arXiv subjects

Evan J. Reed

Publications and source records attributed to Evan J. Reed.

12 recordsLinked to original sources

arXiv2024

Discovery of stable surfaces with extreme work functions by high-throughput density functional theory and machine learning

The work function is the key surface property that determines how much energy is required for an electron to escape the surface of a material. This property is crucial for thermionic energy conversion, band alignment in heterostructures, and electron emission devices. Here, we present a high-throughput workflow using density functional theory (DFT) to calculate the work function and cleavage energy of 33,631 slabs (58,332 work functions) that we created from 3,716 bulk materials, including up to ternary compounds. The number of materials for which we calculated surface properties surpasses the previously largest database, the Materials Project, by a factor of $\sim$27. On the tail ends of the work function distribution we identify 34 and 56 surfaces with an ultra-low (<2 eV) and ultra-high (>7 eV) work function, respectively. Further, we discover that the $(100)$-Ba-O surface of BaMoO$_3$ and the $(001)$-F surface of Ag$_2$F have record-low (1.25 eV) and record-high (9.06 eV) steady-state work functions without requiring coatings, respectively. Based on this database we develop a physics-based approach to featurize surfaces and use supervised machine learning to predict the work function. We find that physical choice of features improves prediction performance far more than choice of model. Our random forest model achieves a mean absolute test error of 0.09 eV, which is more than 6 times better than the baseline and comparable to the accuracy of DFT. This surrogate model enables rapid predictions of the work function ($\sim 10^5$ faster than DFT) across a vast chemical space and facilitates the discovery of material surfaces with extreme work functions for energy conversion, electronic applications, and contacts in 2-dimensional devices.

cond-mat.mtrl-sci↗

arXiv2023

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.

cond-mat.mtrl-sci↗

arXiv2022

Predicting Molecule Size Distribution in Hydrocarbon Pyrolysis using Random Graph Theory

Hydrocarbon pyrolysis is a complex process involving large numbers of chemical species and types of chemical reactions. Its quantitative description is important for planetary sciences, in particular, for understanding the processes occurring in the interior of icy planets, such as Uranus and Neptune, where small hydrocarbons are subjected to high temperature and pressure. We propose a computationally cheap methodology based on an originally developed ten-reaction model, and the configurational model from random graph theory. This methodology yields to accurate predictions for molecule size distributions for a variety of initial chemical compositions and temperatures ranging from 3200K to 5000K. Specifically, we show that the size distribution of small molecules is particularly well predicted, and the size of the largest molecule can be accurately predicted provided that it is not too large.

physics.atm-clus↗

arXiv2022

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.

cond-mat.mtrl-sci↗

arXiv2021

Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parametrized from MD simulations of complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four order of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example in the case of large molecules or solids. Here we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parametrize them. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation timescales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parametrized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.

physics.comp-ph↗

arXiv2020

Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning

The process of crystallization is often understood in terms of the fundamental microstructural elements of the crystallite being formed, such as surface orientation or the presence of defects. Considerably less is known about the role of the liquid structure on the kinetics of crystal growth. Here atomistic simulations and machine learning methods are employed together to demonstrate that the liquid adjacent to solid-liquid interfaces presents significant structural ordering, which effectively reduces the mobility of atoms and slows down the crystallization kinetics. Through detailed studies of silicon and copper we discover that the extent to which liquid mobility is affected by interface-induced ordering (IIO) varies greatly with the degree of ordering and nature of the adjacent interface. Physical mechanisms behind the IIO anisotropy are explained and it is demonstrated that incorporation of this effect on a physically-motivated crystal growth model enables the quantitative prediction of the growth rate temperature dependence.

cond-mat.mtrl-sci↗

arXiv2019

Reversible Electrochemical Phase Change in Monolayer to Bulk MoTe2 by Ionic Liquid Gating

Transition metal dichalcogenides (TMDs) exist in various crystal structures with semiconducting, semi-metallic, and metallic properties. The dynamic control of these phases is of immediate interest for next generation electronics such as phase change memories. Of the binary Mo and W-based TMDs, MoTe2 is attractive for electronic applications because it has the lowest energy difference (40 meV) between the semiconducting (2H) and semi-metallic (1T') phases, allowing for MoTe2 phase change by electrostatic doping. Here we report phase change between the 2H and 1T' polymorphs of MoTe2 in thicknesses ranging from the monolayer case to effective bulk (73nm) using an ionic liquid electrolyte at room temperature and in air. We find consistent evidence of a partially reversible 2H-1T' transition using in-situ Raman spectroscopy where the phase change occurs in the top-most layers of the MoTe2 flake. We find a thickness-dependent transition voltage where higher voltages are necessary to drive the phase change for thicker flakes. We also show evidence of electrochemical activity during the gating process by observation of Te metal deposition. This finding suggests the formation of Te vacancies which have been reported to lower the energy difference between the 2H and 1T' phase, potentially aiding the phase change process. Our discovery that the phase change can be achieved on the surface layer of bulk materials reveals that this electrochemical mechanism does not require isolation of a single layer and the effect may be more broadly applicable than previously thought.

cond-mat.mtrl-sci↗

arXiv2019

Quantifying the search for solid Li-ion electrolyte materials by anion: a data-driven perspective

We compile data and machine learned models of solid Li-ion electrolyte performance to assess the state of materials discovery efforts and build new insights for future efforts. Candidate electrolyte materials must satisfy several requirements, chief among them fast ionic conductivity and robust electrochemical stability. Considering these two requirements, we find new evidence to suggest that optimization of the sulfides for fast ionic conductivity and wide electrochemical stability may be more likely than optimization of the oxides, and that the oft-overlooked chlorides and bromides may be particularly promising families for Li-ion electrolytes. We also find that the nitrides and phosphides appear to be the most promising material families for electrolytes stable against Li-metal anodes. Furthermore, the spread of the existing data in performance space suggests that fast conducting materials that are stable against both Li metal and a >4V cathode are exceedingly rare, and that a multiple-electrolyte architecture is a more likely path to successfully realizing a solid-state Li metal battery by approximately an order of magnitude or more. Our model is validated by its reproduction of well-known trends that have emerged from the limited existing data in recent years, namely that the electronegativity of the lattice anion correlates with ionic conductivity and electrochemical stability. In this work, we leverage the existing data to make solid electrolyte performance trends quantitative for the first time, building a roadmap to complement material discovery efforts around desired material performance.

cond-mat.mtrl-sci↗

arXiv2019

Low Resistivity and High Breakdown Current Density of 10-nm Diameter van der Waals TaSe3 Nanowires by Chemical Vapor Deposition

Micron-scale single-crystal nanowires of metallic TaSe3, a material that forms -Ta-Se3-Ta-Se3- stacks separated from one another by a tubular van der Waals (vdW) gap, have been synthesized using chemical vapor deposition (CVD) on a SiO2/Si substrate, in a process compatible with semiconductor industry requirements. Their electrical resistivity was found unaffected by downscaling from the bulk to as little as 7 nm in width and height, in striking contrast to the resistivity of copper for the same dimensions. While the bulk resistivity of TaSe3 is substantially higher than that of bulk copper, at the nanometer scale the TaSe3 wires become competitive to similar-sized copper ones. Moreover, we find that the vdW TaSe3 nanowires sustain current densities in excess of 108 A/cm2 and feature an electromigration energy barrier twice that of copper. The results highlight the promise of quasi-one-dimensional transition metal trichalcogenides for electronic interconnect applications and the potential of van der Waals materials for downscaled electronics.

cond-mat.mtrl-sci↗

arXiv2018

Machine learning-assisted discovery of many new solid Li-ion conducting materials

We discover many new crystalline solid materials with fast single crystal Li ion conductivity at room temperature, discovered through density functional theory simulations guided by machine learning-based methods. The discovery of new solid Li superionic conductors is of critical importance to the development of safe all-solid-state Li-ion batteries. With a predictive universal structure-property relationship for fast ion conduction not well understood, the search for new solid Li ion conductors has relied largely on trial-and-error computational and experimental searches over the last several decades. In this work, we perform a guided search of materials space with a machine learning (ML)-based prediction model for material selection and density functional theory molecular dynamics (DFT-MD) simulations for calculating ionic conductivity. These materials are screened from over 12,000 experimentally synthesized and characterized candidates with very diverse structures and compositions. When compared to a random search of materials space, we find that the ML-guided search is 2.7 times more likely to identify fast Li ion conductors, with at least a 45x improvement in the log-average of room temperature Li ion conductivity. The F1 score of the ML-based model is 0.50, 3.5 times better than the F1 score expected from completely random guesswork. In a head-to-head competition against six Ph.D. students working in the field, we find that the ML-based model doubles the F1 score of human experts in its ability to identify fast Li-ion conductors from atomistic structure with a thousand- fold increase in speed, clearly demonstrating the utility of this model for the research community. All conducting materials reported here lack transition metals and are predicted to exhibit low electronic conduction, high stability against oxidation, and high thermodynamic stability.

cond-mat.mtrl-sci↗

arXiv2016

A Review on Mechanics and Mechanical Properties of 2D Materials - Graphene and Beyond

Since the first successful synthesis of graphene just over a decade ago, a variety of two-dimensional (2D) materials (e.g., transition metal-dichalcogenides, hexagonal boron-nitride, etc.) have been discovered. Among the many unique and attractive properties of 2D materials, mechanical properties play important roles in manufacturing, integration and performance for their potential applications. Mechanics is indispensable in the study of mechanical properties, both experimentally and theoretically. The coupling between the mechanical and other physical properties (thermal, electronic, optical) is also of great interest in exploring novel applications, where mechanics has to be combined with condensed matter physics to establish a scalable theoretical framework. Moreover, mechanical interactions between 2D materials and various substrate materials are essential for integrated device applications of 2D materials, for which the mechanics of interfaces (adhesion and friction) has to be developed for the 2D materials. Here we review recent theoretical and experimental works related to mechanics and mechanical properties of 2D materials. While graphene is the most studied 2D material to date, we expect continual growth of interest in the mechanics of other 2D materials beyond graphene.

cond-mat.mes-hall↗

arXiv2016

Structural Semiconductor-to-Semimetal Phase Transition in Two-Dimensional Materials Induced by Electrostatic Gating

Dynamic control of conductivity and optical properties via atomic structure changes is of tremendous technological importance in information storage. Energy consumption considerations provide a driving force toward employing thin materials in devices. Monolayer transition metal dichalcogenides are nearly atomically-thin materials that can exist in multiple crystal structures, each with distinct electrical properties. Using density functional approaches, we discover that electrostatic gating device configurations have the potential to drive structural semiconductor-to-semimetal phase transitions in some monolayer transition metal dichalcogenides. For the first time, we show that the dynamical control of this phase transition can be achieved in carefully designed electronic devices. We discover that the semiconductor-to-semimetal phase transition in monolayer MoTe2 can be driven by a gate voltage of several Volts with appropriate choice of dielectric. Structural transitions in monolayer TaSe2 are predicted to occur under similar conditions. While the required field magnitudes are large for these two materials, we find that the gate voltage for the transition can be reduced arbitrarily by alloying, e.g. for MoxW1-xTe2 monolayers. We have developed a method for computing phase diagrams of monolayer materials with respect to charge and voltage, validated by comparing to direct calculations and experimental measurements. Our findings identify a new physical mechanism, not existing in bulk materials, to dynamically control structural phase transitions in two-dimensional materials, enabling potential applications in phase-change electronic devices.

cond-mat.mtrl-sci↗