252 citations · 819 across the 18 of their papers we have counts for
18 papers
Ab initio-based Deep-Learning Prediction of Carrier Mobility in Strongly Anharmonic Materials
Juan Zhang, Boheng Zhao, Yang Li +4
Predicting charge transport in strongly anharmonic materials, particularly ultralow thermal conductors, remains a major challenge for first-principles methods. In such systems, per…
A high-performance and portable implementation of the SISSO method for CPUs and GPUs
Sebastian Eibl, Yi Yao, Matthias Scheffler +3
SISSO (sure-independence screening and sparsifying operator) is an artificial intelligence (AI) method based on symbolic regression and compressed sensing widely used in materials…
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
Kisung Kang, Thomas A. R. Purcell, Christian Carbogno +1
Molecular dynamics (MD) employing machine-learned interatomic potentials (MLIPs) serve as an efficient, urgently needed complement to ab initio molecular dynamics (aiMD). By traini…
Carrier Mobility of Strongly Anharmonic Materials from First Principles
Jingkai Quan, Christian Carbogno, Matthias Scheffler
First-principle approaches for phonon-limited electronic transport are typically based on many-body perturbation theory and transport equations. With that, they rely on the validit…
Self-interaction corrected SCAN functional for molecules and solids in the numeric atom-center orbital framework
Sheng Bi, Christian Carbogno, Igor Ying Zhang +1
Semilocal density-functional approximations (DFAs), including the state-of-the-art SCAN functional, are plagued by the self-interaction error (SIE). While this error is explicitly…
On the Uncertainty Estimates of Equivariant-Neural-Network-Ensembles Interatomic Potentials
Shuaihua Lu, Luca M. Ghiringhelli, Christian Carbogno +2
Machine-learning (ML) interatomic potentials (IPs) trained on first-principles datasets are becoming increasingly popular since they promise to treat larger system sizes and longer…
Recent advances in the SISSO method and their implementation in the SISSO++ code
Thomas A. R. Purcell, Matthias Scheffler, Luca M. Ghiringhelli
Accurate and explainable artificial-intelligence (AI) models are promising tools for the acceleration of the discovery of new materials, ore new applications for existing materials…
Extrapolation to complete basis-set limit in density-functional theory by quantile random-forest models
Daniel T. Speckhard, Christian Carbogno, Luca Ghiringhelli +3
The numerical precision of density-functional-theory (DFT) calculations depends on a variety of computational parameters, one of the most critical being the basis-set size. The ult…
Roadmap on Electronic Structure Codes in the Exascale Era
Vikram Gavini, Stefano Baroni, Volker Blum +38
Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state…
The NOMAD Artificial-Intelligence Toolkit: Turning materials-science data into knowledge and understanding
Luigi Sbailò, Ádám Fekete, Luca M. Ghiringhelli +1
We present the Novel-Materials-Discovery (NOMAD) Artificial-Intelligence (AI) Toolkit, a web-browser-based infrastructure for the interactive AI-based analysis of materials-science…
Shared Metadata for Data-Centric Materials Science
Luca M. Ghiringhelli, Carsten Baldauf, Tristan Bereau +32
The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps r…
FAIR data enabling new horizons for materials research
Matthias Scheffler, Martin Aeschlimann, Martin Albrecht +11
The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors su…
Accelerating Materials-Space Exploration for Thermal Insulators by Mapping Materials Properties via Artificial Intelligence
Thomas A. R. Purcell, Matthias Scheffler, Luca M. Ghiringhelli +1
Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications, including superconductivity, c…
Hierarchical symbolic regression for identifying key physical parameters correlated with bulk properties of perovskites
Lucas Foppa, Thomas A. R. Purcell, Sergey V. Levchenko +2
Symbolic regression identifies key physical parameters describing materials properties by uncovering correlations as nonlinear analytical expressions. However, the pool of expressi…
Artifcial-intelligence-driven discovery of catalyst \textit{genes} with application to CO2 activation on semiconductor oxides
Aliaksei Mazheika, Yanggang Wang, Rosendo Valero +5
Catalytic-materials design requires predictive modeling of the interaction between catalyst and reactants. This is challenging due to the complexity and diversity of structure-prop…
Theoretical evidence for unexpected O-rich phases at corners of MgO surfaces
Saswata Bhattacharya, Daniel Berger, Karsten Reuter +2
Realistic oxide materials are often semiconductors, in particular at elevated temperatures, and their surfaces contain undercoordiated atoms at structural defects such as steps and…
Uncovering structure-property relationships of materials by subgroup discovery
B. R. Goldsmith, M. Boley, J. Vreeken +2
Subgroup discovery (SGD) is presented here as a data-mining approach to help find interpretable local patterns, correlations, and descriptors of a target property in materials-scie…
Learning physical descriptors for materials science by compressed sensing
Luca M. Ghiringhelli, Jan Vybiral, Emre Ahmetcik +4
The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in th…