Copertina

AtomGPT: Atomistic Generative Pre-trained Transformer for Forward and Inverse Materials Design

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Choudhary, Kamal
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Materials Science
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns using Generative Pre-trained Transformer
di: Choudhary, Kamal
Pubblicazione: (2025)

The JARVIS Infrastructure is All You Need for Materials Design
di: Choudhary, Kamal
Pubblicazione: (2025)

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org
di: Lee, Jaehyung, et al.
Pubblicazione: (2025)

MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials
di: Chen, Yan, et al.
Pubblicazione: (2024)

A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials
di: Choudhary, Kamal, et al.
Pubblicazione: (2022)

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties
di: Wines, Daniel, et al.
Pubblicazione: (2024)

Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning
di: Wines, Daniel, et al.
Pubblicazione: (2023)

ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data
di: Choudhary, Kamal, et al.
Pubblicazione: (2022)

InterMat: Accelerating Band Offset Prediction in Semiconductor Interfaces with DFT and Deep Learning
di: Choudhary, Kamal, et al.
Pubblicazione: (2024)

CHIPS-TB: Evaluating Tight-Binding Models For Metals, Semiconductors, and Insulators
di: Park, In Jun, et al.
Pubblicazione: (2025)

M-CODE: Materials Categorization via Ontology, Dimensionality and Evolution
di: Biryukov, Vsevolod, et al.
Pubblicazione: (2026)

Effect of Exchange-Correlation Functionals on Schottky Barriers at Si/Metal Interfaces
di: Dovale-Farelo, Viviana, et al.
Pubblicazione: (2026)

Efficient Computational Design of 2D van der Waals Heterostructures: Band-Alignment, Lattice-Mismatch, Web-app Generation and Machine-learning
di: Choudhary, Kamal, et al.
Pubblicazione: (2020)

Predicting Atomistic Transitions with Transformers
di: Tischler, Henry, et al.
Pubblicazione: (2026)

Inverse Design of Amorphous Materials with Targeted Properties
di: Finkler, Jonas A., et al.
Pubblicazione: (2025)

Inverse Materials Design via Joint Generation of Crystal Structures and Local Electronic Descriptors
di: Okuda, Ibuki, et al.
Pubblicazione: (2026)

Efficient All-electron Hybrid Density Functionals for Atomistic Simulations Beyond 10,000 Atoms
di: Kokott, Sebastian, et al.
Pubblicazione: (2024)

Inverse Materials Design by Large Language Model-Assisted Generative Framework
di: Hao, Yun, et al.
Pubblicazione: (2025)

Stability by Design: Atomistic Insights into Hydrolysis-Driven MOF Degradation
di: Yacham, Ashok, et al.
Pubblicazione: (2025)

Kolmogorov-Arnold Networks in Thermoelectric Materials Design
di: Fronzi, Marco, et al.
Pubblicazione: (2025)

Generative Inverse Design of Crystal Structures via Diffusion Models with Transformers
di: Takahara, Izumi, et al.
Pubblicazione: (2024)

VAE for Modified 1-Hot Generative Materials Modeling, A Step Towards Inverse Material Design
di: El-Awady, Khalid
Pubblicazione: (2023)

Effective Atom Theory: Gradient-Driven ab initio Materials Design
di: Tahmassebpur, Justin, et al.
Pubblicazione: (2025)

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
di: Li, Chaojian, et al.
Pubblicazione: (2025)

Hop-Decorate: An Automated Atomistic Workflow for Generating Defect Transport Data in Chemically Complex Materials
di: Hatton, Peter, et al.
Pubblicazione: (2025)

Generalized Representative Structures for Atomistic Systems
di: Goff, James M., et al.
Pubblicazione: (2024)

Accelerated prediction of dielectric functions in solar cell materials with graph neural networks
di: Ginter, Caden, et al.
Pubblicazione: (2025)

Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery
di: Jia, Shuyi, et al.
Pubblicazione: (2025)

AI-ready design of realistic 2D materials and interfaces with Mat3ra-2D
di: Biryukov, Vsevolod, et al.
Pubblicazione: (2026)

Machine Learning for Predicting Magnetization from X-ray Diffraction of Iron Oxide Nanoparticles Using Simple Physics-Based Data Generation
di: Abel, Frank M., et al.
Pubblicazione: (2025)

Inverse Lieb Materials: Altermagnetism and More
di: Chang, Po-Hao, et al.
Pubblicazione: (2025)

Electronic Structure Guided Inverse Design Using Generative Models
di: Jia, Shuyi, et al.
Pubblicazione: (2025)

AMShortcut: An Inference- and Training-Efficient Inverse Design Model for Amorphous Materials
di: Lin, Yan, et al.
Pubblicazione: (2026)

LeMat-Traj: A Scalable and Unified Dataset of Materials Trajectories for Atomistic Modeling
di: Ramlaoui, Ali, et al.
Pubblicazione: (2025)

The Good, the Bad, and the Ugly of Atomistic Learning for "Clusters-to-Bulk" Generalization
di: Gawkowski, Mikołaj J., et al.
Pubblicazione: (2025)

Uncertainty Quantification and Propagation in Atomistic Machine Learning
di: Dai, Jin, et al.
Pubblicazione: (2024)

Spin Dynamics from Atomistic Quantum Simulations
di: Drigo, Enrico, et al.
Pubblicazione: (2026)

Atomistic Modeling of Martensitic Phase Transition in Hexamethylbenzene
di: Fahim, Zarif, et al.
Pubblicazione: (2025)

Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
di: Li, Kangming, et al.
Pubblicazione: (2024)

Atomistic theory of the phonon angular momentum Hall effect
di: Lopez, Daniel A. Bustamante, et al.
Pubblicazione: (2026)