Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

High-Yield WS2 Synthesis through Sulfurization in Custom-Modified Atmospheric Pressure Chemical Vapor Deposition Reactor, Paving the Way for Selective NH3 Vapor Detection

Nanostructured transition metal dichalcogenides have garnered significant research interest for physical and chemical sensing applications due to their unique crystal structure and large effective surface area. However, the high-yield synthesis of these materials on different substrates and in nanostructured films remains a challenge that hinders their real-world applications. In this work, we demonstrate the synthesis of two-dimensional (2D) tungsten disulfide (WS2) sheets on a hundred-milligram scale by sulfurization of tungsten trioxide (WO3) powder in an atmospheric pressure chemical vapor deposition reactor. The as-synthesized WS2 powders can be formulated into inks and deposited on a broad range of substrates using techniques like screen or inkjet printing, spin-coating, drop-casting, or airbrushing. Structural, morphological, and chemical composition analysis confirm the successful synthesis of edge-enriched WS2 sheets. The sensing performance of the WS2 films prepared with the synthesized 2D material was evaluated for ammonia (NH3) detection at different operating temperatures. The results reveal exceptional gas sensing responses, with the sensors showing a 100% response toward 5 ppm of NH3 at 150 °C. The sensor detection limit was experimentally verified to be below 1 ppm of NH3 at 150 °C. Selectivity tests demonstrated the high selectivity of the edge-enriched WS2 films toward NH3 in the presence of interfering gases like CO, benzene, H2, and NO2. Furthermore, the sensors displayed remarkable stability against high levels of humidity, with only a slight decrease in response from 100% in dry air to 93% in humid environments. Density functional theory and Bayesian optimization simulations were performed, and the theoretical results agree with the experimental findings, revealing that the interaction between gas molecules and WS2 is primarily based on physisorption.

First-Principles Structure Search Study of 17-β-Estradiol Adsorption on Graphene

17-Beta-estradiol (E2), a steroid hormone synthesized from cholesterol, has various impacts on health and the environment. Currently, the gold standard for its measurement in the body is a conventional blood test (mass spectrometry), but carbon-based electrochemical sensors have been proposed as an alternative due to their advantages, such as rapid analysis time and sensitivity. To improve the atomic-level understanding of the interactions at the substrate surface, we performed density functional theory (DFT) simulations to study the nature of the adsorption of E2 on pristine graphene. Bayesian Optimization Structure Search (BOSS) was employed to reduce human bias in the determination of the most favorable adsorption configurations. Two stable adsorption minimum configurations were found. Analysis of their electronic properties indicates that E2 physisorbs on graphene. Embarking upon complex carbonaceous materials, the importance of finding all possible minimum candidates with automated structure search tools is highlighted. Computational investigations facilitate tailoring substrate materials with outstanding performance and applications in neuroscientific research, fertility monitoring, and clinical trials. Combining them with experimental research carries significant potential to advance sensor design beyond the current state-of-the-art.

Accelerated Lignocellulosic Molecule Adsorption Structure Determination

Here, we present a study combining Bayesian optimization structural inference with the machine learning interatomic potential Neural Equivariant Interatomic Potential (NequIP) to accelerate and enable the study of the adsorption of the conformationally flexible lignocellulosic molecules β-d-xylose and 1,4-β-d-xylotetraose on a copper surface. The number of structure evaluations needed to map out the relevant potential energy surfaces are reduced by Bayesian optimization, while NequIP minimizes the time spent on each evaluation, ultimately resulting in cost-efficient and reliable sampling of large systems and configurational spaces. Although the applicability of Bayesian optimization for the conformational analysis of the more flexible xylotetraose molecule is restricted by the sample complexity bottleneck, the latter can be effectively bypassed with external conformer search tools, such as the Conformer-Rotamer Ensemble Sampling Tool, facilitating the subsequent lower-dimensional global minimum adsorption structure determination. Finally, we demonstrate the applicability of the described approach to find adsorption structures practically equivalent to the density functional theory counterparts at a fraction of the computational cost.

Exploring the Conformers of an Organic Molecule on a Metal Cluster with Bayesian Optimization

Finding low-energy conformers of organic molecules is a complex problem due to the flexibilities of the molecules and the high dimensionality of the search space. When such molecules are on nanoclusters, the search complexity is exacerbated by constraints imposed by the presence of the cluster and other surrounding molecules. To address this challenge, we modified our previously developed active learning molecular conformer search method based on Bayesian optimization and density functional theory. Especially, we have developed and tested strategies to avoid steric clashes between a molecule and a cluster. In this work, we chose a cysteine molecule on a well-studied gold-thiolate cluster as a model system to test and demonstrate our method. We found that cysteine conformers in a cluster inherit the hydrogen bond types from isolated conformers. However, the energy rankings and spacings between the conformers are reordered.

Protective Coating Interfaces for Perovskite Solar Cell Materials: A First-Principles Study

The protection of halide perovskites is important for the performance and stability of emergent perovskite-based optoelectronic technologies. In this work, we investigate the potential inorganic protective coating materials ZnO, SrZrO₃, and ZrO₂ for the CsPbI₃ perovskite. The optimal interface registries are identified with Bayesian optimization. We then use semilocal density functional theory (DFT) to determine the atomic structure at the interfaces of each coating material with the clean CsI-terminated surface and three reconstructed surface models with added PbI₂ and CsI complexes. For the final structures, we explore the level alignment at the interface with hybrid DFT calculations. Our analysis of the level alignment at the coating-substrate interfaces reveals no detrimental mid-gap states but rather substrate-dependent valence and conduction band offsets. While ZnO and SrZrO₃ act as insulators on CsPbI₃, ZrO₂ might be suitable as an electron transport layer with the right interface engineering.

Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using Bayesian surrogate models

ABSTRACT

Oxidized tannic acid (OTA) is a useful biomolecule with a strong tendency to form complexes with metals and proteins. In this study we open the possibility to further the application of OTA when assembled as supramolecular systems, which typically exhibit functions that correlate with shape and associated morphological features. We used machine learning (ML) to selectively engineer OTA into particles encompassing one-dimensional to three-dimensional constructs. We employed Bayesian regression to correlate colloidal suspension conditions (pH and pK _a) with the size and shape of the assembled colloidal particles. Fewer than 20 experiments were found to be sufficient to build surrogate model landscapes of OTA morphology in the experimental design space, which were chemically interpretable and endowed predictive power on data. We produced multiple property landscapes from the experimental data, helping us to infer solutions that would satisfy, simultaneously, multiple design objectives. The balance between data efficiency and the depth of information delivered by ML approaches testify to their potential to engineer particles, opening new prospects in the emerging field of particle morphogenesis, impacting bioactivity, adhesion, interfacial stabilization, and other functions inherent to OTA.

IMPACT STATEMENT

Tannic acid is a versatile bio-derived material employed in coatings, surface modifiers, and emulsion and growth stabilizers, which also imparts mild anti-viral health benefits. Our recent work on the crystallization of oxidized tannic acid (OTA) colloids opens the route toward further valuable applications, but here the functional properties tend to depend strongly on particle morphology. In this study, we eschew trial-and-error morphology exploration of OTA particles in favor of a data-driven approach. We digitalized the experimental observations and input them into a Gaussian process regression algorithm to generate morphology surrogate models. These help us to visualize particle morphology in the design space of material processing conditions, and thus determine how to selectively engineer one-dimensional or three-dimensional particles with targeted functionalities. We extend this approach to visualize other experimental outcomes, including particle yield and particle surface-to-volume ratio, which are useful for the design of products based on OTA particles. Our findings demonstrate the use of data-efficient surrogate models for general materials engineering purposes and facilitate the development of next-generation OTA-based applications.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1557/s43577-021-00183-4.

Electronic Characterization of a Charge-Transfer Complex Monolayer on Graphene

Organic charge-transfer complexes (CTCs) formed by strong electron acceptor and strong electron donor molecules are known to exhibit exotic effects such as superconductivity and charge density waves. We present a low-temperature scanning tunneling microscopy and spectroscopy (LT-STM/STS) study of a two-dimensional (2D) monolayer CTC of tetrathiafulvalene (TTF) and fluorinated tetracyanoquinodimethane (F₄TCNQ), self-assembled on the surface of oxygen-intercalated epitaxial graphene on Ir(111) (G/O/Ir(111)). We confirm the formation of the charge-transfer complex by dI/dV spectroscopy and direct imaging of the singly occupied molecular orbitals. High-resolution spectroscopy reveals a gap at zero bias, suggesting the formation of a correlated ground state at low temperatures. These results point to the possibility to realize and study correlated ground states in charge-transfer complex monolayers on weakly interacting surfaces.

First-principles calculations of hybrid inorganic-organic interfaces: from state-of-the-art to best practice

The computational characterization of inorganic-organic hybrid interfaces is arguably one of the technically most challenging applications of density functional theory. Due to the fundamentally different electronic properties of the inorganic and the organic components of a hybrid interface, the proper choice of the electronic structure method, of the algorithms to solve these methods, and of the parameters that enter these algorithms is highly non-trivial. In fact, computational choices that work well for one of the components often perform poorly for the other. As a consequence, default settings for one materials class are typically inadequate for the hybrid system, which makes calculations employing such settings inefficient and sometimes even prone to erroneous results. To address this issue, we discuss how to choose appropriate atomistic representations for the system under investigation, we highlight the role of the exchange-correlation functional and the van der Waals correction employed in the calculation and we provide tips and tricks how to efficiently converge the self-consistent field cycle and to obtain accurate geometries. We particularly focus on potentially unexpected pitfalls and the errors they incur. As a summary, we provide a list of best practice rules for interface simulations that should especially serve as a useful starting point for less experienced users and newcomers to the field.

Efficient Amino Acid Conformer Search with Bayesian Optimization

Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determining conformer structures and energies. Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cysteine as an example, we show that our procedure is both efficient and accurate. After only 1000 single-point calculations and approximately 80 structure relaxations, which is less than 10% computational cost of the current fastest method, we have found the low-energy conformers in good agreement with experimental measurements and reference calculations. To test the transferability of our method, we also repeated the conformer search of serine, tryptophan, and aspartic acid. The results agree well with previous conformer search studies.