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Crystal structure generation is fundamental to materials science, enabling the discovery of novel materials with desired properties.
Efficient Iterative Schemes for Ab Initio Total-Energy Calculations Using a Plane-Wave Basis Set
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Generalized Gradient Approximation Made Simple
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Crystal structure prediction using ab initio evolutionary techniques: Principles and applications
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The Thermodynamic Scale of Inorganic Crystalline Metastability
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Inverse Design of Solid-State Materials via a Continuous Representation
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Coevolutionary Search for Optimal Materials in the Space of All Possible Compounds
Zahed Allahyari and Artem R. Oganov · 2020
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3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning
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Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm
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A Framework for Quantifying Uncertainty in DFT Energy Corrections
Amanda Wang, Ryan Kingsbury, Matthew McDermott, et al · 2021
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Phase Diagram of a Deep Potential Water Model
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E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon Batzner, Albert Musaelian, Lixin Sun, et al · 2022
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A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
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Crystal Diffusion Variational Autoencoder for Periodic Material Generation
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GPT-4 Technical Report
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Crystal Structure Generation with Autoregressive Large Language Modeling
Luis M. Antunes, Keith T. Butler, and Ricardo Grau-Crespo · 2023
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A Foundation Model for Atomistic Materials Chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang, et al · 2023
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Autonomous Chemical Research with Large Language Models
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Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
Nate Gruver, Anuroop Sriram, Andrea Madotto, et al · 2024
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Crystal Structure Prediction by Joint Equivariant Diffusion
Rui Jiao, Wenbing Huang, Peijia Lin, et al · 2024
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EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Yi-Lun Liao, Brandon Wood, Abhishek Das, et al · 2024
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Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge of Large Language Models
Jieyu Lu, Zhangde Song, Qiyuan Zhao, et al · 2024
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Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations
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CHGNet as a Pretrained Universal Neural Network Potential for Charge-Informed Atomistic Modelling
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Graph Neural Networks for Predicting Structural Stability of Cd- and Zn-doped λ \lambda -CsPbI 3
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Language Models Can Generate Molecules, Materials, and Protein Binding Sites Directly in Three Dimensions as XYZ, CIF, and PDB Files
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What Can Large Language Models Do in Chemistry? A Comprehensive Benchmark on Eight Tasks
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14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
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Is Temperature the Creativity Parameter of Large Language Models?
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LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction
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From Molecules to Materials: Pre-Training Large Generalizable Models for Atomic Property Prediction
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FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
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Efficient evolutionary search over chemical space with large language models
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An Equivariant Graph Neural Network for the Elasticity Tensors of All Seven Crystal Systems
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Invariant tokenization of crystalline materials for language model enabled generation
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Pretraining of Attention-Based Deep Learning Potential Model for Molecular Simulation
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Orb-v3: atomistic simulation at scale, 2025
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