A5045742925- Machine Learning in Materials Science
- Synthesis and Properties of Aromatic Compounds
- Fullerene Chemistry and Applications
- Computational Drug Discovery Methods
- Inorganic Chemistry and Materials
- Boron and Carbon Nanomaterials Research
- Advanced Chemical Physics Studies
- Protein Structure and Dynamics
- Spectroscopy and Quantum Chemical Studies
- MXene and MAX Phase Materials
- Graphene research and applications
- Molecular Junctions and Nanostructures
- 2D Materials and Applications
- X-ray Diffraction in Crystallography
- Crystallization and Solubility Studies
- Quantum, superfluid, helium dynamics
- Analytical Chemistry and Chromatography
- Mass Spectrometry Techniques and Applications
- Graph theory and applications
- Organometallic Complex Synthesis and Catalysis
- Crystal Structures and Properties
- Supramolecular Chemistry and Complexes
- Photochemistry and Electron Transfer Studies
- Ammonia Synthesis and Nitrogen Reduction
- Nanocluster Synthesis and Applications
Los Alamos National Laboratory
2021-2025
Center for Integrated Nanotechnologies
2023
Utah State University
2019-2022
Brown University
2022
Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active (AL) is a powerful tool iteratively generate diverse sets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration. If passes certain threshold, then configuration included in set. Here we develop strategy more rapidly discover configurations that meaningfully augment...
Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination computational efficiency physical accuracy. This Perspective summarizes some recent advances in the development neural network-based interatomic potentials. Designing training crucial to overall model One strategy active learning, which...
Aromaticity serves as a kinetic trap for mechanically interlocked cyclo[18]carbon rings.
We present NEXMD version 2.0, the second release of (Nonadiabatic EXcited-state Molecular Dynamics) software package. Across a variety new features, v2.0 incorporates implementations two hybrid quantum-classical dynamics methods, namely, Ehrenfest (EHR) and Ab-Initio Multiple Cloning sampling technique for Multiconfigurational quantum (MCE-AIMC or simply AIMC), which are alternative options to previously implemented trajectory surface hopping (TSH) method. To illustrate these methodologies,...
Investigations of inorganic anion SO42– interactions with water are crucial for understanding the chemistry its aqueous solutions. It is known that isolated dianion unstable, and three H2O molecules required stabilization. In current work, we report our computational study hydrated sulfate clusters SO42–(H2O)n (n = 1–40) in order to understand nature stabilization this important by molecules. We showed most significant charge transfer from takes place at a number n ≤ 7. The directly donates...
We present an extended Lagrangian shadow molecular dynamics scheme with interatomic Born-Oppenheimer potential determined by the relaxed atomic charges of a second-order charge equilibration model. To parameterize model, we use machine learning neural networks to determine environment-dependent electronegativities and chemical hardness parameters for each atom, in addition charge-independent energy force terms. The approximate combination formulation improves numerical stability reduces...
We present an extended Lagrangian shadow molecular dynamics scheme with interatomic Born-Oppenheimer potential determined by the relaxed atomic charges of a second-order charge equilibration model. To parametrize model, we use machine learning neural networks to determine environment-dependent electronegativities and chemical hardness parameters for each atom, in addition charge-independent energy force terms. The approximate combination formulation improves numerical stability reduces...
Molybdenum disulfide is a prominent semiconductor that received lot of attention recently due to its unique properties. Possessing layered crystal structure, one can tune the band gap from indirect direct while transforming material bulk monolayer structure. MoS2 highly researched material. However, not all nature this intriguing revealed yet. The aim work deepen knowledge scientific community about by deciphering bonding picture. As shown using SSAdNDP approach, Mo and S form 2-centered...
Structural characterization of the discrete [Sn@Cu12 @Sn20 ]12- cluster exposed a fascinating architecture composed three concentric structural layers in which an endohedral Sn atom is enclosed Cu12 icosahedron, turn embedded Sn20 dodecahedron. Herein, possibility sustaining aromatic behavior for this prototypical multilayered species was evaluated, order to extend concept more complex clusters on basis magnetic response and bonding analysis by AdNDP approach. This revealed characteristic...
Because of its low toxicity, bismuth is considered to be a "green metal" and has received increasing attention in chemistry materials science. To understand the chemical bonding bismuth, here we report joint experimental theoretical study on series bismuth-doped boron clusters, BiBn– (n = 6–8). Well-resolved photoelectron spectra are obtained used structures conjunction with calculations. Global minimum searches find that all three clusters have planar Bi atom bonded edge Bn moiety via two...
Copper has been found to be able mediate the formation of bilayer borophenes. Copper–boron binary clusters are ideal model systems probe copper–boron interactions, which essential understand growth mechanisms borophenes on copper substrates. Here, we report a joint photoelectron spectroscopy and theoretical study two di-copper-doped boron clusters: Cu2B3– Cu2B4–. Well-resolved spectra obtained, revealing presence low-lying isomer in both cases. Theoretical calculations show that global...
Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when model is extrapolated to new regions of chemical space, e.g., bonding types, many-body interactions. Another important limitation spatial locality assumption architecture, and this cannot overcome with larger or more diverse datasets. The outlined challenges are primarily associated lack electronic structure information...
Abstract In a high‐resolution photoelectron imaging and theoretical study of the IrB 3 − cluster, two isomers were observed experimentally with electron affinities (EAs) 1.3147(8) 1.937(4) eV. Quantum calculations revealed nearly degenerate competing for global minimum, both B ring coordinated Ir atom. The isomer higher EA consists bridge‐bonded atom ( C s , 2 A′), second features tetrahedral structure v A 1 ). neutral was predicted to be considerably more stable than all other isomers....
Borophenes are atom-thin boron layers that can be grown on coinage metal substrates and have become an important class of synthetic 2D nanomaterials. The interactions between critical to understand the growth mechanisms borophenes. Here, we report investigation copper-boron in Cu2B8− bimetallic cluster using photoelectron spectroscopy quantum chemical calculations. Well-resolved spectra obtained at several photon energies combined with theoretical calculations elucidate structures bonding...
Growing demands of material science and, in particular, the field nonlinear optics (NLO) encourage us to look for stable highly polarizable molecules with excess diffuse electrons. An unusual class compounds called electrides comply these requirements. Many attempts have been made, yet only few synthesized as solids and none them molecular species. In this paper, a new theoretically designed species electride characteristics is reported. The idea comes from formation electride-like features...
Following an ongoing interest in the study of transition metal complexes with exotic bonding networks, we report herein synthesis a family heterobimetallic triangular clusters involving Ru and Pd atoms. These are first examples trinuclear combining these nuclei. Structural analyses revealed both analogies unexpected differences for [Pd2Ru]+ compared to their parent [Pd3]+ peers. Noticeably, participation atom π-aromaticity coordinated benzene ring makes synthesized compound second reported...
Abstract Synthetic exploration and theoretical characterization of metal clusters are actively developing branches modern inorganic chemistry. Advances in these areas constantly expand the rich structural diversity viable species, allowing a detailed study fundamental characteristics bench‐stable compounds. In this minireview, we summarize recent achievements synthesis computational analyses main‐group heterometallic containing multiple aromatic/antiaromatic units. These systems range from...
The strong relativistic effects result in many interesting chemical and physical properties for gold compounds. One of the most surprising findings has been that small clusters prefer planar structures. Dopants can be used to tune electronic structural nanoclusters. Here we report an experimental theoretical investigation a Zn-doped cluster, Au9Zn–. Photoelectron spectroscopy reveals Au9Zn– is highly stable system with electron binding energy 4.27 eV. Quantum studies show global minimum D3h...
Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) in its most recent shadow potential energy version has been implemented the semiempirical PyTorch-based software PySeQM. The implementation includes finite electronic temperatures, canonical density matrix perturbation theory, and an adaptive Krylov subspace approximation for integration of equations motion within XL-BOMB approach (KSA-XL-BOMD). PyTorch leverages use GPU machine learning hardware accelerators simulations. new...