Discovery of 2D materials using Transformer Network based Generative Design

• URL: http://arxiv.org/abs/2301.05824v1
• Date: Sat, 14 Jan 2023 05:59:38 GMT
• Title: Discovery of 2D materials using Transformer Network based Generative Design
• Authors: Rongzhi Dong, Yuqi Song, Edirisuriya M. D. Siriwardane, Jianjun Hu
• Abstract summary: We train two 2D materials composition generators using self-learning neural language models based on Transformers with and without transfer learning. The models are then used to generate a large number of candidate 2D compositions, which are fed to known 2D materials templates for crystal structure prediction. We report four new DFT-verified stable 2D materials with zero e-above-hull energies, including NiCl$_4$, IrSBr, CuBr$_3$, and CoBrCl.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Two-dimensional (2D) materials have wide applications in superconductors, quantum, and topological materials. However, their rational design is not well established, and currently less than 6,000 experimentally synthesized 2D materials have been reported. Recently, deep learning, data-mining, and density functional theory (DFT)-based high-throughput calculations are widely performed to discover potential new materials for diverse applications. Here we propose a generative material design pipeline, namely material transformer generator(MTG), for large-scale discovery of hypothetical 2D materials. We train two 2D materials composition generators using self-learning neural language models based on Transformers with and without transfer learning. The models are then used to generate a large number of candidate 2D compositions, which are fed to known 2D materials templates for crystal structure prediction. Next, we performed DFT computations to study their thermodynamic stability based on energy-above-hull and formation energy. We report four new DFT-verified stable 2D materials with zero e-above-hull energies, including NiCl$_4$, IrSBr, CuBr$_3$, and CoBrCl. Our work thus demonstrates the potential of our MTG generative materials design pipeline in the discovery of novel 2D materials and other functional materials.

Related papers

• MaterialSeg3D: Segmenting Dense Materials from 2D Priors for 3D Assets [63.284244910964475]
  We propose a 3D asset material generation framework to infer underlying material from the 2D semantic prior. Based on such a prior model, we devise a mechanism to parse material in 3D space.
  arXiv  Detail & Related papers  (2024-04-22T07:00:17Z)
• Fine-Tuned Language Models Generate Stable Inorganic Materials as Text [57.01994216693825]
  Fine-tuning large language models on text-encoded atomistic data is simple to implement yet reliable. We show that our strongest model can generate materials predicted to be metastable at about twice the rate of CDVAE. Because of text prompting's inherent flexibility, our models can simultaneously be used for unconditional generation of stable material.
  arXiv  Detail & Related papers  (2024-02-06T20:35:28Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• Breathing New Life into 3D Assets with Generative Repainting [74.80184575267106]
  Diffusion-based text-to-image models ignited immense attention from the vision community, artists, and content creators. Recent works have proposed various pipelines powered by the entanglement of diffusion models and neural fields. We explore the power of pretrained 2D diffusion models and standard 3D neural radiance fields as independent, standalone tools. Our pipeline accepts any legacy renderable geometry, such as textured or untextured meshes, and orchestrates the interaction between 2D generative refinement and 3D consistency enforcement tools.
  arXiv  Detail & Related papers  (2023-09-15T16:34:51Z)
• Accelerate & Actualize: Can 2D Materials Bridge the Gap Between Neuromorphic Hardware and the Human Brain? [0.6423239719448168]
  Two-dimensional (2D) materials present an exciting opportunity for devices and systems beyond the von Neumann computing architecture paradigm. All major classes of non-volatile memory (NVM) devices have been demonstrated using 2D materials.
  arXiv  Detail & Related papers  (2023-01-24T19:21:39Z)
• Machine-learning accelerated identification of exfoliable two-dimensional materials [0.0]
  Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties. It is crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by a weak binding energy. We develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials.
  arXiv  Detail & Related papers  (2022-07-18T14:48:53Z)
• Data-driven discovery of novel 2D materials by deep generative models [0.0]
  We show that a crystal diffusion variational autoencoder (CDVAE) is capable of generating 2D materials of high chemical and structural diversity. In total we find 11630 predicted new 2D materials, where 8599 of these have $Delta H_mathrmhull 0.3$ eV/atom as the seed structures.
  arXiv  Detail & Related papers  (2022-06-24T08:42:58Z)
• Family of Two Dimensional Transition Metal Dichlorides Fundamental Properties, Structural Defects, and Environmental Stability [6.098877408363052]
  A large number of novel 2D materials are constantly discovered and deposed into the databases. The next step in this chain, the investigation leads to a comprehensive study of the functionality of the invented materials. The work highlights the importance of using the potential of the invented materials and proposes a comprehensive characterization of a new family of 2D materials.
  arXiv  Detail & Related papers  (2022-04-29T10:01:58Z)
• BIGDML: Towards Exact Machine Learning Force Fields for Materials [55.944221055171276]
  Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 atoms.
  arXiv  Detail & Related papers  (2021-06-08T10:14:57Z)
• Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis [143.22192229456306]
  This paper proposes a deep 3D energy-based model to represent volumetric shapes. The benefits of the proposed model are six-fold. Experiments demonstrate that the proposed model can generate high-quality 3D shape patterns.
  arXiv  Detail & Related papers  (2020-12-25T06:09:36Z)
• Computational discovery of new 2D materials using deep learning generative models [6.918364447822299]
  Two dimensional (2D) materials have emerged as promising functional materials with many applications. We propose a deep learning generative model for composition generation combined with random forest based 2D materials. We have discovered 267,489 new potential 2D materials compositions and confirmed twelve 2D/layered materials.
  arXiv  Detail & Related papers  (2020-12-16T23:10:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.