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The discovery of superconducting materials has led to their use in technological marvels such as magnetic-field sensors in MRI machines, powerful research magnets, short transmission cables, and high-speed trains. Despite such applications,…
Soft materials with a liquid component are an emerging paradigm in materials design. The incorporation of a liquid phase, such as water, liquid metals, or complex fluids, into solid materials imparts unique properties and characteristics…
Discovering new materials can have significant scientific and technological implications but remains a challenging problem today due to the enormity of the chemical space. Recent advances in machine learning have enabled data-driven methods…
The superconducting diode effect may exist in bulk systems as well as in junctions when time-reversal and inversion symmetries are simultaneously broken. Magnetization gradients and textures satisfy both requirements and therefore also…
Semiconductor research has shifted towards exploring two-dimensional (2D) materials as candidates for next-generation electronic devices due to the limitations of existing silicon technology. Transition Metal Dichalcogenides (TMDCs) stand…
Droplets, sub-millilitre liquid volumes with at least one interface, have traditionally served as compartments for storing, transporting, and delivering materials. Beyond familiar applications in food, coatings, and consumer goods, they…
We propose a method to probe the local density of states (LDOS) of atomic systems that provides both spatial and energy resolution. The method combines atomic and tunneling techniques to supply a simple, yet quantitative and operational,…
Liquid-liquid phase transition (LLPT) in supercooled water has been a long-standing controversial issue. We show simulation results of real stable first-order phase transitions between high and low density liquid (HDL and LDL)-like…
Self-driving laboratories (SDLs), by combining automation with machine learning-guided experiment selection, have the potential to transform experimental materials science. To date, most SDLs have been optimisation-driven, designed to…
Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors,…
The search for superconductivity in materials iso-structural and iso-valent with magnesium diboride, MgB2, has not yielded any results close enough to the 39K Tc of MgB2. Lithium magnesium nitride, LiMgN, resembles MgB2 in that they both…
Presently, the main method of producing new high-temperature superconductors is chemical when record temperatures of the superconducting transition $T_c$ are reached due to changes in the chemical structure of a superconductor. This paper…
Large language models (LLMs) are beginning to reshape how chemists plan and run reactions in organic synthesis. Trained on millions of reported transformations, these text-based models can propose synthetic routes, forecast reaction…
The observation of metallic ground states in a variety of two-dimensional electronic systems poses a fundamental challenge for the theory of electron fluids. Here, we analyze evidence for the existence of a regime, which we call the…
Lidar point cloud synthesis based on generative models offers a promising solution to augment deep learning pipelines, particularly when real-world data is scarce or lacks diversity. By enabling flexible object manipulation, this synthesis…
Metamaterials are artificial structures which have recently enabled the realization of novel electromagnetic components with engineered and even unnatural functionalities. Existing metamaterials are specifically designed for a single…
Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and reasoning. In this paper, we apply fully-convolutional deep…
Large language models (LLMs) have demonstrated rapid progress across a wide array of domains. Owing to the very large number of parameters and training data in LLMs, these models inherently encompass an expansive and comprehensive materials…
Molecular dynamics simulations are a cornerstone in science, allowing to investigate from the system's thermodynamics to analyse intricate molecular interactions. In general, to create extended molecular trajectories can be a…
We propose a method to fuse frozen text-only large language models (LLMs) with pre-trained image encoder and decoder models, by mapping between their embedding spaces. Our model demonstrates a wide suite of multimodal capabilities: image…