Related papers: First-Principles Prediction of Electronic Transpor…
Electron transfer (ET) at molecule-metal or molecule-semiconductor interfaces is a fundamental reaction that underlies all electro-chemical and molecular electronic processes as well as substrate-mediated surface photochemistry. In this…
Thermoelectric effects, measured by the Seebeck coefficients, refer to the phenomena in which a temperature difference or gradient imposed across a thermoelectric material induces an electrical potential difference or gradient, and vice…
Machine learning has become a crucial tool for predicting the properties of crystalline materials. However, existing methods primarily represent material information by constructing multi-edge graphs of crystal structures, often overlooking…
Quantum materials are driving a technology revolution in sensing, communication, and computing, while simultaneously testing many core theories of the past century. Materials such as topological insulators, complex oxides, quantum dots,…
Over the past decade inter-atomic potentials based on machine-learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure…
The discovery of novel superconducting materials is a longstanding challenge in materials science, with a wealth of potential for applications in energy, transportation, and computing. Recent advances in artificial intelligence (AI) have…
A ferromagnetic insulator (FI) attached to a conventional superconductor (S) changes drastically the properties of the latter. Specifically, the exchange field at the FI/S interface leads to a splitting of the superconducting density of…
Thermoelectric materials create an electric potential when subject to a temperature gradient and vice versa hence they can be used to harvest waste heat into electricity and in thermal management applications. However, finding highly…
An algorithm for first-principles electronic structure calculations having a computational cost which scales linearly with the system size is presented. Our method exploits the real-space localization of the density matrix, and in this…
Ionic mobility determines the rate performance of several applications, such as batteries, fuel cells, and electrochemical sensors and is exponentially dependent on the migration barrier ($E_m$), a difficult to measure/calculate quantity.…
Electrical Impedance Tomography (EIT) is a powerful imaging technique with diverse applications, e.g., medical diagnosis, industrial monitoring, and environmental studies. The EIT inverse problem is about inferring the internal conductivity…
We present a simple and efficient approximation to the electron-phonon scattering rate suitable for high-throughput screening of candidate materials for thermoelectric devices, based on electronic transport. The method is applied to…
The study of the electronic properties of charged defects is crucial for our understanding of various electrical properties of materials. However, the high computational cost of density functional theory (DFT) hinders the research on large…
Photoplasticity, the light-induced change in plastic deformation, plays a pivotal role in the mechanical durability and manufacturing of semiconductor materials. Yet, its governing mechanisms remain incompletely understood, owing to the…
Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving electronic observables such as dipole moments and…
This chapter provides a tutorial overview of first principles methods to describe the properties of matter at the ground state or equilibrium. It begins with a brief introduction to quantum and statistical mechanics for predicting the…
We develop inductive biases for the machine learning of complex physical systems based on the port-Hamiltonian formalism. To satisfy by construction the principles of thermodynamics in the learned physics (conservation of energy,…
Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic…
Using first-principles pseudopotential method and Boltzmann transport theory, we give a comprehensive understanding of the electronic and phonon transport properties of thermoelectric material BiCuSeO. By choosing proper hybrid functional…
Nanostructured materials enable high thermal transport tunability, holding promises for thermal management and heat harvesting applications. Predicting the effect that nanostructuring has on thermal conductivity requires models, such as the…