Large language models (LLMs) are emerging as a powerful tool in chemistry across multiple domains. In chemistry, LLMs are able to accurately predict properties, design new molecules, optimize synthesis pathways, and accelerate drug and material discovery. A core emerging idea is combining LLMs with chemistry-specific tools like synthesis planners and databases, leading to so-called "agents." This review covers LLMs' recent history, current capabilities, design, challenges specific to chemistry, and future directions. Particular attention is given to agents and their emergence as a cross-chemistry paradigm. Agents have proven effective in diverse domains of chemistry, but challenges remain. It is unclear if creating domain-specific versus generalist agents and developing autonomous pipelines versus "co-pilot" systems will accelerate chemistry. An emerging direction is the development of multi-agent systems using a human-in-the-loop approach. Due to the incredibly fast development of this field, a repository has been built to keep track of the latest studies: https://github.com/ur-whitelab/LLMs-in-science.

By shaking a container, we can tell its inventory status from the generated acoustic vibrations, such as the quantity and geometry of the objects inside. Similarly, we can identify the material and geometry of the entire object through multiple tappings. Human hands are equipped with high-frequency skin vibrations to help capture such complex object properties [1]. However, despite the significance of acoustic vibrations for tactile perception, equipping robot manipulators with acoustic vibration sensing capability for rich object perception remains difficult [2, 3, 4, 5, 6]. Though previous research has explored placing air microphones near robot platforms to estimate liquid height [7] and pouring amounts [8], classify object materials [9] and categories [10, 11, 12], air microphones mainly capture Figure 1: SonicSense enables container sound waves transmitted through air, leading to noisy inventory status differentiation, heterogeneous signals with ambient noises. On the other hand, contact material prediction, 3D microphones only sense the acoustic vibrations caused by shape reconstruction, and object reidentification on a diverse set of 83 realworld physical contact. Past work has studied contact microphones objects.

Translating advances in visual perception to robotic grasping and manipulation of objects remains challenging. For complex manipulation tasks such as peg insertion, pulling or twisting with resistance, and dynamic motions such as throwing and catching, fine-grained manipulation requires tactile perception. Tactile sensors have been paired with visual sensors for both classical control and machine learning approaches to these tasks [1], but issues of fragility and cost present barriers to heavy use or industrial integration, particularly for manipulation tasks that would place higher forces on sensors at the tactile edge. Previously, a GelSight [2] tactile sensor was used to train an agent on a USB insertion task [3], the first time this was achieved with imitation learning. GelSight is not designed for robustness to higher shear forces and was noted to break irrecoverably during data collection and inference for that task, requiring repeated replacement. This work also demonstrated an approach using tactile information only during pretraining, then ablating the tactile sensor at inference, achieving a more robust vision-only manipulation system. BeadSight [4] aimed to make a simpler, low cost calibration-free sensor that, like GelSight, still operated at an end effector's point of contact with objects. We constructed the BeadSight sensor, which does not rely on any calibration and instead relies entirely on neural networks to distill information about contacts and movements at the tactile edge. In this work, we repeated the visuo-tactile pretraining USB plugging experiment using the much lower fidelity BeadSight to produce a direct comparison with the GelSight sensor in the task of plugging in a USB cable.

Machine learning (ML) plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules. However, most existing ML models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work, we developed a unified ML method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with the widely-used hybrid and double hybrid functionals in computational costs and prediction accuracy of various quantum chemical properties. As case studies, we apply the model to aromatic compounds and semiconducting polymers on both ground state and excited state properties, demonstrating its accuracy and generalization capability to complex systems that are hard to calculate using CCSD(T)-level methods.

Concept Drift is a phenomenon in which the underlying data distribution and statistical properties of a target domain change over time, leading to a degradation of the model's performance. Consequently, models deployed in production require continuous monitoring through drift detection techniques. Most drift detection methods to date are supervised, i.e., based on ground-truth labels. However, true labels are usually not available in many real-world scenarios. Although recent efforts have been made to develop unsupervised methods, they often lack the required accuracy, have a complexity that makes real-time implementation in production environments difficult, or are unable to effectively characterize drift. To address these challenges, we propose DriftLens, an unsupervised real-time concept drift detection framework. It works on unstructured data by exploiting the distribution distances of deep learning representations. DriftLens can also provide drift characterization by analyzing each label separately. A comprehensive experimental evaluation is presented with multiple deep learning classifiers for text, image, and speech. Results show that (i) DriftLens performs better than previous methods in detecting drift in $11/13$ use cases; (ii) it runs at least 5 times faster; (iii) its detected drift value is very coherent with the amount of drift (correlation $\geq 0.85$); (iv) it is robust to parameter changes.

This contribution gives an overview of an ongoing project using machine learning and computer vision components for improving the electronic waste recycling process. In circular economy, the "virtual mines" concept refers to production cycles where interesting raw materials are reclaimed in an efficient and cost-effective manner from end-of-life items. In particular, the growth of e-waste, due to the increasingly shorter life cycle of hi-tech goods, is a global problem. In this paper, we describe a pipeline based on deep learning model to recycle printed circuit boards at the component level. A pre-trained YOLOv5 model is used to analyze the results of the locally developed dataset. With a different distribution of class instances, YOLOv5 managed to achieve satisfactory precision and recall, with the ability to optimize with large component instances.

The waste of electrical and electronic equipment has been increased due to the fast evolution of technology products and competition of many IT sectors. Every year millions of tons of electronic waste are thrown into the environment which causes high consequences for human health. Therefore, it is crucial to control this waste flow using technology, especially using Artificial Intelligence but also reclamation of critical raw materials for new production processes. In this paper, we focused on the measurement of recyclability of waste electronic components (WECs) from waste printed circuit boards (WPCBs) using mathematical innovation model. This innovative approach evaluates both the recyclability and recycling difficulties of WECs, integrating an AI model for improved disassembly and sorting. Assessing the recyclability of individual electronic components present on WPCBs provides insight into the recovery potential of valuable materials and indicates the level of complexity involved in recycling in terms of economic worth and production utility. This novel measurement approach helps AI models in accurately determining the number of classes to be identified and sorted during the automated disassembly of discarded PCBs. It also facilitates the model in iterative training and validation of individual electronic components.

Gas recognition technology has received considerable attention from researchers in recent years. Nevertheless, the gas recognition area has faced obstacles in implementing deep learning-based recognition solutions due to the absence of standardized protocols. To tackle this problem, we suggest using two sets of specialized evaluation measures for gas recognition algorithms. These metrics will make it easier to examine the performance of these algorithms on various datasets. In addition, we provide a new model called the Wavelet Attention GRU (WAG), which is based on the wavelet attention mechanism. This method facilitates the more efficient retrieval of sensor signals. Compared to other models, WAG significantly decreases the number of sensors needed by 75% while obtaining an identification accuracy of 98.33%. This suggests that WAG is a potential approach for advancing gas recognition algorithms.

Neutron-Transformer Reflectometry and Advanced Computation Engine (N-TRACE ), a neural network model using transformer architecture, is introduced for neutron reflectometry data analysis. It offers fast, accurate initial parameter estimations and efficient refinements, improving efficiency and precision for real-time data analysis of lithium-mediated nitrogen reduction for electrochemical ammonia synthesis, with relevance to other chemical transformations and batteries. Despite limitations in generalizing across systems, it shows promises for the use of transformers as the basis for models that could replace trial-and-error approaches to modeling reflectometry data.

Molecular modeling, a central topic in quantum mechanics, aims to accurately calculate the properties and simulate the behaviors of molecular systems. The molecular model is governed by physical laws, which impose geometric constraints such as invariance and equivariance to coordinate rotation and translation. While numerous deep learning approaches have been developed to learn molecular representations under these constraints, most of them are built upon heuristic and costly modules. We argue that there is a strong need for a general and flexible framework for learning both invariant and equivariant features. In this work, we introduce a novel Transformer-based molecular model called GeoMFormer to achieve this goal. Using the standard Transformer modules, two separate streams are developed to maintain and learn invariant and equivariant representations. Carefully designed cross-attention modules bridge the two streams, allowing information fusion and enhancing geometric modeling in each stream. As a general and flexible architecture, we show that many previous architectures can be viewed as special instantiations of GeoMFormer. Extensive experiments are conducted to demonstrate the power of GeoMFormer. All empirical results show that GeoMFormer achieves strong performance on both invariant and equivariant tasks of different types and scales. Code and models will be made publicly available at https://github.com/c-tl/GeoMFormer.