Electronic Structure; Structure Prediction; Materials Design; Simulation; Data Mining; Machine Learning
Amsler Maximilian, Wolverton Chris (2017), Dense superconducting phases of copper-bismuth at high pressure, in Physical Review Materials
, 1(3), 031801-031801.
Fisicaro Giuseppe, Sicher Michael, Amsler Maximilian, Saha Santanu, Genovese Luigi, Goedecker Stefan (2017), Surface reconstruction of fluorites in vacuum and aqueous environment, in Physical Review Materials
, 1(3), 033609-033609.
Rasoulkhani Robabe, Tahmasbi Hossein, Ghasemi S. Alireza, Faraji Somayeh, Rostami Samare, Amsler Maximilian (2017), Energy landscape of ZnO clusters and low-density polymorphs, in Physical Review B
, 96(6), 064108-064108.
Clarke Samantha M., Amsler Maximilian, Walsh James P. S., Yu Tony, Wang Yanbin, Meng Yue, Jacobsen Steven D., Wolverton Chris, Freedman Danna E. (2017), Creating Binary Cu–Bi Compounds via High-Pressure Synthesis: A Combined Experimental and Theoretical Study, in Chemistry of Materials
, 29(12), 5276-5285.
Ohno Saneyuki, Aydemir Umut, Amsler Maximilian, Pöhls Jan-Hendrik, Chanakian Sevan, Zevalkink Alex, White Mary Anne, Bux Sabah K., Wolverton Chris, Snyder G. Jeffrey (2017), Achieving zT > 1 in Inexpensive Zintl Phase Ca 9 Zn 4+x Sb 9 by Phase Boundary Mapping, in Advanced Functional Materials
, 27(20), 1606361-1606361.
Faraji Somayeh, Ghasemi S. Alireza, Rostami Samare, Rasoulkhani Robabe, Schaefer Bastian, Goedecker Stefan, Amsler Maximilian (2017), High accuracy and transferability of a neural network potential through charge equilibration for calcium fluoride, in Physical Review B
, 95(10), 104105-104105.
Powderly K. M., Clarke S. M., Amsler M., Wolverton C., Malliakas C. D., Meng Y., Jacobsen S. D., Freedman D. E. (2017), High-pressure discovery of β-NiBi, in Chem. Commun.
, 53(81), 11241-11244.
Amsler Maximilian, Naghavi S. Shahab, Wolverton Chris (2017), Prediction of superconducting iron–bismuth intermetallic compounds at high pressure, in Chem. Sci.
, 8(3), 2226-2234.
Eivari Hossein Asnaashari, Ghasemi S. Alireza, Tahmasbi Hossein, Rostami Samare, Faraji Somayeh, Rasoulkhani Robabe, Goedecker Stefan, Amsler Maximilian (2017), Two-Dimensional Hexagonal Sheet of TiO2, in Chemistry of Materials
, 0(0), 0.
He Jiangang, Amsler Maximilian, Xia Yi, Naghavi S. Shahab, Hegde Vinay I., Hao Shiqiang, Goedecker Stefan, Ozoliņš Vidvuds, Wolverton Chris (2016), Ultralow Thermal Conductivity in Full Heusler Semiconductors, in Physical Review Letters
, 117(4), 046602-046602.
Zhu Li, Amsler Maximilian, Fuhrer Tobias, Schaefer Bastian, Faraji Somayeh, Rostami Samare, Ghasemi S Alireza, Sadeghi Ali, Grauzinyte Migle, Wolverton Chris, Goedecker Stefan (2016), A fingerprint based metric for measuring similarities of crystalline structures., in The Journal of chemical physics
, 144(3), 034203-034203.
Valencia-Jaime Irais, Sarmiento-Pérez Rafael, Botti Silvana, Marques Miguel A.L., Amsler M., Goedecker S., Romero Aldo H. (2016), Novel crystal structures for lithium–silicon alloy predicted by minima hopping method, in Journal of Alloys and Compounds
, 655, 147-154.
Flores-Livas José A., Amsler Maximilian, Heil Christoph, Sanna Antonio, Boeri Lilia, Profeta Gianni, Wolverton Chris, Goedecker Stefan, Gross E. K. U. (2016), Superconductivity in metastable phases of phosphorus-hydride compounds under high pressure, in Phys. Rev. B
, 93, 020508-020508.
Amsler Maximilian, Goedecker Stefan, Zeier Wolfgang G., Snyder G Jeffrey, Wolverton Chris, Chaput Laurent (2016), ZnSb Polymorphs with Improved Thermoelectric Properties, in Chemistry of Materials
Materials design has become one of the most rapidly increasing fields of condensed matter science during the last years. Discovery of novel materials are called for with potential applications in various technologically relevant fields such as energy production and storage, novel electronic devices, data storage, medical application and catalysis. Instead of synthesizing and characterizing novel materials intime-consuming, expensive experiments, theoretical material scientists have started to create databases obtained from all materials known to date and to systematically analyze them with high-throughput computation methods, trying to gain insight on how chemistry and structures are linked to material properties and to extract patterns from the underlying data. New, innovative computational methods are needed to efficiently characterize the continuously growing amount of materials data, and techniques need to be developed to extrapolate materials properties to design novel, undiscovered compounds with improved properties.The presented project is aimed at solving two key limitations in materials design. First, most structure prediction methods do not access the knowledge stored in the large structural databases. By combining the Minima Hopping structure prediction scheme and machine-learning techniques a sophisticated method will be developed to explore new chemistries and to discover compounds with improved or new properties in energy applications. The new method will be applied to real-life challenges in materials design to find thermoelectric and hydrogen storage materials, or materials for use in photovoltaic applications. Second, Quantum Monte Carlo methods will be used to refine the energetic ordering of different phases in various compounds to significantly improve the predictive power by going beyond the accuracy of conventional density functional theory calculations used in current approaches. The outcome of this project will be of great value for many material scientists and will considerably accelerate the theoretical discovery of novel materials.