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Transition-metal complexes (TMCs) are promising photosensitizers for Type~I photodynamic therapy (PDT), where electron-transfer processes can generate reactive oxygen species under hypoxic conditions. Yet identifying candidates with the…
Ion-conducting solid electrolytes are widely used for a variety of purposes. Therefore, designing highly ion-conductive materials is in strongly demand. Because of advancement in computers and enhancement of computational codes, theoretical…
The rapid evolution of artificial intelligence in drug discovery encounters challenges with generalization and extensive training, yet Large Language Models (LLMs) offer promise in reshaping interactions with complex molecular data. Our…
The Li-Sn binary system has been the focus of extensive research because it features Li-rich alloys with potential applications as battery anodes. Our present re-examination of the binary system with a combination of machine learning and ab…
One of the main goals and challenges of materials discovery is to find the best candidates for each interest property or application. Machine learning rises in this context to efficiently optimize this search, exploring the immense…
Creating a single unified interatomic potential capable of attaining ab initio accuracy across all chemistry remains a long-standing challenge in computational chemistry and materials science. This work introduces a training protocol for…
Solid-state lithium batteries (SLBs) offers a promising avenue for the development of next-generation lithium-ion batteries with ultrahigh energy density and safety performance. This review provides a quick overview of the state-of-the-art…
We critically reexamine the superconducting properties of rock-salt transition-metal carbides (TMCs), often regarded as textbook conventional superconductors, combining first-principles electron-phonon calculations with variable-composition…
Thin-layer chromatography (TLC) is a crucial technique in molecular polarity analysis. Despite its importance, the interpretability of predictive models for TLC, especially those driven by artificial intelligence, remains a challenge.…
Topological materials discovery has evolved at a rapid pace over the past 15 years following the identification of the first nonmagnetic topological insulators (TIs), topological crystalline insulators (TCIs), and 3D topological semimetals…
Investigating Li$^+$ transport within the amorphous lithium phosphorous oxynitride (LiPON) framework, especially across a Li||LiPON interface, has proven challenging due to its amorphous nature and varying stoichiometry, necessitating large…
Solid-state materials with high ionic conduction are necessary to many technologies including all-solid-state Li-ion batteries. Understanding how crystal structure dictates ionic diffusion is at the root of the development of fast ionic…
Semiconductor superlattices are interesting for two distinct reasons: the possibility to design their structure (band-width(s),doping, etc.) gives access to a large parameter space where different physical phenomena can be explored.…
We predict a new class of three dimensional topological insulators in thallium-based III-V-VI$_2$ ternary chalcogenides, including TlBiQ$_2$ and TlSbQ$_2$ (Q = Te, Se and S). These topological insulators have robust and simple surface…
The International Linear Collider (ILC) is the next large scale project in accelerator particle physics. Colliding electrons with positrons at energies from 0.3 TeV up to about 1 TeV, the ILC is expected to provide the accuracy needed to…
Contrastive learning (CL) has emerged as a powerful paradigm for learning transferable representations without the reliance on large labeled datasets. Its ability to capture intrinsic similarities and differences among data samples has led…
High-quality topological insulator-superconductor (TI-SC) heterostructure with an atomically sharp and well-controlled interface is crucial for realizing topological superconductivity and topological quantum qubit. In particular, many…
Ionic liquids are promising candidates for novel electrolytes as they possess large electrochemical and thermodynamic stability and offer a high degree of tunability. As purely-ionic electrolyte without neutral solvent they exhibit…
Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse datasets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000…
Network traffic classification (NTC) is vital for efficient network management, security, and performance optimization, particularly with 5G/6G technologies. Traditional methods, such as deep packet inspection (DPI) and port-based…