Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

Modeling and tuning the electronic, mechanical and optical properties of a recently synthesized 2D polyaramid: a first principles study

This work delves into a methodology of modeling 2D materials and their structural engineering, considering an example of a recently synthesized 2D polyaramid (2DPA-1). A bottom-up approach similar to experimental techniques is implemented for modeling, and then its electronic structures and phonon spectrum and the quadratic nature of flexural phonons are analyzed. Furthermore, boron and nitrogen atoms are substituted for the carbon atom of the amide group of 2DPA-1, and their effects on its electronic properties, phonon spectrum, and mechanical properties are compared with those of pristine 2DPA-1 using density functional theory calculations. The ab initio molecular dynamics (AIMD) simulations validate the thermal stability of our system at high temperatures. The spin-polarized electronic structures reveal the transformation of pristine 2DPA-1 from a semiconductor to a half-metal and its magnetic behaviour upon nitrogen substitution. Constraining the quadratic nature of flexural phonons using the Born-Huang criteria significantly enhances the phonon spectra, leading to more accurate and reliable simulations. For modulated 2DPA-1, the elastic modulus varies between 17 and 27 N m-1, and the absorption peaks shift from ∼5.15 eV to 2.42 eV, enabling the application of polymeric 2D nanomaterials in photocatalysis and sensing, where light absorption in the near-infrared region is important. Finally, validation of our methodology is confirmed, as computed Young's modulus (11.26-11.76 GPa) of 2DPA-1 matches excellently with the experimental value (12.7 ± 3.8 GPa). Overall, this study reveals the modeling of a newly synthesized polymeric 2D material, and tuning its properties results in smaller bandgaps and half-metallic and magnetic behaviours.

Induction of breast cancer cell apoptosis by novel thiouracil-fused heterocyclic compounds through boosting of Bax/Bcl-2 ratio and DFT study

Breast cancer is a common public health disease causing mortality worldwide. Thus, providing novel chemotherapies that tackle breast cancer is of great interest. In this investigation, novel pyrido[2,3-d]pyrimidine derivatives 3,4,(6a-c),(8a,b),9-20 were synthesized and characterized using a variety of spectrum analyses. The geometric and thermal parameters of the novel thiouracil derivatives 3,4,6a,(8a,b),11,12,17,18, 19 were measured using density functional theory (DFT) via DFT/B3LYP/6-31 + G(d,p) basis set. All synthesized compounds were evaluated by MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) method using MCF-7 and MDA-MB-231 breast cancerous cells, compound 17 had the maximum anticancer activity against both breast cancerous cells, recording the lowest half-maximal inhibitory concentration (IC50) values (56.712 μg/mL for MCF-7 cells and 48.743 μg/mL for MDA-MB-231 cells). The results were confirmed in terms of the intrinsic mechanism of apoptosis, where compound 17 had the highest percentage in the case of both cancer cells and recorded Bax (Bcl-2 associated X)/Bcl-2 (B-cell lymphoma 2) ratio 17.5 and 96.667 for MCF-7 and MDA-MB-231 cells, while compound 19 came after 17 in the ability for induction of apoptosis, where the Bax/Bcl-2 ratio was 15.789 and 44.273 for both cancerous cells, respectively. Also, compound 11 recorded a high Bax/Bcl-2 ratio for both cells. The safety of the synthesized compounds was applied on normal WI-38 cells, showing minimum cytotoxic effect with undetectable IC50. Compounds 17, 11, and 19 recorded a significant increase of p53 upregulated modulator of apoptosis (PUMA) expression levels in the cancerous cells. The DFT method was also used to establish a connection between the experimentally determined values of the present investigated compounds and their predicted quantum chemical parameters. It was concluded that Compounds 17, 11, and 19 had anti-breast cancer potential through the induction of apoptotic Bax/Bcl-2 and PUMA expression levels.

ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT

The pursuit of designing smart and functional materials is of paramount importance across various domains, such as material science, engineering, chemical technology, electronics, biomedicine, energy, and numerous others. Consequently, researchers are actively involved in the development of innovative models and strategies for material design. Recent advancements in analytical tools, experimentation, and computer technology additionally enhance the material design possibilities. Notably, data-driven techniques like artificial intelligence and machine learning have achieved substantial progress in exploring various applications within material science. One such approach, ChatGPT, a large language model, holds transformative potential for addressing complex queries. In this article, we explore ChatGPT's understanding of material science by assigning some simple tasks across various subareas of computational material science. The findings indicate that while ChatGPT may make some minor errors in accomplishing general tasks, it demonstrates the capability to learn and adapt through human interactions. However, issues like output consistency, probable hidden errors, and ethical consequences should be addressed.

Design, synthesis, and application of some two-dimensional materials

Two-dimensional (2D) materials are widely used as key components in the fields of energy conversion and storage, optoelectronics, catalysis, biomedicine, etc. To meet the practical needs, molecular structure design and aggregation process optimization have been systematically carried out. The intrinsic correlation between preparation methods and the characteristic properties is investigated. This review summarizes the recent research achievements of 2D materials in the aspect of molecular structure modification, aggregation regulation, characteristic properties, and device applications. The design strategies to fabricate functional 2D materials starting from precursor molecules are introduced in detail referring to organic synthetic chemistry and self-assembly technology. It provides important research ideas for the design and synthesis of related materials.

Treating Polarization Effects in Charged and Polar Bio-Molecules Through Variable Electrostatic Parameters

Polarization plays important roles in charged and hydrogen bonding containing systems. Much effort ranging from the construction of physics-based models to quantum mechanism (QM)-based and machine learning (ML)-assisted models have been devoted to incorporating the polarization effect into the conventional force fields at different levels, such as atomic and coarse grained (CG). The application of polarizable force fields or polarization models was limited by two aspects, namely, computational cost and transferability. Different from physics-based models, no predetermining parameters were required in the QM-based approaches. Taking advantage of both the accuracy of QM calculations and efficiency of molecular mechanism (MM) and ML, polarization effects could be treated more efficiently while maintaining the QM accuracy. The computational cost could be reduced with variable electrostatic parameters, such as the charge, dipole, and electronic dielectric constant with the help of linear scaling fragmentation-based QM calculations and ML models. Polarization and entropy effects on the prediction of partition coefficient of druglike molecules are demonstrated by using both explicit or implicit all-atom molecular dynamics simulations and machine learning-assisted models. Directions and challenges for future development are also envisioned.

Design of Thermally Activated Delayed Fluorescence Materials with High Intersystem Crossing Efficiencies by Machine Learning-Assisted Virtual Screening

Efficient intersystem crossing (ISC) and reverse ISC (RISC) processes are of vital significance for thermally activated delayed fluorescence (TADF) materials to achieve 100% internal quantum efficiency. However, it is challenging to rapidly predict the ISC/RISC rates of large amounts of TADF materials and screen promising candidates because of their flexible molecular design. Here, we perform virtual screening of 564 candidates constructed from 20 unique building blocks linking in D-A, D-π-A, and D-A-D (D') configurations using the established machine learning models of GBRT and RF-GBRT-KNN with the Pearson's correlation coefficients (r) of 0.89 and 0.82, respectively. Novel descriptors of ΔE_LL, Polar, and ΔE_TT for predicting ISC/RISC rates were proposed, and nine TADF molecules with the predicted ISC and RISC rates of >7 × 10⁷ and 2 × 10⁵ s^-1, respectively, were revealed. We provide an efficient approach to predicting ISC and RISC rates of TADF molecules on a large scale, elucidating important building blocks and architectures to design high-performance optoelectronic materials for experimental explorations.

A Transfer Learning Approach for Reaction Discovery in Small Data Situations Using Generative Model

Sustainable practices in chemical sciences can be better realized by adopting interdisciplinary approaches that combine the advantages of machine learning (ML) on the initially acquired small data in reaction discovery. Developing new reactions generally remains heuristic and even time and resource intensive. For instance, synthesis of fluorine-containing compounds, which constitute ∼20% of the marketed drugs, relies on deoxyfluorination of abundantly available alcohols. Herein, we demonstrate the use of a recurrent neural network-based deep generative model built on a library of just 37 alcohols for effective learning and exploration of the chemical space. The proof-of-concept ML model is able to generate good quality, synthetically accessible, higher-yielding novel alcohol molecules. This protocol would have superior utility for deployment into a practical reaction discovery pipeline.

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Dual pronged transfer learning, both to generate and predict yields of new molecules

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Demonstrated the utility for an important family of deoxyfluorination of alcohols

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Applicable for practically more likely situations with relatively smaller data

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Extendable to other reaction manifolds to facilitate expedited reaction discovery