CRYSPNet: Crystal Structure Predictions via Neural Network

• URL: http://arxiv.org/abs/2003.14328v1
• Date: Tue, 31 Mar 2020 16:05:18 GMT
• Title: CRYSPNet: Crystal Structure Predictions via Neural Network
• Authors: Haotong Liang, Valentin Stanev, A. Gilad Kusne, Ichiro Takeuchi
• Abstract summary: We present an alternative approach utilizing machine learning for crystal structure prediction. We developed a tool called Crystal Structure Prediction Network (CRYSPNet) that can predict the Bravais lattice, space group, and lattice parameters of an inorganic material. It was trained and validated on more than 100,000 entries from the Inorganic Crystal Structure Database.
• Score: 14.930208990741129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most materials characteristics. However, predicting crystal structure of solids remains a formidable and not fully solved problem. Standard theoretical tools for this task are computationally expensive and at times inaccurate. Here we present an alternative approach utilizing machine learning for crystal structure prediction. We developed a tool called Crystal Structure Prediction Network (CRYSPNet) that can predict the Bravais lattice, space group, and lattice parameters of an inorganic material based only on its chemical composition. CRYSPNet consists of a series of neural network models, using as inputs predictors aggregating the properties of the elements constituting the compound. It was trained and validated on more than 100,000 entries from the Inorganic Crystal Structure Database. The tool demonstrates robust predictive capability and outperforms alternative strategies by a large margin. Made available to the public (at https://github.com/AuroraLHT/cryspnet), it can be used both as an independent prediction engine or as a method to generate candidate structures for further computational and/or experimental validation.

Related papers

• CrySPAI: A new Crystal Structure Prediction Software Based on Artificial Intelligence [18.986318006059566]
  We present CrySPAI, a crystal structure prediction package developed using artificial intelligence (AI) to predict energetically stable crystal structures of inorganic materials. The software consists of three key modules, an evolutionary optimization algorithm (EOA) that searches for all possible crystal structure configurations, density functional theory (DFT) that provides the accurate energy values, and a deep neural network (DNN) that learns the relationship between crystal structures and their corresponding energies.
  arXiv  Detail & Related papers  (2025-01-27T07:53:06Z)
• Generative Hierarchical Materials Search [91.93125016916463]
  We propose Generative Hierarchical Materials Search (GenMS) for controllable generation of crystal structures. GenMS consists of (1) a language model that takes high-level natural language as input and generates intermediate textual information about a crystal. GenMS additionally uses a graph neural network to predict properties (e.g., formation energy) from the generated crystal structures.
  arXiv  Detail & Related papers  (2024-09-10T17:51:28Z)
• CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction [0.0]
  In material science literature, it is well-known that crystalline materials exhibit topological structures. In this paper, we propose an unsupervised framework namely, CrysAtom, using untagged crystal data to generate dense vector representation of atoms.
  arXiv  Detail & Related papers  (2024-09-07T06:58:55Z)
• A Space Group Symmetry Informed Network for O(3) Equivariant Crystal Tensor Prediction [89.38877696273364]
  We consider the prediction of general tensor properties of crystalline materials. We propose a General Materials Network (GMTNet), which is carefully designed to satisfy the required symmetries. Experimental results show that our GMTNet achieves promising performance on crystal tensors of various orders.
  arXiv  Detail & Related papers  (2024-06-03T16:26:16Z)
• Complete and Efficient Graph Transformers for Crystal Material Property Prediction [53.32754046881189]
  Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. We introduce a novel approach that utilizes the periodic patterns of unit cells to establish the lattice-based representation for each atom. We propose ComFormer, a SE(3) transformer designed specifically for crystalline materials.
  arXiv  Detail & Related papers  (2024-03-18T15:06:37Z)
• 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)
• Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction [62.36797874900395]
  In computational chemistry, crystal structure prediction is an optimization problem. One approach to tackle this problem involves building simulators based on density functional theory (DFT) followed by running search in simulation. We show that our approach, dubbed LCOMs (latent conservative objective models), performs comparably to the best current approaches in terms of success rate of structure prediction.
  arXiv  Detail & Related papers  (2023-10-16T04:35:44Z)
• Crystal-GFN: sampling crystals with desirable properties and constraints [103.79058968784163]
  We introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials. In this paper, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.
  arXiv  Detail & Related papers  (2023-10-07T21:36:55Z)
• DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for Predicting Properties of Crystalline Materials [0.0]
  We propose DeepCrysTet, a novel deep learning approach for predicting material properties. DeepCrysTet uses crystal structures represented as a 3D tetrahedral mesh generated by Delaunay tetrahedralization. Experiments show that DeepCrysTet significantly outperforms existing GNN models in classifying crystal structures and state-of-the-art performance in predicting elastic properties.
  arXiv  Detail & Related papers  (2023-09-07T05:23:52Z)
• Shotgun crystal structure prediction using machine-learned formation energies [3.2563787689949133]
  Crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Here, we present significant progress toward solving the crystal structure prediction problem. We performed noniterative, single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor.
  arXiv  Detail & Related papers  (2023-05-03T14:46:16Z)
• Neural Structure Fields with Application to Crystal Structure Autoencoders [10.680545976155173]
  We propose neural structure fields (NeSF) as an accurate and practical approach for representing crystal structures using neural networks. NeSF overcomes the tradeoff between spatial resolution and computational complexity and can represent any crystal structure. We propose an autoencoder of crystal structures that can recover various crystal structures, such as those of perovskite structure materials and cuprate superconductors.
  arXiv  Detail & Related papers  (2022-12-08T16:41:41Z)

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.