The fusion of LiDAR and photogrammetry point clouds is a necessary advancement in 3D-modeling, enabling more comprehensive and accurate representations of physical environments. The main contribution of this paper is the development of an innovative fusion system that combines classical algorithms, such as Structure from Motion (SfM), with advanced machine learning techniques, like Coherent Point Drift (CPD) and Feature-Metric Registration (FMR), to improve point cloud alignment and fusion. Experimental results, using a custom dataset of real-world scenes, demonstrate that the hybrid fusion method achieves an average error of less than 5% in the measurements of small reconstructed objects, with large objects showing less than 2% deviation from real sizes. The fusion process significantly improved structural continuity, reducing artifacts like edge misalignments. The k-nearest neighbors (kNN) analysis showed high reconstruction accuracy for the hybrid approach, demonstrating that the hybrid fusion system, particularly when combining machine learning-based refinement with traditional alignment methods, provides a notable advancement in both geometric accuracy and computational efficiency for real-time 3D-modeling applications.

In optimizing organic chemical synthesis, researchers often face challenges in efficiently generating viable synthesis procedures that conserve time and resources in laboratory settings. This paper systematically analyzes multiple approaches to efficiently generate synthesis procedures for a wide variety of organic synthesis reactions, aiming to decrease time and resource consumption in laboratory work. We investigated the suitability of different sizes of BART, T5, FLAN-T5, molT5, and classic sequence-to-sequence transformer models for our text-to-text task and utilized a large dataset prepared specifically for the task. Experimental investigations demonstrated that a fine-tuned molT5-large model achieves a BLEU score of 47.75. The results demonstrate the capability of LLMs to predict chemical synthesis procedures involving 24 possible distinct actions, many of which include various parameters like solvents, reaction agents, temperature, duration, solvent ratios, and other specific parameters. Our findings show that only when the core reactants are used as input, the models learn to correctly predict what ancillary components need to be included in the resulting procedure. These results are valuable for AI researchers and chemists, suggesting that curated datasets and large language model fine-tuning techniques can be tailored for specific reaction classes and practical applications. This research contributes to the field by demonstrating how deep-learning-based methods can be customized to meet the specific requirements of chemical synthesis, leading to more intelligent and resource-efficient laboratory processes.

This paper presents a novel approach to predicting esterification procedures in organic chemistry by employing generative large language models (LLMs) to interpret and translate SMILES molecular notation into detailed procedural texts of synthesis reactions. Esterification reaction is important in producing various industrial intermediates, fragrances, and flavors. Recognizing the challenges of accurate prediction in complex chemical landscapes, we have compiled and made publicly available a curated dataset of esterification reactions to enhance research collaboration. We systematically compare machine learning algorithms, ranging from the conventional k-nearest neighbors (kNN) to advanced sequence-to-sequence transformer models, including FLAN-T5 and ChatGPT-based variants. Our analysis highlights the FLAN-T5 model as the standout performer with a BLEU score of 51.82, suggesting that the model has significant potential in enhancing reaction planning and chemical synthesis. Our findings contribute to the growing field of AI in chemistry, offering a promising direction for enhancing the efficiency of reaction planning and chemical synthesis.

This article proposes a methodology that uses machine learning algorithms to extract actions from structured chemical synthesis procedures, thereby bridging the gap between chemistry and natural language processing. The proposed pipeline combines ML algorithms and scripts to extract relevant data from USPTO and EPO patents, which helps transform experimental procedures into structured actions. This pipeline includes two primary tasks: classifying patent paragraphs to select chemical procedures and converting chemical procedure sentences into a structured, simplified format. We employ artificial neural networks such as long short-term memory, bidirectional LSTMs, transformers, and fine-tuned T5. Our results show that the bidirectional LSTM classifier achieved the highest accuracy of 0.939 in the first task, while the Transformer model attained the highest BLEU score of 0.951 in the second task. The developed pipeline enables the creation of a dataset of chemical reactions and their procedures in a structured format, facilitating the application of AI-based approaches to streamline synthetic pathways, predict reaction outcomes, and optimize experimental conditions. Furthermore, the developed pipeline allows for creating a structured dataset of chemical reactions and procedures, making it easier for researchers to access and utilize the valuable information in synthesis procedures.

Crystallization is an important purification technique for solid products in a chemical laboratory. However, the correct selection of a solvent is important for the success of the procedure. In order to accelerate the solvent or solvent mixture search process, we offer an in silico alternative, i.e., a never previously demonstrated approach that can model the reaction mixture crystallization conditions which are invariant to the reaction type. The offered deep learning-based method is trained to directly predict the solvent labels used in the crystallization steps of the synthetic procedure. Our solvent label prediction task is a multi-label multi-class classification task during which the method must correctly choose one or several solvents from 13 possible examples. During the experimental investigation, we tested two multi-label classifiers (i.e., Feed-Forward and Long Short-Term Memory neural networks) applied on top of vectors. For the vectorization, we used two methods (i.e., extended-connectivity fingerprints and autoencoders) with various parameters. Our optimized technique was able to reach the accuracy of 0.870 ± 0.004 (which is 0.693 above the baseline) on the testing dataset. This allows us to assume that the proposed approach can help to accelerate manual R&D processes in chemical laboratories.

In optimizing organic chemical synthesis, researchers often face challenges in efficiently generating viable synthesis procedures that conserve time and resources in laboratory settings. This paper systematically analyzes multiple approaches to efficiently generate synthesis procedures for a wide variety of organic synthesis reactions, aiming to decrease time and resource consumption in laboratory work. We investigated the suitability of different sizes of BART, T5, FLAN-T5, molT5, and classic sequence-to-sequence transformer models for our text-to-text task and utilized a large dataset prepared specifically for the task. Experimental investigations demonstrated that a fine-tuned molT5-large model achieves a BLEU score of 47.75. The results demonstrate the capability of LLMs to predict chemical synthesis procedures involving 24 possible distinct actions, many of which include various parameters like solvents, reaction agents, temperature, duration, solvent ratios, and other specific parameters. Our findings show that only when the core reactants are used as input, the models learn to correctly predict what ancillary components need to be included in the resulting procedure. These results are valuable for AI researchers and chemists, suggesting that curated datasets and large language model fine-tuning techniques can be tailored for specific reaction classes and practical applications. This research contributes to the field by demonstrating how deep-learning-based methods can be customized to meet the specific requirements of chemical synthesis, leading to more intelligent and resource-efficient laboratory processes.