These molecular properties were calculated using a hybrid quantum simulation (Gaussian 09) at the B3LYP/6-31G(2df,p) level of theory. In this study, we created a subset of the QM9 dataset with a limited number of atoms, M 14, per molecule, which we refer to as the "QM9under14atoms" dataset in the main text. As the learning/predicting targets, we selected three kinds of energy properties: atomization energy at 0 K, zero point vibrational energy, and enthalpy at 298.15 K. E RN is the bias vector in layer ℓ. The LCAO considers the normalization for the coefficients in Eq. (6) in the main text. Additionally, the normalization term in Eq. (7) in the main text is calculated as follows: Z(qn,ζn)=

New discoveries in c hemistry and materials science, with increasingly expanding volume of requisite knowledge and experimental workload, provide unique opportunities for machine learning (ML) to take critical roles in accelerat ing research efficiency . Here, we demonstrate (1) the use of large language models (LLMs) for automated literature reviews, and (2) the training of an ML model to predict chemical knowledge (thermodynamic parameters) . Our LLM - based literature review tool (LMExt) successfully extracted chemical information and beyond into a machine - readable structure, including stability constants for metal cation - ligand interactions, thermodynamic properties, and other broader data types ( medical research papers, and financial reports), effectively overcoming the challenges inherent in each domain. Using the autonomous acquisition of thermodynamic data, an ML model was trained using the CatBoost algorithm for accurately predict ing thermodynamic parameters (e.g., enthalpy of formation) of minerals. This work highlights the transformative potential of integrated ML approaches to reshape chemistry and materials science research . Keywords: Thermodynamics, Machine L earning, Large Language Model, D ata M ining, Database Introduction Chemi cal thermodynamics are fundamental for understanding chemical reactions, proposing novel methods to control these reactions, and pred icting chemical equilibria /reactions for new materials. Although scientific breakthroughs occur regularly, contributing to these advances becomes progressively more complex. T ypical research project necessitates a comprehensive literature review that should cover the current state of the field and identify knowledge gaps . Subsequently, rigorous experimentation and modeling are performed to fill such gaps or check hypothesis - driven predictions . Both these steps are essential research steps not unique in chemical research, which however, are inherently mentally - intensive and time - consuming .

The predictive accuracy of density functional theory (DFT) for alloy formation enthalpies is often limited by intrinsic energy resolution errors, particularly in ternary phase stability calculations. In this work, we present a machine learning (ML) approach to systematically correct these errors, improving the reliability of first-principles predictions. A neural network model has been trained to predict the discrepancy between DFT-calculated and experimentally measured enthalpies for binary and ternary alloys and compounds. The model utilizes a structured feature set comprising elemental concentrations, atomic numbers, and interaction terms to capture key chemical and structural effects. By applying supervised learning and rigorous data curation we ensure a robust and physically meaningful correction. The model is implemented as a multi-layer perceptron (MLP) regressor with three hidden layers, optimized through leave-one-out cross-validation (LOOCV) and k-fold cross-validation to prevent overfitting. We illustrate the effectiveness of this method by applying it to the Al-Ni-Pd and Al-Ni-Ti systems, which are of interest for high-temperature applications in aerospace and protective coatings.

In the quest for electrically-driven soft actuators, the focus has shifted away from liquid-gas phase transition, commonly associated with reduced strain rates and actuation delays, in favour of electrostatic and other electrothermal actuation methods. This prevented the technology from capitalizing on its unique characteristics, particularly: low voltage operation, controllability, scalability, and ease of integration into robots. Here, we introduce a phase transition electric soft actuator capable of strain rates of over 16%/s and pressurization rates of 100 kPa/s, approximately one order of magnitude higher than previous attempts. Blocked forces exceeding 50 N were achieved while operating at voltages up to 24 V. We propose a method for selecting working fluids which allows for application-specific optimization, together with a nonlinear control approach that reduces both parasitic vibrations and control lag. We demonstrate the integration of this technology in soft robotic systems, including the first quadruped robot powered by liquid-gas phase transition.

Exploring molecular spaces is crucial for advancing our understanding of chemical properties and reactions, leading to groundbreaking innovations in materials science, medicine, and energy. This paper explores an approach for active learning in molecular discovery using Deep Kernel Learning (DKL), a novel approach surpassing the limits of classical Variational Autoencoders (VAEs). Employing the QM9 dataset, we contrast DKL with traditional VAEs, which analyze molecular structures based on similarity, revealing limitations due to sparse regularities in latent spaces. DKL, however, offers a more holistic perspective by correlating structure with properties, creating latent spaces that prioritize molecular functionality. This is achieved by recalculating embedding vectors iteratively, aligning with the experimental availability of target properties. The resulting latent spaces are not only better organized but also exhibit unique characteristics such as concentrated maxima representing molecular functionalities and a correlation between predictive uncertainty and error. Additionally, the formation of exclusion regions around certain compounds indicates unexplored areas with potential for groundbreaking functionalities. This study underscores DKL's potential in molecular research, offering new avenues for understanding and discovering molecular functionalities beyond classical VAE limitations.

In the last decade, the use of Machine and Deep Learning (MDL) methods in Condensed Matter physics has seen a steep increase in the number of problems tackled and methods employed. A number of distinct MDL approaches have been employed in many different topics; from prediction of materials properties to computation of Density Functional Theory potentials and inter-atomic force fields. In many cases the result is a surrogate model which returns promising predictions but is opaque on the inner mechanisms of its success. On the other hand, the typical practitioner looks for answers that are explainable and provide a clear insight on the mechanisms governing a physical phenomena. In this work, we describe a proposal to use a sophisticated combination of traditional Machine Learning methods to obtain an explainable model that outputs an explicit functional formulation for the material property of interest. We demonstrate the effectiveness of our methodology in deriving a new highly accurate expression for the enthalpy of formation of solid solutions of lanthanides orthophosphates.

Graph Neural Networks (GNNs) have recently gained attention in the machine learning community. They have achieved state-of-the-art performance in a number of tasks by leveraging the geometric prior inherent to many real-world problems [1]. Concurrently, several model-agnostic algorithms for meta-learning have been developed, such as Model-Agnostic Meta-Learning (MAML) [2] and Reptile [3]. Although as their name suggests these algorithms are model agnostic, works in the literature have mainly applied them to classical fully-connected and convolutional neural networks. In this paper, we explore the application of Reptile to GNN regression tasks. We show that modelagnostic algorithms for meta-learning are also applicable to GNNs and specifically, that meta-learning can exploit the underlying structure of molecules to quickly adapt models to learning new molecular regression tasks. We experimentally demonstrate that GNN expressivity is correlated to metalearning performance. Finally, we also show that using GNN ensembles can even further improve meta-learning.