9 research outputs found
A data-centric framework for crystal structure identification in atomistic simulations using machine learning
Atomic-level modeling performed at large scales enables the investigation of
mesoscale materials properties with atom-by-atom resolution. The spatial
complexity of such cross-scale simulations renders them unsuitable for simple
human visual inspection. Instead, specialized structure characterization
techniques are required to aid interpretation. These have historically been
challenging to construct, requiring significant intuition and effort. Here we
propose an alternative framework for a fundamental structural characterization
task: classifying atoms according to the crystal structure to which they
belong. Our approach is data-centric and favors the employment of Machine
Learning over heuristic rules of classification. A group of data-science tools
and simple local descriptors of atomic structure are employed together with an
efficient synthetic training set. We also introduce the first standard and
publicly available benchmark data set for evaluation of algorithms for
crystal-structure classification. It is demonstrated that our data-centric
framework outperforms all of the most popular heuristic methods -- especially
at high temperatures when lattices are the most distorted -- while introducing
a systematic route for generalization to new crystal structures. Moreover,
through the use of outlier detection algorithms our approach is capable of
discerning between amorphous atomic motifs (i.e., noncrystalline phases) and
unknown crystal structures, making it uniquely suited for exploratory materials
synthesis simulations.Comment: 16 pages, 7 figure
Crystal Structure Search with Random Relaxations Using Graph Networks
Materials design enables technologies critical to humanity, including
combating climate change with solar cells and batteries. Many properties of a
material are determined by its atomic crystal structure. However, prediction of
the atomic crystal structure for a given material's chemical formula is a
long-standing grand challenge that remains a barrier in materials design. We
investigate a data-driven approach to accelerating ab initio random structure
search (AIRSS), a state-of-the-art method for crystal structure search. We
build a novel dataset of random structure relaxations of Li-Si battery anode
materials using high-throughput density functional theory calculations. We
train graph neural networks to simulate relaxations of random structures. Our
model is able to find an experimentally verified structure of Li15Si4 it was
not trained on, and has potential for orders of magnitude speedup over AIRSS
when searching large unit cells and searching over multiple chemical
stoichiometries. Surprisingly, we find that data augmentation of adding
Gaussian noise improves both the accuracy and out of domain generalization of
our models.Comment: Removed citations from the abstract, paper content is unchange
Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions
Abstract Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model (SynthNN) that leverages the entire space of synthesized inorganic chemical compositions. By reformulating material discovery as a synthesizability classification task, SynthNN identifies synthesizable materials with 7× higher precision than with DFT-calculated formation energies. In a head-to-head material discovery comparison against 20 expert material scientists, SynthNN outperforms all experts, achieves 1.5× higher precision and completes the task five orders of magnitude faster than the best human expert. Remarkably, without any prior chemical knowledge, our experiments indicate that SynthNN learns the chemical principles of charge-balancing, chemical family relationships and ionicity, and utilizes these principles to generate synthesizability predictions. The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials
Data Mining for New Two- and One-Dimensional Weakly Bonded Solids and Lattice-Commensurate Heterostructures
Layered materials held together
by weak interactions including van der Waals forces, such as graphite,
have attracted interest for both technological applications and fundamental
physics in their layered form and as an isolated single-layer. Only
a few dozen single-layer van der Waals solids have been subject to
considerable research focus, although there are likely to be many
more that could have superior properties. To identify a broad spectrum
of layered materials, we present a novel data mining algorithm that
determines the dimensionality of weakly bonded subcomponents based
on the atomic positions of bulk, three-dimensional crystal structures.
By applying this algorithm to the Materials Project database of over
50,000 inorganic crystals, we identify 1173 two-dimensional layered
materials and 487 materials that consist of weakly bonded one-dimensional
molecular chains. This is an order of magnitude increase in the number
of identified materials with most materials not known as two- or one-dimensional
materials. Moreover, we discover 98 weakly bonded heterostructures
of two-dimensional and one-dimensional subcomponents that are found
within bulk materials, opening new possibilities for much-studied
assembly of van der Waals heterostructures. Chemical families of materials,
band gaps, and point groups for the materials identified in this work
are presented. Point group and piezoelectricity in layered materials
are also evaluated in single-layer forms. Three hundred and twenty-five
of these materials are expected to have piezoelectric monolayers with
a variety of forms of the piezoelectric tensor. This work significantly
extends the scope of potential low-dimensional weakly bonded solids
to be investigated