Retro-Rank-In: A Ranking-Based Approach for Inorganic Materials Synthesis Planning

Retrosynthesis strategically plans the synthesis of a chemical target compound from simpler, readily available precursor compounds. This process is critical for synthesizing novel inorganic materials, yet traditional methods in inorganic chemistry continue to rely on trial-and-error experimentation. Emerging machine-learning approaches struggle to generalize to entirely new reactions due to their reliance on known precursors, as they frame retrosynthesis as a multi-label classification task. To address these limitations, we propose Retro-Rank-In, a novel framework that reformulates the retrosynthesis problem by embedding target and precursor materials into a shared latent space and learning a pairwise ranker on a bipartite graph of inorganic compounds. We evaluate Retro-Rank-In's generalizability on challenging retrosynthesis dataset splits designed to mitigate data duplicates and overlaps. For instance, for Cr2AlB2, it correctly predicts the verified precursor pair CrB + Al despite never seeing them in training, a capability absent in prior work. Extensive experiments show that Retro-Rank-In sets a new state-of-the-art, particularly in out-of-distribution generalization and candidate set ranking, offering a powerful tool for accelerating inorganic material synthesis.

Docking-Aware Attention: Dynamic Protein Representations through Molecular Context Integration

Computational prediction of enzymatic reactions represents a crucial challenge in sustainable chemical synthesis across various scientific domains, ranging from drug discovery to materials science and green chemistry. These syntheses rely on proteins that selectively catalyze complex molecular transformations. These protein catalysts exhibit remarkable substrate adaptability, with the same protein often catalyzing different chemical transformations depending on its molecular partners. Current approaches to protein representation in reaction prediction either ignore protein structure entirely or rely on static embeddings, failing to capture how proteins dynamically adapt their behavior to different substrates. We present Docking-Aware Attention (DAA), a novel architecture that generates dynamic, context-dependent protein representations by incorporating molecular docking information into the attention mechanism. DAA combines physical interaction scores from docking predictions with learned attention patterns to focus on protein regions most relevant to specific molecular interactions. We evaluate our method on enzymatic reaction prediction, where it outperforms previous state-of-the-art methods, achieving 62.2\% accuracy versus 56.79\% on complex molecules and 55.54\% versus 49.45\% on innovative reactions. Through detailed ablation studies and visualizations, we demonstrate how DAA generates interpretable attention patterns that adapt to different molecular contexts. Our approach represents a general framework for context-aware protein representation in biocatalysis prediction, with potential applications across enzymatic synthesis planning. We open-source our implementation and pre-trained models to facilitate further research.

DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for ensuring safety, reducing maintenance costs, and optimizing usage. However, predicting RUL is challenging due to the nonlinear characteristics of the degradation caused by complex chemical reactions. Machine learning allows precise predictions by learning the latent functions of degradation relationships based on cycling behavior. This study introduces an accurate RUL prediction approach based on feature engineering and DLinear, applied to the dataset from NASA's Prognostics Center of Excellence. Among the 20 features generated from current, voltage, temperature, and time provided in this dataset, key features contributing to degradation are selected using Pearson correlation coefficient and Shapley values. Shapley value-based feature selection effectively reflects cell-to-cell variability, showing similar importance rankings across all cells. The DLinear-based RUL prediction using key features efficiently captures the time-series trend, demonstrating significantly better performance compared to Long Short-Term Memory and Transformer models.

Constructing and explaining machine learning models for chemistry: example of the exploration and design of boron-based Lewis acids

The integration of machine learning (ML) into chemistry offers transformative potential in the design of molecules. However, the focus has often been on creating highly efficient predictive models, sometimes at the expense of interpretability. We leverage explainable AI techniques to explore the design of boron-based Lewis acids, which play a pivotal role in organic reactions. Using Fluoride Ion Affinity as a proxy for Lewis acidity, we developed interpretable ML models based on chemically meaningful descriptors, including ab initio features and substituent-based parameters. By constraining the chemical space to well-defined molecular scaffolds, we achieved highly accurate predictions, surpassing conventional black-box deep learning models in low-data regime. Interpretability analyses of the models unraveled the origin of Lewis acidity in these compounds and identified actionable levers to modulate it. This work bridges ML and chemist's way of thinking, demonstrating how explainable models can inspire molecular design and enhance scientific understanding of chemical reactivity.

Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniques

Understanding the long-time dynamics of complex physical processes depends on our ability to recognize patterns. To simplify the description of these processes, we often introduce a set of reaction coordinates, customarily referred to as collective variables (CVs). The quality of these CVs heavily impacts our comprehension of the dynamics, often influencing the estimates of thermodynamics and kinetics from atomistic simulations. Consequently, identifying CVs poses a fundamental challenge in chemical physics. Recently, significant progress was made by leveraging the predictive ability of unsupervised machine learning techniques to determine CVs. Many of these techniques require temporal information to learn slow CVs that correspond to the long timescale behavior of the studied process. Here, however, we specifically focus on techniques that can identify CVs corresponding to the slowest transitions between states without needing temporal trajectories as input, instead using the spatial characteristics of the data. We discuss the latest developments in this category of techniques and briefly discuss potential directions for thermodynamics-informed spatial learning of slow CVs.

Deep Learning Models for Colloidal Nanocrystal Synthesis

Colloidal synthesis of nanocrystals usually includes complex chemical reactions and multi-step crystallization processes. Despite the great success in the past 30 years, it remains challenging to clarify the correlations between synthetic parameters of chemical reaction and physical properties of nanocrystals. Here, we developed a deep learning-based nanocrystal synthesis model that correlates synthetic parameters with the final size and shape of target nanocrystals, using a dataset of 3500 recipes covering 348 distinct nanocrystal compositions. The size and shape labels were obtained from transmission electron microscope images using a segmentation model trained with a semi-supervised algorithm on a dataset comprising 1.2 million nanocrystals. By applying the reaction intermediate-based data augmentation method and elaborated descriptors, the synthesis model was able to predict nanocrystal's size with a mean absolute error of 1.39 nm, while reaching an 89% average accuracy for shape classification. The synthesis model shows knowledge transfer capabilities across different nanocrystals with inputs of new recipes. With that, the influence of chemicals on the final size of nanocrystals was further evaluated, revealing the importance order of nanocrystal composition, precursor or ligand, and solvent. Overall, the deep learning-based nanocrystal synthesis model offers a powerful tool to expedite the development of high-quality nanocrystals.

Learning Chemical Reaction Representation with Reactant-Product Alignment

Organic synthesis stands as a cornerstone of chemical industry. The development of robust machine learning models to support tasks associated with organic reactions is of significant interest. However, current methods rely on hand-crafted features or direct adaptations of model architectures from other domains, which lacks feasibility as data scales increase or overlook the rich chemical information inherent in reactions. To address these issues, this paper introduces {\modelname}, a novel chemical reaction representation learning model tailored for a variety of organic-reaction-related tasks. By integrating atomic correspondence between reactants and products, our model discerns the molecular transformations that occur during the reaction, thereby enhancing the comprehension of the reaction mechanism. We have designed an adapter structure to incorporate reaction conditions into the chemical reaction representation, allowing the model to handle diverse reaction conditions and adapt to various datasets and downstream tasks, e.g., reaction performance prediction. Additionally, we introduce a reaction-center aware attention mechanism that enables the model to concentrate on key functional groups, thereby generating potent representations for chemical reactions. Our model has been evaluated on a range of downstream tasks, including reaction condition prediction, reaction yield prediction, and reaction selectivity prediction. Experimental results indicate that our model markedly outperforms existing chemical reaction representation learning architectures across all tasks. Notably, our model significantly outperforms all the baselines with up to 25\% (top-1) and 16\% (top-10) increased accuracy over the strongest baseline on USPTO\_CONDITION dataset for reaction condition prediction. We plan to open-source the code contingent upon the acceptance of the paper.

A large language model-type architecture for high-dimensional molecular potential energy surfaces

Computing high dimensional potential surfaces for molecular and materials systems is considered to be a great challenge in computational chemistry with potential impact in a range of areas including fundamental prediction of reaction rates. In this paper we design and discuss an algorithm that has similarities to large language models in generative AI and natural language processing. Specifically, we represent a molecular system as a graph which contains a set of nodes, edges, faces etc. Interactions between these sets, which represent molecular subsystems in our case, are used to construct the potential energy surface for a reasonably sized chemical system with 51 dimensions. Essentially a family of neural networks that pertain to the graph-based subsystems, get the job done for this 51 dimensional system. We then ask if this same family of lower-dimensional neural networks can be transformed to provide accurate predictions for a 186 dimensional potential surface. We find that our algorithm does provide reasonably accurate results for this larger dimensional problem with sub-kcal/mol accuracy for the higher dimensional potential surface problem.

Chimera: Accurate retrosynthesis prediction by ensembling models with diverse inductive biases

Planning and conducting chemical syntheses remains a major bottleneck in the discovery of functional small molecules, and prevents fully leveraging generative AI for molecular inverse design. While early work has shown that ML-based retrosynthesis models can predict reasonable routes, their low accuracy for less frequent, yet important reactions has been pointed out. As multi-step search algorithms are limited to reactions suggested by the underlying model, the applicability of those tools is inherently constrained by the accuracy of retrosynthesis prediction. Inspired by how chemists use different strategies to ideate reactions, we propose Chimera: a framework for building highly accurate reaction models that combine predictions from diverse sources with complementary inductive biases using a learning-based ensembling strategy. We instantiate the framework with two newly developed models, which already by themselves achieve state of the art in their categories. Through experiments across several orders of magnitude in data scale and time-splits, we show Chimera outperforms all major models by a large margin, owing both to the good individual performance of its constituents, but also to the scalability of our ensembling strategy. Moreover, we find that PhD-level organic chemists prefer predictions from Chimera over baselines in terms of quality. Finally, we transfer the largest-scale checkpoint to an internal dataset from a major pharmaceutical company, showing robust generalization under distribution shift. With the new dimension that our framework unlocks, we anticipate further acceleration in the development of even more accurate models.

Exploring Hierarchical Molecular Graph Representation in Multimodal LLMs

Following the milestones in large language models (LLMs) and multimodal models, we have seen a surge in applying LLMs to biochemical tasks. Leveraging graph features and molecular text representations, LLMs can tackle various tasks, such as predicting chemical reaction outcomes and describing molecular properties. However, most current work overlooks the multi-level nature of graph features. The impact of different feature levels on LLMs and the importance of each level remain unexplored, and it is possible that different chemistry tasks require different feature levels. In this work, we first investigate the effect of feature granularity by fusing GNN-generated feature tokens, discovering that even reducing all tokens to a single token does not significantly impact performance. We then explore the effect of various feature levels on performance, finding that both the quality of LLM-generated molecules and performance on different tasks benefit from different feature levels. We conclude with two key insights: (1) current molecular Multimodal LLMs(MLLMs) lack a comprehensive understanding of graph features, and (2) static processing is not sufficient for hierarchical graph feature. Our code will be publicly available soon.