, 2023) [Paper]
DeepRelaxScalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification  (Nature Communications, 2024) [Paper]   [Code]

Common Dataset and Platform

DatasetDescriptionURL
Materials ProjectMaterials Project encompasses over 120,000 materials, each accompanied by a comprehensive specification of its crystal structure and important physical properties.Materials Project
JARVIS-DFTJARVIS-DFT encompasses data for approximately 40,000 materials and includes around one million calculated properties.JARVIS-DFT
OQMDOQMD is a repository of thermodynamic and structural properties of inorganic materials, derived from high-throughput DFT calculations.OQMD
Perov-5Perov-5 is a specialized dataset of perovskite crystal materials, containing 18,928 different perovskite materials.Perov-5
Carbon-24Carbon-24 is a specialized dataset of carbon materials, containing over 10,000 different carbon structures.Carbon-24
Crystallography Open DatabaseCrystallography Open Database is a crystallography database that specializes in collecting and storing crystal structure information for inorganic compounds, small organic molecules, metal-organic compounds, and minerals.Crystallography Open Database
Raman Open DatabaseRaman Open Database is an open database that specializes in collecting and storing Raman spectroscopy data.Raman Open Database
Inorganic Crystal Structure DatabaseInorganic Crystal Structure Database is the world's largest database for completely identified inorganic crystal structures.Inorganic Crystal Structure Database
Open Catalyst ProjectThe goal of Open Catalyst Project is to utilize artificial intelligence to simulate and discover new catalysts for renewable energy storage.Open Catalyst Project
Python Materials GenomicsPython Materials Genomics is a robust, open-source Python library for materials analysis, offering a range of modules for handling crystal structures, band structures, phase diagrams, and material properties.Python Materials Genomics
MatBenchMatBench is a benchmark suite in the field of materials science, designed to evaluate and compare the performance of various ML models.MatBench
M² HubM² Hub is a machine learning toolkit for materials discovery research that covers the entire workflow.M² Hub
Phonon DOS DatasetPhonon DOS Dataset contains approximately 1,500 crystalline materials whose phonon DOS is calculated from DFPT.Phonon DOS Dataset
Carolina Materials DatabaseCMD primarily consists of ternary and quaternary materials generated by some AI methods.Carolina Materials Database
Alexandria DatabaseAlexandria Database includes a large quantity of hypothetical crystal structures generated by ML methods or other algorithmic methodologies.Alexandria Database
Materials Project Trajectory DatasetMPtrj contains 1,580,395 atomic configurations, corresponding energies, 7,944,833 magnetic moments, 49,295,660 forces, and 14,223,555 stress values.Materials Project Trajectory Dataset
Quantum MOFQMOF is a dataset of over 20K metal-organic frameworks and coordination polymers derived from DFT.Quantum MOF
Open Materials 2024OMat24 contains over 110 million DFT calculations focused on structural and compositional diversity.Open Materials 2024
SuperCon3DSuperCon3D contains 1,578 superconductor materials (includes 83 distinct elements), each with both Tc and crystal structure data.SuperCon3D


学术之友
\x26quot;学术之友\x26quot;旨在建立一个综合的学术交流平台。主要内容包括:分享科研资讯,总结学术干货,发布科研招聘等。让我们携起手来共同学习,一起进步!
 最新文章