We are engaged in theory and modeling of materials at the atomic scale. Our recent work has two primary directions:
1. Monolayer and few layer materials (i.e. graphene, MoS2) for electronics, NEMS, and energy applications.
2. Materials at conditions of high temperature, electromagnetic fields, and pressures, including dynamic or shock compression.
Recent research topics include piezoelectricity and phase change effects in monolayer materials. Past topics include THz radiation generation, energetic materials, and photonic crystals. We develop and utilize computational tools (molecular dynamics statistical methods, electronic structure, materials informatics approaches, etc.) and interact closely with experimentalists.
Honors & Awards
ONR Young Investigator Program Award (YIP), Office of Naval Research (2015)
NSF Career Award, NSF (2014)
Young Faculty Award, DARPA (2012)
Robert Noyce Faculty Scholar, Stanford University School of Engineering (2010-2013)
Ernest O. Lawrence Postdoctoral Fellow, Lawrence Livermore National Laboratory (2004 - 2007)
Stanford SystemX Alliance
BS, Caltech, Applied Physics (1998)
PhD, MIT, Physics (2003)
Independent Studies (10)
- Directed Studies in Applied Physics
APPPHYS 290 (Aut, Win, Spr, Sum)
- Graduate Independent Study
MATSCI 399 (Aut, Win, Spr, Sum)
- Master's Research
MATSCI 200 (Aut, Win, Spr, Sum)
- Participation in Materials Science Teaching
MATSCI 400 (Win, Spr)
- Ph.D. Research
MATSCI 300 (Aut, Win, Spr, Sum)
- Practical Training
APPPHYS 291 (Aut, Sum)
- Practical Training
MATSCI 299 (Aut, Win, Spr, Sum)
PHYSICS 490 (Win)
- Undergraduate Independent Study
MATSCI 100 (Aut, Win, Spr, Sum)
- Undergraduate Research
MATSCI 150 (Aut, Win, Spr, Sum)
- Directed Studies in Applied Physics
Prior Year Courses
- Atomic Arrangements in Solids
MATSCI 193 (Spr)
- Atom-based computational methods for materials
MATSCI 331 (Win)
- Atomic Arrangements in Solids
MATSCI 193, MATSCI 203 (Aut)
- Atom-based computational methods for materials
MATSCI 331 (Win)
- Atomic Arrangements in Solids
MATSCI 193, MATSCI 203 (Aut)
- Nanoscale Materials Physics Computation Laboratory
MATSCI 165, MATSCI 175 (Spr)
- Atomic Arrangements in Solids
Doctoral Dissertation Reader (AC)
Kris Brown, Minda Deng, Alex Gabourie, Varun Harbola, Geoff McConohy, Philipp Muscher, Yifan Wang, Hayley Weir, Dante Zakhidov
Doctoral Dissertation Advisor (AC)
Evan Antoniuk, Vincent Dufour Decieux, Eder Lomeli, Akash Ramdas, Brandi Ransom, Yanbing Zhu
Doctoral Dissertation Co-Advisor (AC)
Jaime Avilés Acosta, Sathya Ranjan Chitturi, Amalya Johnson, Jackson Meng, Kyrstyn Ong
Master's Program Advisor
Anchit Narain, Wei Ren
Yi Hu, Brandi Ransom
Novel Ultrabright and Air-Stable Photocathodes Discovered from Machine Learning and Density Functional Theory Driven Screening.
Advanced materials (Deerfield Beach, Fla.)
The high brightness, low emittance electron beams achieved in modern X-ray free-electron lasers (XFELs) have enabled powerful X-ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub-nanometer length scales. One of the most promising directions for increasing the brightness of XFELs is through the development of novel photocathode materials. Whereas past efforts aimed at discovering photocathode materials have typically employed trial-and-error-based iterative approaches, this work represents the first data-driven screening for high brightness photocathode materials. Through screening over 74 000 semiconducting materials, a vast photocathode dataset is generated, resulting in statistically meaningful insights into the nature of high brightness photocathode materials. This screening results in a diverse list of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. In a second effort, multiobjective screening is employed to identify the family of M2 O (M = Na, K, Rb) that exhibits photoemission properties that are comparable to the current state-of-the-art photocathode materials, but with superior air stability. This family represents perhaps the first intrinsically bright, visible light photocathode materials that are resistant to reactions with oxygen, allowing for their transport and storage in dry air environments.
View details for DOI 10.1002/adma.202104081
View details for PubMedID 34510594
Highly Efficient Uniaxial In-Plane Stretching of a 2D Material via Ion Insertion.
Advanced materials (Deerfield Beach, Fla.)
On-chip dynamic strain engineering requires efficient micro-actuators that can generate large in-plane strains. Inorganic electrochemical actuators are unique in that they are driven by low voltages (1V) and produce considerable strains (1%). However, actuation speed and efficiency are limited by mass transport of ions. Minimizing the number of ions required to actuate is thus key to enabling useful "straintronic" devices. Here, it is shown that the electrochemical intercalation of exceptionally few lithium ions into WTe2 causes large anisotropic in-plane strain: 5% in one in-plane direction and 0.1% in the other. This efficient stretching of the 2D WTe2 layers contrasts to intercalation-induced strains in related materials which are predominantly in the out-of-plane direction. The unusual actuation of Lix WTe2 is linked to the formation of a newly discovered crystallographic phase, referred to as Td', with an exotic atomic arrangement. On-chip low-voltage (<0.2V) control is demonstrated over the transition to the novel phase and its composition. Within the Td'-Li0.5- delta WTe2 phase, a uniaxial in-plane strain of 1.4% is achieved with a change of delta of only 0.075. This makes the in-plane chemical expansion coefficient of Td'-Li0.5-delta WTe2 far greater than of any other single-phase material, enabling fast and efficient planar electrochemical actuation.
View details for DOI 10.1002/adma.202101875
View details for PubMedID 34331368
Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations.
The journal of physical chemistry. A
The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parameterized from molecular dynamics (MD) simulations of the complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four orders 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 an 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 parameterize them, a critical feature to describe rare events. 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 time scales 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 parameterized 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.
View details for DOI 10.1021/acs.jpca.1c00942
View details for PubMedID 33973780
- Finding a Needle in a Haystack: Success stories of Data Mining and Machine Learning for Electronic Materials Selection IEEE. 2021
Spectrum of Exfoliable 1D van der Waals Molecular Wires and Their Electronic Properties.
Two-dimensional (2D) materials derived from van der Waals (vdW)-bonded layered crystals have been the subject of considerable research focus, but their one-dimensional (1D) analogues have received less attention. These bulk crystals consist of covalently bonded multiatom atomic chains with weak van der Waals bonds between adjacent chains. Using density-functional-theory-based methods, we find the binding energies of several 1D families of materials to be within typical exfoliation ranges possible for 2D materials. In addition, we compute the electronic properties of a variety of insulating, semiconducting, and metallic individual wires and find differences that could enable the identification of and distinction between 1D, 2D, and 3D forms during mechanical exfoliation onto a substrate. We find 1D wires from chemical families of the forms PdBr2, SbSeI, and GePdS3 are likely to be distinguishable from bulk materials via photoluminescence. Like 2D vdW materials, we find some of these 1D vdW materials have the potential to retain their bulk properties down to nearly atomic film thicknesses, including the structural families of HfI3 and PNF2, a useful property for some applications including electronic interconnects. We also study naturally occurring bulk crystalline heterostructures of 1D wires and identify two families that are likely to be exfoliable and identifiable as individual 1D wire subcomponents.
View details for DOI 10.1021/acsnano.1c00781
View details for PubMedID 34047183
- Generalizable density functional theory based photoemission model for the accelerated development of photocathodes and other photoemissive devices PHYSICAL REVIEW B 2020; 101 (23)
Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning.
2020; 11 (1): 3260
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.
View details for DOI 10.1038/s41467-020-16892-4
View details for PubMedID 32591501
- Quantifying the Search for Solid Li-Ion Electrolyte Materials by Anion: A Data-Driven Perspective JOURNAL OF PHYSICAL CHEMISTRY C 2020; 124 (15): 8067–79
Combining Superionic Conduction and Favorable Decomposition Products in the Crystalline Lithium-Boron-Sulfur System: A New Mechanism for Stabilizing Solid Li-Ion Electrolytes.
ACS applied materials & interfaces
2020; 12 (34): 37957–66
We report a solid-state Li-ion electrolyte predicted to exhibit simultaneously fast ionic conductivity, wide electrochemical stability, low cost, and low mass density. We report exceptional density functional theory (DFT)-based room-temperature single-crystal ionic conductivity values for two phases within the crystalline lithium-boron-sulfur (Li-B-S) system: 62 (+9, -2) mS cm-1 in Li5B7S13 and 80 (-56, -41) mS cm-1 in Li9B19S33. We report significant ionic conductivity values for two additional phases: between 0.0056 and 0.16 mS/cm -1 in Li2B2S5 and between 0.0031 and 9.7 mS cm-1 in Li3BS3 depending on the room-temperature extrapolation scheme used. To our knowledge, our prediction gives Li9B19S33 and Li5B7S13 the second and third highest reported DFT-computed single-crystal ionic conductivities of any crystalline material. We compute the thermodynamic electrochemical stability window widths of these materials to be 0.50 V for Li5B7S13, 0.16 V for Li2B2S5, 0.45 V for Li3BS3, and 0.60 V for Li9B19S33. Individually, these materials exhibit similar or better ionic conductivity and electrochemical stability than the best-known sulfide-based solid-state Li-ion electrolyte materials, including Li10GeP2S12 (LGPS). However, we predict that electrolyte materials synthesized from a range of compositions in the Li-B-S system may exhibit even wider thermodynamic electrochemical stability windows of 0.63 V and possibly as high as 3 V or greater. The Li-B-S system also has a low elemental cost of approximately 0.05 USD/m2 per 10 μm thickness, which is significantly lower than that of germanium-containing LGPS, and a comparable mass density below 2 g/cm3. These fast-conducting phases were initially brought to our attention by a machine learning-based approach to screen over 12,000 solid electrolyte candidates, and the evidence provided here represents an inspiring success for this model.
View details for DOI 10.1021/acsami.9b19091
View details for PubMedID 32700896
Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data.
The Journal of chemical physics
2019; 150 (21): 214701
Machine learning (ML) methods have the potential to revolutionize materials design, due to their ability to screen materials efficiently. Unlike other popular applications such as image recognition or language processing, large volumes of data are not available for materials design applications. Here, we first show that a standard learning approach using generic descriptors does not work for small data, unless it is guided by insights from physical equations. We then propose a novel method for transferring such physical insights onto more generic descriptors, allowing us to screen billions of unknown compositions for Li-ion conductivity, a scale which was previously unfeasible. This is accomplished by using the accurate model trained with physical insights to create a large database, on which we train a new ML model using the generic descriptors. Unlike previous applications of ML, this approach allows us to screen materials which have not necessarily been tested before (i.e., not on ICSD or Materials Project). Our method can be applied to any materials design application where a small amount of data is available, combined with high details of physical understanding.
View details for DOI 10.1063/1.5093220
View details for PubMedID 31176329
Transferable Kinetic Monte Carlo Models with Thousands of Reactions Learned from Molecular Dynamics Simulations.
The journal of physical chemistry. A
Molecular dynamics (MD) simulation of complex chemistry typically involves thousands of atoms propagating over millions of time steps, generating a wealth of data. Traditionally these data are used to calculate some aggregate properties of the system and then discarded, but we propose that these data can be reused to study related chemical systems. Using approximate chemical kinetic models and methods from statistical learning, we study hydrocarbon chemistries under extreme thermodynamic conditions. We discover that a single MD simulation can contain sufficient information about reactions and rates to predict the dynamics of related yet different chemical systems using kinetic Monte Carlo (KMC) simulation. Our learned KMC models identify thousands of reactions and run 4 orders of magnitude faster than MD. The transferability of these models suggests that we can viably reuse data from existing MD simulations to accelerate future simulation studies and reduce the number of new MD simulations required.
View details for PubMedID 30735373
- Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials CHEMISTRY OF MATERIALS 2019; 31 (2): 342–52
ODE integration schemes for plane-wave real-time time-dependent density functional theory.
The Journal of chemical physics
2019; 150 (1): 014101
Integration schemes are implemented with a plane-wave basis in the context of real-time time-dependent density functional theory. Crank-Nicolson methods and three classes of explicit integration schemes are explored and assessed in terms of their accuracy and stability properties. Within the framework of plane-wave density functional theory, a graphene monolayer system is used to investigate the error, stability, and serial computational cost of these methods. The results indicate that Adams-Bashforth and Adams-Bashforth-Moulton methods of orders 4 and 5 outperform commonly used methods, including Crank-Nicolson and Runge-Kutta methods, in simulations where a relatively low error is desired. Parallel runtime scaling of the most competitive serial methods is presented, further demonstrating that the Adams-Bashforth and Adams-Bashforth-Moulton methods are efficient methods for propagating the time-dependent Kohn-Sham equations. Our integration schemes are implemented as an extension to the Quantum ESPRESSO code.
View details for PubMedID 30621412
New Assembly-Free Bulk Layered Inorganic Vertical Heterostructures with Infrared and Optical Bandgaps
2019; 19 (1): 142–49
In principle, a nearly endless number of unique van der Waals heterostructures can be created through the vertical stacking of two-dimensional (2D) materials, resulting in unprecedented potential for material design. However, this widely employed synthetic method for generating van der Waals heterostructures is slow, imprecise, and prone to introducing interlayer contaminants when compared with synthesis methods that are scalable to industrially relevant scales. Herein, we study the properties of a new class of layered bulk inorganic materials that has recently been reported, which we call assembly-free bulk layered inorganic heterostructures, wherein the individual layers are of dissimilar chemical composition, distinguishing them from commonly studied layered materials. We find that these bulk materials exhibit properties similar to vertical heterostructures, but without the complex and unscalable stacking process. Using state-of-the-art computational approaches, we study the electronic properties of livingstonite (HgSb4S8), a naturally occurring mineral that is a bulk lattice-commensurate heterostructure. We find that isolated bilayers of livingstonite have an intralayer HSE-06 band gap of 2.08eV. This is the first report of a naturally occurring van der Waals heterostructure with a calculated band gap in the visible spectrum. We also studied the electronic properties of tetragonal Ti3Bi4O12, Sm2Ti3Bi2O12, orthorhombic Ti3Bi4O12, Nb3Bi5O15, LaTiNbBi2O9 and AgPbBrO and found some of them are potentially well suited for photovoltaic applications. We also provide characterization of the electronic structure of the isolated bilayer and monolayer subcomponents of the bulk heterostructures. The report of the properties of these materials significantly enhances the library of known van der Waals heterostructures.
View details for DOI 10.1021/acs.nanolett.8b03500
View details for Web of Science ID 000455561300017
View details for PubMedID 30525679
Revealing the Spectrum of Unknown Layered Materials with Superhuman Predictive Abilities
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
2018; 9 (24): 6967–72
We discover the chemical composition of over 1000 materials that are likely to exhibit layered and two-dimensional phases but have yet to be synthesized. This includes two materials our calculations indicate can exist in distinct structures with different band gaps, expanding the short list of two-dimensional phase change materials. While databases of over 1000 layered materials have been reported, we provide the first full database of materials that are likely layered but yet to be synthesized, providing a roadmap for the synthesis community. We accomplish this by combining physics with machine learning on experimentally obtained data and verify a subset of candidates using density functional theory. We find our model performs five times better than practitioners in the field at identifying layered materials and is comparable or better than professional solid-state chemists. Finally, we find that semi-supervised learning can offer benefits for materials design where labels for some of the materials are unknown.
View details for PubMedID 30481462
- Refrigeration in 2D: Electrostaticaloric effect in monolayer materials PHYSICAL REVIEW MATERIALS 2018; 2 (11)
- Microscopic Origins of the Variability of Water Contact Angle with Adsorbed Contaminants on Layered Materials JOURNAL OF PHYSICAL CHEMISTRY C 2018; 122 (32): 18520–27
Metallic Metal-Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
2018; 9 (16): 4562–69
Emerging applications of metal-organic frameworks (MOFs) in electronic devices will benefit from the design and synthesis of intrinsically, highly electronically conductive MOFs. However, very few are known to exist. It is a challenging task to search for electronically conductive MOFs within the tens of thousands of reported MOF structures. Using a new strategy (i.e., transfer learning) of combining machine learning techniques, statistical multivoting, and ab initio calculations, we screened 2932 MOFs and identified 6 MOF crystal structures that are metallic at the level of semilocal DFT band theory: Mn2[Re6X8(CN)6]4 (X = S, Se,Te), Mn[Re3Te4(CN)3], Hg[SCN]4Co[NCS]4, and CdC4. Five of these structures have been synthesized and reported in the literature, but their electrical characterization has not been reported. Our work demonstrates the potential power of machine learning in materials science to aid in down-selecting from large numbers of potential candidates and provides the information and guidance to accelerate the discovery of novel advanced materials.
View details for PubMedID 30052453
- The potential for fast van der Waals computations for layered materials using a Lifshitz model 2D MATERIALS 2017; 4 (2)
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.
View details for DOI 10.1021/acs.nanolett.6b05229
View details for PubMedID 28191965
- Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials ENERGY & ENVIRONMENTAL SCIENCE 2017; 10 (1): 306-320
Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics.
2017; 8 (8): 5781–96
We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD). Existing approaches for identifying species and reactions from molecular dynamics typically use bond length and duration criteria, where bond duration is a fixed parameter motivated by an understanding of bond vibrational frequencies. In contrast, we show that for highly reactive systems, bond duration should be a model parameter that is chosen to maximize the predictive power of the resulting statistical model. We demonstrate our method on a high temperature, high pressure system of reacting liquid methane, and show that the learned KMC model is able to extrapolate more than an order of magnitude in time for key molecules. Additionally, our KMC model of elementary reactions enables us to isolate the most important set of reactions governing the behavior of key molecules found in the MD simulation. We develop a new data-driven algorithm to reduce the chemical reaction network which can be solved either as an integer program or efficiently using L1 regularization, and compare our results with simple count-based reduction. For our liquid methane system, we discover that rare reactions do not play a significant role in the system, and find that less than 7% of the approximately 2000 reactions observed from molecular dynamics are necessary to reproduce the molecular concentration over time of methane. The framework described in this work paves the way towards a genomic approach to studying complex chemical systems, where expensive MD simulation data can be reused to contribute to an increasingly large and accurate genome of elementary reactions and rates.
View details for PubMedID 28989618
- Quantum Nuclear Effects in Stishovite Crystallization in Shock-Compressed Fused Silica JOURNAL OF PHYSICAL CHEMISTRY C 2016; 120 (31): 17759-17766
Structural Phase Transitions by Design in Monolayer Alloys.
2016; 10 (1): 289-297
Two-dimensional monolayer materials are a highly anomalous class of materials under vigorous exploration. Mo- and W-dichalcogenides are especially unusual two-dimensional materials because they exhibit at least three different monolayer crystal structures with strongly differing electronic properties. This intriguing yet poorly understood feature, which is not present in graphene, may support monolayer phase engineering, phase change memory and other applications. However, knowledge of the relevant phase boundaries and how to engineer them is lacking. Here we show using alloy models and state-of-the-art density functional theory calculations that alloyed MoTe2-WTe2 monolayers support structural phase transitions, with phase transition temperatures tunable over a large range from 0 to 933 K. We map temperature-composition phase diagrams of alloys between pure MoTe2 and pure WTe2, and benchmark our methods to analogous experiments on bulk materials. Our results suggest applications for two-dimensional materials as phase change materials that may provide scale, flexibility, and energy consumption advantages.
View details for DOI 10.1021/acsnano.5b04359
View details for PubMedID 26647117
Structural semiconductor-to-semimetal phase transition in two-dimensional materials induced by electrostatic gating.
2016; 7: 10671-?
Dynamic control of conductivity and optical properties via atomic structure changes is of technological importance in information storage. Energy consumption considerations provide a driving force towards 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. By developing new density functional-based methods, we discover that electrostatic gating device configurations have the potential to drive structural semiconductor-to-semimetal phase transitions in some monolayer transition metal dichalcogenides. Here we show 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. We find that the transition gate voltage can be reduced arbitrarily by alloying, for example, for MoxW1-xTe2 monolayers. 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.
View details for DOI 10.1038/ncomms10671
View details for PubMedID 26868916
View details for PubMedCentralID PMC4754345
- Nanosecond homogeneous nucleation and crystal growth in shock-compressed SiO2 NATURE MATERIALS 2016; 15 (1): 60-?
- Piezoelectricity: Now in two dimensions. Nature nanotechnology 2015; 10 (2): 106-107
Strain engineering in monolayer materials using patterned adatom adsorption.
2014; 14 (8): 4299-4305
We utilize reactive empirical bond order (REBO)-based interatomic potentials to explore the potential for the engineering of strain in monolayer materials using lithographically or otherwise patterned adatom adsorption. In the context of graphene, we discover that the monolayer strain results from a competition between the in-plane elasticity and out-of-plane relaxation deformations. For hydrogen adatoms on graphene, the strain outside the adsorption region vanishes due to out-of-plane relaxation deformations. Under some circumstances, an annular adsorption pattern generates homogeneous tensile strains of approximately 2% in graphene inside the adsorption region, approximately 30% of the strain in the adsorbed region. We find that an elliptical adsorption pattern produces strains of as large as 5%, close to the strain in the adsorbed region. Also, nonzero maximum shear strain (∼4%) can be introduced by the elliptical adsorption pattern. We find that an elastic plane stress model provides qualitative guidance for strain magnitudes and conditions under which strain-diminishing buckling can be avoided. We identify geometric conditions under which this effect has potential to be scaled to larger areas. Our results elucidate a method for strain engineering at the nanoscale in monolayer devices.
View details for DOI 10.1021/nl500974t
View details for PubMedID 25051232
Structural phase transitions in two-dimensional Mo- and W-dichalcogenide monolayers.
2014; 5: 4214-?
Mo- and W-dichalcogenide compounds have a two-dimensional monolayer form that differs from graphene in an important respect: it can potentially have more than one crystal structure. Some of these monolayers exhibit tantalizing hints of a poorly understood structural metal-to-insulator transition with the possibility of long metastable lifetimes. If controllable, such a transition could bring an exciting new application space to monolayer materials beyond graphene. Here we discover that mechanical deformations provide a route to switching thermodynamic stability between a semiconducting and a metallic crystal structure in these monolayer materials. Based on state-of-the-art density functional and hybrid Hartree-Fock/density functional calculations including vibrational energy corrections, we discover that MoTe2 is an excellent candidate phase change material. We identify a range from 0.3 to 3% for the tensile strains required to transform MoTe2 under uniaxial conditions at room temperature. The potential for mechanical phase transitions is predicted for all six studied compounds.
View details for DOI 10.1038/ncomms5214
View details for PubMedID 24981779
Flexural Electromechanical Coupling: A Nanoscale Emergent Property of Boron Nitride Bilayers
2013; 13 (4): 1681-1686
The symmetry properties of atomically thin boron nitride (BN) monolayers endow them with piezoelectric properties, whereas the bulk parent crystal of stacked BN layers is not piezoelectric. This suggests potential for unusual electromechanical properties in the few layer regime. In this work, we explore this regime and discover that a bilayer consisting of two BN monolayers exhibits a strong mechanical coupling between curvature and electric fields. Using a mechanical model with parameters obtained from density functional theory, we find that these bilayers amplify in-plane piezoelectric displacements by exceedingly large factors on the order of 10(3)-10(4). We find that this type of electromechanical coupling is an emergent nanoscale property that occurs only for the case of two stacked BN monolayers.
View details for DOI 10.1021/nl4001635
View details for Web of Science ID 000317549300051
View details for PubMedID 23484488
Simulations of Shocked Methane Including Self-Consistent Semiclassical Quantum Nuclear Effects
JOURNAL OF PHYSICAL CHEMISTRY A
2012; 116 (42): 10451-10459
A methodology is described for atomistic simulations of shock-compressed materials that incorporates quantum nuclear effects on the fly. We introduce a modification of the multiscale shock technique (MSST) that couples to a quantum thermal bath described by a colored noise Langevin thermostat. The new approach, which we call QB-MSST, is of comparable computational cost to MSST and self-consistently incorporates quantum heat capacities and Bose-Einstein harmonic vibrational distributions. As a first test, we study shock-compressed methane using the ReaxFF potential. The Hugoniot curves predicted from the new approach are found comparable with existing experimental data. We find that the self-consistent nature of the method results in the onset of chemistry at 40% lower pressure on the shock Hugoniot than observed with classical molecular dynamics. The temperature shift associated with quantum heat capacity is determined to be the primary factor in this shift.
View details for DOI 10.1021/jp308068c
View details for Web of Science ID 000310120800022
View details for PubMedID 23013329
- Intrinsic Piezoelectricity in Two-Dimensional Materials JOURNAL OF PHYSICAL CHEMISTRY LETTERS 2012; 3 (19): 2871-2876
Ultrafast Detonation of Hydrazoic Acid (HN3)
PHYSICAL REVIEW LETTERS
2012; 109 (3)
The fastest self-sustained chemical reactions in nature occur during detonation of energetic materials where reactions are thought to occur on nanosecond or longer time scales in carbon-containing materials. Here we perform the first atomistic simulation of an azide energetic material, HN3, from the beginning to the end of the chemical evolution and find that the time scale for complete decomposition is a mere 10 ps, orders of magnitude shorter than that of secondary explosives and approaching the fundamental limiting time scale for chemistry; i.e., vibrational time scale. We study several consequences of the short time scale including a state of vibrational disequilibrium induced by the fast transformations.
View details for DOI 10.1103/PhysRevLett.109.038301
View details for Web of Science ID 000306466900025
View details for PubMedID 22861903
Engineered Piezoelectricity in Graphene
2012; 6 (2): 1387-1394
We discover that piezoelectric effects can be engineered into nonpiezoelectric graphene through the selective surface adsorption of atoms. Our calculations show that doping a single sheet of graphene with atoms on one side results in the generation of piezoelectricity by breaking inversion symmetry. Despite their 2D nature, piezoelectric magnitudes are found to be comparable to those in 3D piezoelectric materials. Our results elucidate a designer piezoelectric phenomenon, unique to the nanoscale, that has potential to bring dynamical control to nanoscale electromechanical devices.
View details for DOI 10.1021/nn204198g
View details for Web of Science ID 000300757900046
View details for PubMedID 22196055
- Electron-Ion Coupling in Shocked Energetic Materials JOURNAL OF PHYSICAL CHEMISTRY C 2012; 116 (3): 2205-2211
Observation of terahertz radiation coherently generated by acoustic waves.
2009; 5: 285-288
View details for DOI 10.1038/nphys1219
Reversible Electrochemical Phase Change in Monolayer to Bulk-like 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 to bulk-like case (73 nm) 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 topmost 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 formation. This finding suggests the formation of Te vacancies which have been reported to lower the energy difference between the 2H and 1T' phases, potentially aiding the phase change process. Our discovery that the phase change can be achieved on the surface layer of bulk-like 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.
View details for DOI 10.1021/acsnano.9b07095
View details for PubMedID 32045212
- Theoretical potential for low energy consumption phase change memory utilizing electrostatically-induced structural phase transitions in 2D materials NPJ COMPUTATIONAL MATERIALS 2018; 4
COMPUTATIONAL MATERIALS SCIENCE Two-dimensional tellurium
2017; 552 (7683): 1–2
View details for Web of Science ID 000417560500013
Structural phase transition in monolayer MoTe2 driven by electrostatic doping
2017; 550 (7677): 487-+
Monolayers of transition-metal dichalcogenides (TMDs) exhibit numerous crystal phases with distinct structures, symmetries and physical properties. Exploring the physics of transitions between these different structural phases in two dimensions may provide a means of switching material properties, with implications for potential applications. Structural phase transitions in TMDs have so far been induced by thermal or chemical means; purely electrostatic control over crystal phases through electrostatic doping was recently proposed as a theoretical possibility, but has not yet been realized. Here we report the experimental demonstration of an electrostatic-doping-driven phase transition between the hexagonal and monoclinic phases of monolayer molybdenum ditelluride (MoTe2). We find that the phase transition shows a hysteretic loop in Raman spectra, and can be reversed by increasing or decreasing the gate voltage. We also combine second-harmonic generation spectroscopy with polarization-resolved Raman spectroscopy to show that the induced monoclinic phase preserves the crystal orientation of the original hexagonal phase. Moreover, this structural phase transition occurs simultaneously across the whole sample. This electrostatic-doping control of structural phase transition opens up new possibilities for developing phase-change devices based on atomically thin membranes.
View details for PubMedID 29019982
Statistical learning of kinetic Monte Carlo models of high temperature chemistry from molecular dynamics
AMER CHEMICAL SOC. 2017
View details for Web of Science ID 000429525603636
Hundreds of new two- and one-dimensional weakly bonded solids and lattice-commensurate heterostructures via data mining
AMER CHEMICAL SOC. 2017
View details for Web of Science ID 000429525604399
2017; 11 (1): 900-905
Chemical vapor deposition allows the preparation of few-layer films of MoTe2 in three distinct structural phases depending on the growth quench temperature: 2H, 1T', and 1T. We present experimental and computed Raman spectra for each of the phases and utilize transport measurements to explore the properties of the 1T MoTe2 phase. Density functional theory modeling predicts a (semi-)metallic character. Our experimental 1T films affirm the former, show facile μA-scale source-drain currents, and increase in conductivity with temperature, different from the 1T' phase. Variation of the growth method allows the formation of hybrid films of mixed phases that exhibit susceptibility to gating and significantly increased conductivity.
View details for DOI 10.1021/acsnano.6b07499
View details for PubMedID 27992719
Ultrafast electronic and structural response of monolayer MoS2 under intense photoexcitation conditions.
2014; 8 (10): 10734-10742
We report on the dynamical response of single layer transition metal dichalcogenide MoS2 to intense above-bandgap photoexcitation using the nonlinear-optical second order susceptibility as a direct probe of the electronic and structural dynamics. Excitation conditions corresponding to the order of one electron-hole pair per unit cell generate unexpected increases in the second harmonic from monolayer films, occurring on few picosecond time-scales. These large amplitude changes recover on tens of picosecond time-scales and are reversible at megahertz repetition rates with no photoinduced change in lattice symmetry observed despite the extreme excitation conditions.
View details for DOI 10.1021/nn5044542
View details for PubMedID 25244589