Reinforcement learning for in silico determination of adsorbate-substrate structures.

Reinforcement learning (RL) methods have helped to define the state of the art in the field of modern artificial intelligence, mostly after the breakthrough involving AlphaGo and the discovery of novel algorithms. In this work, we present a RL method, based on Q-learning, for the structural determination of adsorbate@substrate models in silico, where the minimization of the energy landscape resulting from adsorbate interactions with a substrate is made by actions on states (translations and rotations) chosen from an agent's policy. The proposed RL method is implemented in an early version of the reinforcement learning software for materials design and discovery (RLMaterial), developed in Python3.x. RLMaterial interfaces with deMon2k, DFTB+, ORCA, and Quantum Espresso codes to compute the adsorbate@substrate energies. The RL method was applied for the structural determination of (i) the amino acid glycine and (ii) 2-amino-acetaldehyde, both interacting with a boron nitride (BN) monolayer, (iii) host-guest interactions between phenylboronic acid and ß-cyclodextrin and (iv) ammonia on naphthalene. Density functional tight binding calculations were used to build the complex search surfaces with a reasonably low computational cost for systems (i)-(iii) and DFT for system (iv). Artificial neural network and gradient boosting regression techniques were employed to approximate the Q-matrix or Q-table for better decision making (policy) on next actions. Finally, we have developed a transfer-learning protocol within the RL framework that allows learning from one chemical system and transferring the experience to another, as well as from different DFT or DFTB levels.

Thermal isomerization rates in retinal analogues using Ab-Initio molecular dynamics.

For a detailed understanding of chemical processes in nature and industry, we need accurate models of chemical reactions in complex environments. While Eyring transition state theory is commonly used for modeling chemical reactions, it is most accurate for small molecules in the gas phase. A wide range of alternative rate theories exist that can better capture reactions involving complex molecules and environmental effects. However, they require that the chemical reaction is sampled by molecular dynamics simulations. This is a formidable challenge since the accessible simulation timescales are many orders of magnitude smaller than typical timescales of chemical reactions. To overcome these limitations, rare event methods involving enhanced molecular dynamics sampling are employed. In this work, thermal isomerization of retinal is studied using tight-binding density functional theory. Results from transition state theory are compared to those obtained from enhanced sampling. Rates obtained from dynamical reweighting using infrequent metadynamics simulations were in close agreement with those from transition state theory. Meanwhile, rates obtained from application of Kramers' rate equation to a sampled free energy profile along a torsional dihedral reaction coordinate were found to be up to three orders of magnitude higher. This discrepancy raises concerns about applying rate methods to one-dimensional reaction coordinates in chemical reactions.

A DFTB study on the electronic response of encapsulated DNA nucleobases onto chiral CNTs as a sequencer.

Sequencing the DNA nucleobases is essential in the diagnosis and treatment of many diseases related to human genes. In this article, the encapsulation of DNA nucleobases with some of the important synthesized chiral (7, 6), (8, 6), and (10, 8) carbon nanotubes were investigated. The structures were modeled by applying density functional theory based on tight binding method (DFTB) by considering semi-empirical basis sets. Encapsulating DNA nucleobases on the inside of CNTs caused changes in the electronic properties of the selected chiral CNTs. The results confirmed that van der Waals (vdW) interactions, π-orbitals interactions, non-bonded electron pairs, and the presence of high electronegative atoms are the key factors for these changes. The result of electronic parameters showed that among the CNTs, CNT (8, 6) is a suitable choice in sequencing guanine (G) and cytosine (C) DNA nucleobases. However, they are not able to sequence adenine (A) and thymine (T). According to the band gap energy engineering approach and absorption energy, the presence of G and C DNA nucleobases decreased the band gap energy of CNTs. Hence selected CNTs suggested as biosensor substrates for sequencing G and C DNA nucleobases.

Advances in Computational Methods for Modeling Photocatalytic Reactions: A Review of Recent Developments.

Photocatalysis is a fascinating process in which a photocatalyst plays a pivotal role in driving a chemical reaction when exposed to light. Its capacity to harness light energy triggers a cascade of reactions that lead to the formation of intermediate compounds, culminating in the desired final product(s). The essence of this process is the interaction between the photocatalyst's excited state and its specific interactions with reactants, resulting in the creation of intermediates. The process's appeal is further enhanced by its cyclic nature-the photocatalyst is rejuvenated after each cycle, ensuring ongoing and sustainable catalytic action. Nevertheless, comprehending the photocatalytic process through the modeling of photoactive materials and molecular devices demands advanced computational techniques founded on effective quantum chemistry methods, multiscale modeling, and machine learning. This review analyzes contemporary theoretical methods, spanning a range of lengths and accuracy scales, and assesses the strengths and limitations of these methods. It also explores the future challenges in modeling complex nano-photocatalysts, underscoring the necessity of integrating various methods hierarchically to optimize resource distribution across different scales. Additionally, the discussion includes the role of excited state chemistry, a crucial element in understanding photocatalysis.

Energy Decomposition Scheme for Rectangular Graphene Flakes.

We show-to our own surprise-that total electronic energies for a family of m × n rectangular graphene flakes can be very accurately represented by a simple function of the structural parameters m and n with errors not exceeding 1 kcal/mol. The energies of these flakes, usually referred to as multiple zigzag chains Z(m,n), are computed for m, n < 21 at their optimized geometries using the DFTB3 methodology. We have discovered that the structural parameters m and n (and their simple algebraic functions) provide a much better basis for the energy decomposition scheme than the various topological invariants usually used in this context. Most terms appearing in our energy decomposition scheme seem to have simple chemical interpretations. Our observation goes against the well-established knowledge stating that many-body energies are complicated functions of molecular parameters. Our observations might have far-reaching consequences for building accurate machine learning models.

Interfacial Charge Transfer in Photoexcited QD-Molecule Composite of Tetrahedral CdSe Quantum Dot Coupled with Carbazole.

Photoexcited charge transfer dynamics in CdSe quantum dots (QDs) coupled with carbazole were explored to model QD-molecule systems for light-harvesting applications. The absorption spectra of QDs with different sizes, i.e., Cd35Se20X30L30 (T1), Cd56Se35X42L42 (T2), and Cd84Se56X56L56 (T3) were simulated with quantum dynamical methods, which qualitatively match the reported experimental spectra. The carbazole is attached with a 3-amino group at the apex position of T1 (namely T1-3A-Cz), establishing proper electronic communication between T1 and carbazole. The spectra of T1-3A-Cz is 0.22 eV red-shifted compared to T1. A time-dependent perturbation was applied in tune with the lowest energy peak (3.63 eV) of T1-3A-Cz to investigate the charge transfer dynamics, which revealed an ultrafast charge separation within the femtosecond time scale. The electronic structure showed a favorable energy alignment between T1 and carbazole in T1-3A-Cz. The LUMO of carbazole was situated below the conduction band of the QD, while the HOMO of carbazole mixed perfectly with the top of the valence band of the QD, developing the interfacial charge transfer states. These states promoted the photoexcited electron transfer directly from the CdSe core to carbazole. A rapid and enhanced charge separation occurred with the laser field strength increasing from 0.001 to 0.005 V/Å. However, T1 connected to the other positions of carbazole did not show charge separation effectively. The photoinduced charge transfer is negligible in the case of T2-carbazole systems due to poor electronic coupling, and it is not observed in T3-carbazole systems. So, the T1-3A-Cz model acts as a perfect donor-acceptor QD-molecule nanocomposite that can harvest photon energy efficiently. Further enhancement of charge transfer can be achieved by coupling more carbazoles to the T1 QD (e.g., T1-3A-Cz2) due to the extension of hole delocalization between T1 and the carbazoles.

A Novel Coating System Based on Layered Double Hydroxide/HQS Hierarchical Structure for Reliable Protection of Mg Alloy: Electrochemical and Computational Perspectives.

Growing research activity on layered double hydroxide (LDH)-based materials for novel applications has been increasing; however, promoting LDH layer growth and examining its morphologies without resorting to extreme pressure conditions remains a challenge. In the present study, we enhance LDH growth and morphology examination without extreme pressure conditions. By synthesizing Mg-Al LDH directly on plasma electrolytic oxidation (PEO)-treated Mg alloy surfaces and pores at ambient pressure, the direct synthesis was achieved feasibly without autoclave requirements, employing a suitable chelating agent. Additionally, enhancing corrosion resistance involved incorporating electron donor-acceptor compounds into a protective layer, with 8-Hydroxyquinoline-5-sulfonic acid (HQS) that helps in augmenting Mg alloy corrosion resistance through the combination of LDH ion-exchange ability and the organic layer. DFT simulations were used to explain the mutual interactions in the LDH system and provide a theoretical knowledge of the interfacial process at the molecular level.

Activation of Cryptochrome 4 from Atlantic Herring.

Marine fish migrate long distances up to hundreds or even thousands of kilometers for various reasons that include seasonal dependencies, feeding, or reproduction. The ability to perceive the geomagnetic field, called magnetoreception, is one of the many mechanisms allowing some fish to navigate reliably in the aquatic realm. While it is believed that the photoreceptor protein cryptochrome 4 (Cry4) is the key component for the radical pair-based magnetoreception mechanism in night migratory songbirds, the Cry4 mechanism in fish is still largely unexplored. The present study aims to investigate properties of the fish Cry4 protein in order to understand the potential involvement in a radical pair-based magnetoreception. Specifically, a computationally reconstructed atomistic model of Cry4 from the Atlantic herring (Clupea harengus) was studied employing classical molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) methods to investigate internal electron transfers and the radical pair formation. The QM/MM simulations reveal that electron transfers occur similarly to those found experimentally and computationally in Cry4 from European robin (Erithacus rubecula). It is therefore plausible that the investigated Atlantic herring Cry4 has the physical and chemical properties to form radical pairs that in turn could provide fish with a radical pair-based magnetic field compass sensor.

Benchmark Investigation of SCC-DFTB against Standard and Hybrid DFT to Model Electronic Properties in Two-Dimensional MOFs for Thermoelectric Applications.

Recent studies have shown that metal-organic frameworks (MOFs) have potential as thermoelectric materials, and the topic has received increasing attention. The main motivation for this project is to further our knowledge of thermoelectric properties in MOFs and find which available self-consistent-charge density functional tight binding (SCC-DFTB) method can best predict (at least trends in) the electronic properties of MOFs at a lower computational cost than standard density functional theory (DFT). In this work, the electronic properties of monolayer, serrated, AA-stacked, and/or AB-stacked Zn3C6O6, Cd3C6O6, Zn-NH-MOF─for which no previous calculations of thermoelectric performance exist─and Ni3(HITP)2 MOFs are modeled with DFT-PBE, DFT-HSE06, GFN1-xTB, GFN2-xTB, and DFTB-3ob/mio. The band structures, density of states, and their relative orbital contributions, as well as the electrical conductivity, Seebeck coefficient, and power factor, are compared across methods and geometries. Our results suggest that GFN-xTB is adequate to predict the MOFs' band structure shape and density of states but not band gap. Our calculations further indicate that Zn3C6O6, Cd3C6O6, and Zn-NH-MOF have higher power factor values than Ni3(HITP)2, one of the highest performing synthesized MOFs, and are therefore promising for thermoelectric applications.