Dataset of Random Relaxations for Crystal Structure Search of Li-Si System
Crystal structure search is a long-standing challenge in materials design. We present a dataset of more than 100,000 structural relaxations of potential battery anode materials from randomized structures using density functional theory calculations. We illustrate the usage of the dataset by training graph neural networks to predict structural relaxations from randomly generated structures. Our models directly predict stresses in addition to forces, which allows them to accurately simulate relaxations of both ionic positions and lattice vectors. We show that models trained on the molecular dynamics simulations fail to simulate relaxations from random structures, while training on our data leads to up to two orders of magnitude decrease in error for the same task. Our model is able to find an experimentally verified structure of a stoichiometry held out from training. We find that randomly perturbing atomic positions during training improves both the accuracy and out of domain generalization of the models.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationDomain GeneralizationSimilar Papers 제목 키워드 기반
Crystal structure prediction of materials with high symmetry using differential evolution
Crystal structure determines properties of materials. With the crystal structure of a chemical substance, many physical and chemical properties can be predicted by first-principles calculations or machine learning models…
global-optimizationVocal Bursts Intensity PredictionCrystalGRW: Generative Modeling of Crystal Structures with Targeted Properties via Geodesic Random Walks
Determining whether a candidate crystalline material is thermodynamically stable depends on identifying its true ground-state structure, a central challenge in computational materials science. We introduce CrystalGRW, a …
Graph Neural NetworkScalable Diffusion for Materials Generation
Generative models trained on internet-scale data are capable of generating novel and realistic texts, images, and videos. A natural next question is whether these models can advance science, for example by generating nov…
Formation EnergyNear-Optimal Coalition Structures in Polynomial Time
We study the classical coalition structure generation (CSG) problem and compare the anytime behavior of three algorithmic paradigms: dynamic programming (DP), MILP branch-and-bound, and sparse relaxations based on greedy…
Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction
In computational chemistry, crystal structure prediction (CSP) is an optimization problem that involves discovering the lowest energy stable crystal structure for a given chemical formula. This problem is challenging as …
Computational chemistryFormation Energy