Fetching the paper…
Reading the bibliography…
Crystalline materials, with symmetrical and periodic structures, exhibit a wide spectrum of properties and have been widely used in numerous applications across electronics, energy, and beyond.
International tables for crystallography , vol. 1 (Reidel Dordrecht, 1983)
Hahn, T., Shmueli, U. & Arthur, J. W · 1983
Earlier work this paper cites.
Wyckoff positions used for the classification of bravais classes of modulated crystals
Janner, A., Janssen, T. & De Wolff, P · 1983
Earlier work this paper cites.
Nomenclature and generation of three-periodic nets: the vector method
Chung, S. J., Hahn, T. & Klee, W · 1984
Earlier work this paper cites.
Crystallographic databases
Bergerhoff, G., Brown, I., Allen, F. et al · 1987
Earlier work this paper cites.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
Weininger, D · 1988
Earlier work this paper cites.
The crystallographic information file (cif): a new standard archive file for crystallography
Hall, S. R., Allen, F. H. & Brown, I. D · 1991
Earlier work this paper cites.
Experimental design for combinatorial and high throughput materials development (Citeseer, 2003)
Cawse, J. N · 2003
Earlier work this paper cites.
Generalized neural-network representation of high-dimensional potential-energy surfaces
Behler, J. & Parrinello, M · 2007
Earlier work this paper cites.
Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G · 2010
Earlier work this paper cites.
Euclidian embeddings of periodic nets: definition of a topologically induced complete set of geometric descriptors for crystal structures
Eon, J.-G · 2011
Earlier work this paper cites.
New cubic perovskites for one-and two-photon water splitting using the computational materials repository
Castelli, I. E. et al · 2012
Earlier work this paper cites.
Commentary: The materials project: A materials genome approach to accelerating materials innovation
Jain, A. et al · 2013
Earlier work this paper cites.
Materials design and discovery with high-throughput density functional theory: the open quantum materials database (oqmd)
Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C · 2013
Earlier work this paper cites.
Python materials genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P. et al · 2013
Earlier work this paper cites.
The open quantum materials database (oqmd): assessing the accuracy of dft formation energies
Kirklin, S. et al · 2015
Earlier work this paper cites.
Computational predictions of energy materials using density functional theory
Jain, A., Shin, Y. & Persson, K. A · 2016
Earlier work this paper cites.
Accurate tight-binding hamiltonians for two-dimensional and layered materials
Agapito, L. A. et al · 2016
Earlier work this paper cites.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K. et al · 2017
Earlier work this paper cites.
Classification of crystal structure using a convolutional neural network
Park, W. B. et al · 2017
Earlier work this paper cites.
Machine learning of accurate energy-conserving molecular force fields
Chmiela, S. et al · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E · 2017
Earlier work this paper cites.
Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Xie, T. & Grossman, J. C · 2018
Earlier work this paper cites.
Schnet–a deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R · 2018
Earlier work this paper cites.
Insightful classification of crystal structures using deep learning
Ziletti, A., Kumar, D., Scheffler, M. & Ghiringhelli, L. M · 2018
Earlier work this paper cites.
Advances in neutron imaging
Kardjilov, N., Manke, I., Woracek, R., Hilger, A. & Banhart, J · 2018
Earlier work this paper cites.
Advanced steel microstructural classification by deep learning methods
Azimi, S. M., Britz, D., Engstler, M., Fritz, M. & Mücklich, F · 2018
Earlier work this paper cites.
High-throughput density-functional perturbation theory phonons for inorganic materials
Petretto, G. et al · 2018
Earlier work this paper cites.
Autonomous efficient experiment design for materials discovery with bayesian model averaging
Talapatra, A. et al · 2018
Earlier work this paper cites.
Machine learning modeling of superconducting critical temperature
Stanev, V. et al · 2018
Earlier work this paper cites.
Graph networks as a universal machine learning framework for molecules and crystals
Chen, C., Ye, W., Zuo, Y., Zheng, C. & Ong, S. P · 2019
Earlier work this paper cites.
Raman open database: first interconnected raman–x-ray diffraction open-access resource for material identification
El Mendili, Y. et al · 2019
Earlier work this paper cites.
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Gebauer, N., Gastegger, M. & Schütt, K · 2019
Earlier work this paper cites.
Synthesis, optical imaging, and absorption spectroscopy data for 179072 metal oxides
Stein, H. S., Soedarmadji, E., Newhouse, P. F., Guevarra, D. & Gregoire, J. M · 2019
Earlier work this paper cites.
Cormorant: Covariant molecular neural networks
Anderson, B., Hy, T. S. & Kondor, R · 2019
Earlier work this paper cites.
Recent developments in the inorganic crystal structure database: theoretical crystal structure data and related features
Zagorac, D., Müller, H., Ruehl, S., Zagorac, J. & Rehme, S · 2019
Earlier work this paper cites.
A robotic platform for flow synthesis of organic compounds informed by ai planning
Coley, C. W. et al · 2019
Earlier work this paper cites.
A graph-convolutional neural network model for the prediction of chemical reactivity
Coley, C. W. et al · 2019
Earlier work this paper cites.
Automated algorithm selection: Survey and perspectives
Kerschke, P., Hoos, H. H., Neumann, F. & Trautmann, H · 2019
Earlier work this paper cites.
The joint automated repository for various integrated simulations (jarvis) for data-driven materials design
Choudhary, K. et al · 2020
Earlier work this paper cites.
Predicting materials properties without crystal structure: deep representation learning from stoichiometry
Goodall, R. E. & Lee, A. A · 2020
Earlier work this paper cites.
An introduction to electrocatalyst design using machine learning for renewable energy storage
Zitnick, C. L. et al · 2020
Earlier work this paper cites.
A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic xrd powder patterns
Lee, J.-W., Park, W. B., Lee, J. H., Singh, S. P. & Sohn, K.-S · 2020
Earlier work this paper cites.
Robust atomistic modeling of materials, organometallic, and biochemical systems
Spicher, S. & Grimme, S · 2020
Earlier work this paper cites.
Crystals and crystal structures (John Wiley & Sons, 2020)
Tilley, R. J · 2020
Earlier work this paper cites.
Application of raman spectroscopy to probe fundamental properties of two-dimensional materials
Cong, X., Liu, X.-L., Lin, M.-L. & Tan, P.-H · 2020
Earlier work this paper cites.
Graph convolutional neural networks with global attention for improved materials property prediction
Louis, S.-Y. et al · 2020
Earlier work this paper cites.
Building powerful and equivariant graph neural networks with structural message-passing
Vignac, C., Loukas, A. & Frossard, P · 2020
Earlier work this paper cites.
Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm
Dunn, A., Wang, Q., Ganose, A., Dopp, D. & Jain, A · 2020
Earlier work this paper cites.
Directional message passing for molecular graphs
Gasteiger, J., Groß, J. & Günnemann, S · 2020
Earlier work this paper cites.
Systematic development of ab initio tight-binding models for hexagonal metals
Smutna, J., Fogarty, R., Wenman, M. & Horsfield, A · 2020
Earlier work this paper cites.
Accurate many-body repulsive potentials for density-functional tight binding from deep tensor neural networks
Stohr, M., Medrano Sandonas, L. & Tkatchenko, A · 2020
Earlier work this paper cites.
Airss data for carbon at 10gpa and the c+n+h+o system at 1gpa (2020)
Pickard, C. J · 2020
Earlier work this paper cites.
Atomly, https://atomly.net (2020)
2020
Earlier work this paper cites.
A mobile robotic chemist
Burger, B. et al · 2020
Earlier work this paper cites.
A decade survey of transfer learning (2010–2020)
Niu, S., Liu, Y., Wang, J. & Song, H · 2020
Earlier work this paper cites.
Computational sustainability meets materials science
Gomes, C. P., Fink, D., Van Dover, R. B. & Gregoire, J. M · 2021
Earlier work this paper cites.
Open catalyst 2020 (oc20) dataset and community challenges
Chanussot*, L. et al · 2021
Earlier work this paper cites.
Crystal diffusion variational autoencoder for periodic material generation
Xie, T., Fu, X., Ganea, O.-E., Barzilay, R. & Jaakkola, T. S · 2021
Earlier work this paper cites.
The open catalyst 2020 (oc20) dataset and community challenges. acs catalysis 11, 10 (may 2021), 6059–6072 (2021)
Chanussot, L. et al · 2021
Earlier work this paper cites.
Crystal graph attention networks for the prediction of stable materials
Schmidt, J., Pettersson, L., Verdozzi, C., Botti, S. & Marques, M. A · 2021
Earlier work this paper cites.
Deep learning-assisted quantification of atomic dopants and defects in 2d materials
Yang, S.-H. et al · 2021
Earlier work this paper cites.
Scanning probe microscopy
Bian, K. et al · 2021
Earlier work this paper cites.
Atomistic line graph neural network for improved materials property predictions
Choudhary, K. & DeCost, B · 2021
Earlier work this paper cites.
Direct prediction of phonon density of states with euclidean neural networks
Chen, Z. et al · 2021
Earlier work this paper cites.
Do transformers really perform badly for graph representation?
Ying, C. et al · 2021
Earlier work this paper cites.
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Schütt, K., Unke, O. & Gastegger, M · 2021
Earlier work this paper cites.
Directional message passing on molecular graphs via synthetic coordinates
Gasteiger, J., Yeshwanth, C. & Günnemann, S · 2021
Earlier work this paper cites.
Gemnet: Universal directional graph neural networks for molecules
Gasteiger, J., Becker, F. & Günnemann, S · 2021
Earlier work this paper cites.
High-throughput discovery of novel cubic crystal materials using deep generative neural networks
Zhao, Y. et al · 2021
Earlier work this paper cites.
A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
Ko, T. W., Finkler, J. A., Goedecker, S. & Behler, J · 2021
Earlier work this paper cites.
Machine learning method for tight-binding hamiltonian parameterization from ab-initio band structure
Wang, Z. et al · 2021
Earlier work this paper cites.
Validation of the crystallography open database using the crystallographic information framework
Vaitkus, A., Merkys, A. & Gražulis, S · 2021
Earlier work this paper cites.
High-throughput discovery of novel cubic crystal materials using deep generative neural networks
Zhao, Y. et al · 2021
Earlier work this paper cites.
Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
Rosen, A. S. et al · 2021
Cited alongside, same era.
Towards the unification and robustness of perturbation and gradient based explanations
Agarwal, S. et al · 2021
Cited alongside, same era.
A survey on generative adversarial networks: Variants, applications, and training
Jabbar, A., Li, X. & Omar, B · 2021
Cited alongside, same era.
Experimental discovery of structure–property relationships in ferroelectric materials via active learning
Liu, Y. et al · 2022
Cited alongside, same era.
Accelerating materials discovery using artificial intelligence, high performance computing and robotics
Pyzer-Knapp, E. O. et al · 2022
Cited alongside, same era.
Learning superconductivity from ordered and disordered material structures
Chen, P. et al · 2024
Closest in time.
FlowMM: Generating materials with riemannian flow matching
Miller, B. K., Chen, R. T. Q., Sriram, A. & Wood, B. M · 2024
Closest in time.
Equivariant message passing neural network for crystal material discovery
Klipfel, A. et al · 2024
Closest in time.
Deep learning generative model for crystal structure prediction
Luo, X. et al · 2024
Closest in time.
Con-cdvae: A method for the conditional generation of crystal structures
Ye, C.-Y., Weng, H.-M. & Wu, Q.-S · 2024
Closest in time.
Equivariant diffusion for crystal structure prediction
Lin, P. et al · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Choudhary, K. et al · 2022
Cited alongside, same era.
A universal graph deep learning interatomic potential for the periodic table
Chen, C. & Ong, S. P · 2022
Cited alongside, same era.
E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S. et al · 2022
Cited alongside, same era.
Geometrically equivariant graph neural networks: A survey
Han, J., Rong, Y., Xu, T. & Huang, W · 2022
Cited alongside, same era.
Graph neural networks for materials science and chemistry
Reiser, P. et al · 2022
Cited alongside, same era.
Atomic level defect structure engineering for unusually high average thermoelectric figure of merit in n-type pbse rivalling pbte
Ge, B. et al · 2022
Cited alongside, same era.
Capturing long-range interaction with reciprocal space neural network
Yu, H., Hong, L., Chen, S., Gong, X. & Xiang, H · 2022
Cited alongside, same era.
Vector field oriented diffusion model for crystal material generation
Klipfel, A., Fregier, Y., Sayede, A. & Bouraoui, Z · 2024
Closest in time.
Crystal structure generation with autoregressive large language modeling
Antunes, L. M., Butler, K. T. & Grau-Crespo, R · 2024
Closest in time.
Flowllm: Flow matching for material generation with large language models as base distributions
Sriram, A., Miller, B. K., Chen, R. T. & Wood, B. M · 2024
Closest in time.
Sprueill, H. W. et al · 2024
Closest in time.
3d structure prediction of atomic systems with flow-based direct preference optimization
Jiao, R., Kong, X., Huang, W. & Liu, Y · 2024
Closest in time.
Rapid prediction of molecular crystal structures using simple topological and physical descriptors
Galanakis, N. & Tuckerman, M. E · 2024
Closest in time.
Wycryst: Wyckoff inorganic crystal generator framework
Zhu, R., Nong, W., Yamazaki, S. & Hippalgaonkar, K · 2024
Closest in time.
A database of computed raman spectra of inorganic compounds with accurate hybrid functionals
Li, Y. et al · 2024
Closest in time.
Deep learning tight-binding approach for large-scale electronic simulations at finite temperatures with ab initio accuracy
Gu, Q. et al · 2024
Closest in time.
Jarvis-leaderboard: a large scale benchmark of materials design methods
Choudhary, K. et al · 2024
Closest in time.
Structure-based out-of-distribution (ood) materials property prediction: a benchmark study
Omee, S. S., Fu, N., Dong, R., Hu, M. & Hu, J · 2024
Closest in time.
A review of large language models and autonomous agents in chemistry
Ramos, M. C., Collison, C. & White, A. D · 2024
Closest in time.
Augmenting large language models with chemistry tools
M. Bran, A. et al · 2024
Closest in time.
Llmatdesign: Autonomous materials discovery with large language models
Jia, S., Zhang, C. & Fung, V · 2024
Closest in time.
Beavertails: Towards improved safety alignment of llm via a human-preference dataset
Ji, J. et al · 2024
Closest in time.
A survey on large language model (llm) security and privacy: The good, the bad, and the ugly
Yao, Y. et al · 2024
Closest in time.
Human-in-the-loop ai reviewing: Feasibility, opportunities, and risks
Drori, I. & Te’eni, D · 2024
Closest in time.
On replacing humans with large language models in voice-based human-in-the-loop systems
Huang, S.-H. et al · 2024
Closest in time.
Wang, Z. et al · 2024
Closest in time.
Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors
Gallegos, M., Vassilev-Galindo, V., Poltavsky, I., Martín Pendás, Á. & Tkatchenko, A · 2024
Closest in time.
Explainable data-driven modeling of adsorption energy in heterogeneous catalysis (2024)
Vinchurkar, T., Ock, J. & Farimani, A. B · 2024
Closest in time.
A survey of geometric graph neural networks: Data structures, models and applications
Han, J. et al · 2024
Closest in time.
Evolutionary computation in the era of large language model: Survey and roadmap
Wu, X., Wu, S.-h., Wu, J., Feng, L. & Tan, K. C · 2024
Closest in time.
Scientific large language models: A survey on biological & chemical domains
Zhang, Q. et al · 2024
Closest in time.
Diffusion-driven domain adaptation for generating 3d molecules
Hong, H., Lin, W. & Tan, K. C · 2024
Closest in time.
A generative model for inorganic materials design
Zeni, C. et al · 2025
Closest in time.
Simxrd-4m: Big simulated x-ray diffraction data and crystal symmetry classification benchmark
Bin, C. et al · 2025
Closest in time.
Regnet: Reciprocal space-aware long-range modeling for crystalline property prediction
Nie, J., Xiao, P., Ji, K. & Gao, P · 2025
Closest in time.
Symmcd: Symmetry-preserving crystal generation with diffusion models
Levy, D. et al · 2025
Closest in time.
Wyckoffdiff–a generative diffusion model for crystal symmetry
Kelvinius, F. E. et al · 2025
Closest in time.
Rethinking the role of frames for se (3)-invariant crystal structure modeling
Ito, Y., Taniai, T., Igarashi, R., Ushiku, Y. & Ono, K · 2025
Closest in time.
Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration
Zou, H. et al · 2025
Closest in time.
Transformer-generated atomic embeddings to enhance prediction accuracy of crystal properties with machine learning
Jin, L. et al · 2025
Closest in time.
A multi-modal transformer for predicting global minimum adsorption energy
Chen, J., Huang, X., Hua, C., He, Y. & Schwaller, P · 2025
Closest in time.
A denoising pre-training framework for accelerating novel material discovery
Shen, S., Liu, K., Zhu, M. & Chen, H · 2025
Closest in time.
Pddformer: pairwise distance distribution graph transformer for crystal material property prediction
Shen, X. et al · 2025
Closest in time.
Code-generated graph representations using multiple llm agents for material properties prediction
Huang, J., Xing, Q., Ji, J. & Yang, B · 2025
Closest in time.
Accurate prediction of synthesizability and precursors of 3d crystal structures via large language models
Song, Z., Lu, S., Ju, M., Zhou, Q. & Wang, J · 2025
Closest in time.
Scitoolagent: a knowledge-graph-driven scientific agent for multitool integration
Ding, K. et al · 2025
Closest in time.
Local-global associative frames for symmetry-preserving crystal structure modeling
Hua, H. & Lin, W · 2025
Closest in time.
Exploration of crystal chemical space using text-guided generative artificial intelligence
Park, H., Onwuli, A. & Walsh, A · 2025
Closest in time.
Predicting emergence of crystals from amorphous precursors with deep learning potentials
Aykol, M., Merchant, A., Batzner, S., Wei, J. N. & Cubuk, E. D · 2025
Closest in time.
Matexpert: Decomposing materials discovery by mimicking human experts
Ding, Q., Miret, S. & Liu, B · 2025
Closest in time.
Mofflow: Flow matching for structure prediction of metal-organic frameworks
Kim, N., Kim, S., Kim, M., Park, J. & Ahn, S · 2025
Closest in time.
Periodic materials generation using text-guided joint diffusion model
DAS, K. et al · 2025
Closest in time.
A periodic bayesian flow for material generation
Wu, H. et al · 2025
Closest in time.
Osda agent: Leveraging large language models for de novo design of organic structure directing agents
Hu, Z. et al · 2025
Closest in time.
Flow matching with general discrete paths: A kinetic-optimal perspective
Shaul, N. et al · 2025
Closest in time.
Efficient crystal structure prediction based on the symmetry principle
Han, Y. et al · 2025
Closest in time.
A robust crystal structure prediction method to support small molecule drug development with large scale validation and blind study
Zhou, D. et al · 2025
Closest in time.
Wyckoff transformer: Generation of symmetric crystals
Kazeev, N. et al · 2025
Closest in time.
Kinetic langevin diffusion for crystalline materials generation
Cornet, F. et al · 2025
Closest in time.
Open materials generation with stochastic interpolants
Höllmer, P. et al · 2025
Closest in time.
All-atom diffusion transformers: Unified generative modelling of molecules and materials
Joshi, C. K. et al · 2025
Closest in time.
Macs: Multi-agent reinforcement learning for optimization of crystal structures
Zamaraeva, E. et al · 2025
Closest in time.
Space group equivariant crystal diffusion
Chang, R. et al · 2025
Closest in time.
Llm meets diffusion: A hybrid framework for crystal material generation
Khastagir, S. et al · 2025
Closest in time.
Mof-bfn: Metal-organic frameworks structure prediction via bayesian flow networks
Jiao, R. et al · 2025
Closest in time.
Flexible mof generation with torsion-aware flow matching
Kim, N., Kim, S. & Ahn, S · 2025
Closest in time.
Crystalicl: Enabling in-context learning for crystal generation
Wang, R., Tan, Q., Wang, Y., Wang, Y. & Wang, X · 2025
Closest in time.
Ecd: A machine learning benchmark for predicting enhanced-precision electronic charge density in crystalline inorganic materials
Chen, P. et al · 2025
Closest in time.
Probing the limitations of multimodal language models for chemistry and materials research
Alampara, N. et al · 2025
Closest in time.
A framework to evaluate machine learning crystal stability predictions
Riebesell, J. et al · 2025
Closest in time.
Systematic softening in universal machine learning interatomic potentials
Deng, B. et al · 2025
Closest in time.
Dis-csp: Disordered crystal structure predictions
Petersen, M. H. et al · 2025
Closest in time.
Learning crystallographic disorder: Bridging prediction and experiment in materials discovery
Jakob, K. S., Walsh, A., Reuter, K. & Margraf, J. T · 2025
Closest in time.