2023 New's Items

Tin Metal Improves the Lithiation Kinetics of High-Capacity Silicon Anodes

Yao K
Tong W
Persson KA
2023

Si-based anodes present a great promise for high energy density lithium-ion batteries. However, its commercialization is largely hindered by a grand challenge of a rapid capacity fade. Here, we demonstrate excellent cycling stability on a Si–Sn thin film electrode that outperforms pure Si or Sn counterpart under the similar conditions. Combined with the first-principles calculations, in situ transmission electron microscopy studies reveal a reduced volume expansion, increased conductivity, as well as dynamic rearrangement upon lithiation of the Si–Sn film. We attribute the improved...

Spatially resolved structural order in low-temperature liquid electrolyte

Xie Y
Zheng H
Persson KA
2023

Determining the degree and the spatial extent of structural order in liquids is a grand challenge. Here, we are able to resolve the structural order in a model organic electrolyte of 1 M lithium hexafluorophosphate (LiPF6) dissolved in 1:1 (v/v) ethylene carbonate:diethylcarbonate by developing an integrated method of liquid-phase transmission electron microscopy (TEM), cryo-TEM operated at −30°C, four-dimensional scanning TEM, and data analysis based on deep learning. This study reveals the presence of short-range order (SRO) in the high–salt concentration...

Designing transparent conductors using forbidden optical transitions

Woods-Robinson R
Persson KA
2023

Many semiconductors present weak or forbidden transitions at their fundamental band gaps, inducing a widened region of transparency. This occurs in high-performing n-type transparent conductors (TCs) such as Sn-doped In2O3In2O3 (ITO); however, thus far, the presence of forbidden transitions has been neglected in the search for...

Stability and synthesis across barium tin sulfide material space

Woods-Robinson R
Zakutayev A
Persson KA
2023

Barium tin sulfide (Ba–Sn–S) is a ternary phase space with interesting material candidates for optoelectronic and thermoelectric applications, yet its properties have not been explored in-depth experimentally, and no thin films have been synthesized. This study uses combinatorial sputtering and theoretical calculations to survey the phase space of Ba–Sn–S materials. We experimentally find that at deposition temperatures up to 600 °C, phases of rocksalt-derived BaS structures (Fm...

A method to computationally screen for tunable properties of crystalline alloys

Woods-Robinson R
Persson KA
2023

Conventionally, high-throughput computational materials searches start from an input set of bulk compounds extracted from material databases, but, in contrast, many real functional materials are heavily engineered mixtures of compounds rather than single bulk compounds. We present a framework and open-source code to automatically construct and analyze possible alloys and solid solutions from a set of existing experimental or calculated ordered compounds, without requiring additional metadata except crystal structure. As a demonstration, we apply this framework to all compounds in the...

Chemical reaction networks and opportunities for machine learning

Wen M
Persson KA
2023

Chemical reaction networks (CRNs), defined by sets of species and possible reactions between them, are widely used to interrogate chemical systems. To capture increasingly complex phenomena, CRNs can be leveraged alongside data-driven methods and machine learning (ML). In this Perspective, we assess the diverse strategies available for CRN construction and analysis in pursuit of a wide range of scientific goals, discuss ML techniques currently being applied to CRNs and outline future CRN-ML approaches, presenting scientific and technical challenges to overcome.

Extracting structured seed-mediated gold nanorod growth procedures from scientific text with LLMs

Walker N
Jain A
Persson KA
2023

Although gold nanorods have been the subject of much research, the pathways for controlling their shape and thereby their optical properties remain largely heuristically understood. Although it is apparent that the simultaneous presence of and interaction between various reagents during synthesis control these properties, computational and experimental approaches for exploring the synthesis space can be either intractable or too time-consuming in practice. This motivates an alternative approach leveraging the wealth of synthesis information already embedded in the body of scientific...

Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks

Venetos MC
Wen M
Persson KA
2023

The nuclear magnetic resonance (NMR) chemical shift tensor is a highly sensitive probe of the electronic structure of an atom and furthermore its local structure. Recently, machine learning has been applied to NMR in the prediction of isotropic chemical shifts from a structure. Current machine learning models, however, often ignore the full chemical shift tensor for the easier-to-predict isotropic chemical shift, effectively ignoring a multitude of structural information available in the NMR chemical shift tensor. Here we use an equivariant graph neural network (GNN) to predict full...

Elementary Decomposition Mechanisms of Lithium Hexafluorophosphate in Battery Electrolytes and Interphases

Spotte-Smith EWC
Persson KA
2022

Electrolyte decomposition constitutes an outstanding challenge to long-life Li-ion batteries (LIBs) as well as emergent energy storage technologies, contributing to protection via solid electrolyte interphase (SEI) formation and irreversible capacity loss over a battery’s life. Major strides have been made to understand the breakdown of common LIB solvents; however, salt decomposition mechanisms remain elusive. In this work, we use density functional theory to explain the decomposition of lithium hexafluorophosphate (LiPF6) salt under SEI formation...

A database of molecular properties integrated in the Materials Project

Spotte-Smith EWC
Persson KA
2023

Advanced chemical research is increasingly reliant on large computed datasets of molecules and reactions to discover new functional molecules, understand chemical trends, train machine learning models, and more. To be of greatest use to the scientific community, such datasets should follow FAIR principles (i.e. be findable, accessible, interoperable, and reusable). In this work, we present a FAIR expansion of the Materials Project database (“MPcules”) that adds more than 170...