Challenges Reconciling Theory and Experiments in the Prediction of Lattice Thermal Conductivity: The Case of Cu-Based Sulvanites.

The exploration of large chemical spaces in search of new thermoelectric materials requires the integration of experiments, theory, simulations, and data science. The development of high-throughput strategies that combine DFT calculations with machine learning has emerged as a powerful approach to discovering new materials. However, experimental validation is crucial to confirm the accuracy of these workflows.

Theoretical Investigation of the Lattice Thermal Conductivities of II-IV-V Pnictide Semiconductors.

Ternary pnictide semiconductors with II-IV-V stoichiometry hold potential as cost-effective thermoelectric materials with suitable electronic transport properties, but their lattice thermal conductivities (κ) are typically too high. Insights into their vibrational properties are therefore crucial to finding strategies to reduce κ and achieve improved thermoelectric performance. We present a theoretical exploration of the lattice thermal conductivities for a set of pnictide semiconductors with ABX composition (A = Zn, Cd; B = Si, Ge, Sn; and X = P, As) using machine-learning-based regression algorithms to extract force constants from a reduced number of density functional theory simulations and then solving the Boltzmann transport equation for phonons.

Design Principles Guided by DFT Calculations and High-Throughput Frameworks for the Discovery of New Diamond-like Chalcogenide Thermoelectric Materials.

Rational design principles are one pathway to discovering new materials. However, technological breakthroughs rarely occur in this way because these design principles are usually based on incremental advances that seldom lead to disruptive applications. The emergence of machine-learning (ML) and high-throughput (HT) techniques has changed the paradigm, opening up new possibilities for efficiently screening large chemical spaces and creating on-the-fly design principles for the discovery of novel materials with desired properties.

High-Throughput Prediction of the Thermal and Electronic Transport Properties of Large Physical and Chemical Spaces Accelerated by Machine Learning: Charting the of Binary Skutterudites.

Thermal and electronic transport properties are the keys to many technological applications of materials. Thermoelectric, TE, materials can be considered a singular case in which not only one but three different transport properties are combined to describe their performance through their TE figure of merit, . Despite the availability of high-throughput experimental techniques, synthesizing, characterizing, and measuring the properties of samples with numerous variables affecting are not a cost- or time-efficient approach to lead this strategy.

Hypochlorous Acid as a Potential Postsurgical Antimicrobial Agent in Periodontitis: A Randomized, Controlled, Non-Inferiority Trial.

Background: Hypochlorous acid (HOCl) is an antimicrobial agent with high affinity to Gram-negative bacteria of the subgingival biofilm. It could have an equivalent or no inferiority effect to chlorhexidine (CHX) to avoid recolonization of these microorganisms after the post-surgical period.

Objective: The objective is to compare the reduction of plaque index (PI), gingival index (GI), pocket depth (PD), gain of clinical attachment level (CAL), and bacterial recolonization of periodontopathic microorganisms in subgingival biofilm at 7, 21, and 90 days after Open Flap Debridement (OFD) under two antimicrobial protocols: (A) HOCl 0.