Publications, Activities, and Awards
- Accelerated discovery of perovskites and prediction of band gaps using machine-learning methods
- Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome Them
- Accelerating the Discovery of Materials with Machine Learning: Potential Roadblocks and How to Overcome Them
- Accelerating the Discovery of Materials: Machine-Learning Approach
- Accelerating the Discovery of Solid State Materials with Machine-Learning Approaches
- Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
- Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
- Accelerating the Discovery of Solid State Materials: From Traditional to Machine-Learning Approaches
- Alkaline Earth Metal-Organic Frameworks with Tailorable Ion Release: A Path for Supporting Biomineralization
- Classification of Half-Heusler Compounds through Machine Learning Approaches
- Classification of Half-Heusler Compounds through Machine-Learning Approaches
- Coloured intermetallic compounds LiCu2Al and LiCu2Ga
- Computational workshop
- Design of Experiments and Machine Learning-Assisted Organic Solar Cell Efficiency Optimization
- Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches
- Discovery of Noncentrosymmetric Ternary Compounds from Elemental Composition: A Machine-Learning Approach
- Discovery of ternary noncentrosymmetric compounds: A machine-learning approach with experimental proof
- Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC
- Effectively Exploring Parameter Space: Design of Experiments and Machine Learning-assisted Organic Solar Cell Efficiency Optimization
- Exploring the colours of gold alloys with machine learning
- Half-Heusler Structures with Full-Heusler Counterparts: Machine-Learning Predictions and Experimental Validation
- Hexagonal Double Perovskite Cs2AgCrCl6
- How large is an atom?
- How to look for compounds
- How to look for compounds
- How to look for compounds
- In Search of Coloured Intermetallics
- Introduction to Machine Learning: A Practical Workshop
- Invited talk: ACS national meeting, Boston
- Machine Learning and Models: How we find optimal materials for Solar and CCS technologies
- Machine learning in solid-state chemistry: Heusler compounds
- Not Just Par for the Course: 73 Quaternary Germanides RE4M2XGe4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for Intermetallics with Low Thermal Conductivity
- Optimization of Organic Solar Cell Efficiency via Machine Learning and Design of Experiments
- Predicting noncentrosymmetric quaternary tellurides using machine learning
- Prediction of Novel Compounds and Rapid Property Screening through a Machine Learning Approach
- Quarternary Rare-earth Transition-Metal Germanides: RE4M2CdGe4 and RE4M2AgGe4 (RE=La-SM, Gd-Lu, M=Mn-Ni)
- Quaternary rare-earth sulfides RE3M0.5M'S7 (M = Zn, Cd; M' = Si, Ge)
- Quaternary Rare-Earth Transition-Metal Germanides RE4M2CdGe4 and RE4M2AgGe4 (RE = La–Sm, Gd–Tm, Lu; M = Mn–Ni)
- Rare-earth transition-metal oxyselenides
- Revealing hidden patterns through chemical intuition and interpretable machine learning: A case study of binary rare-earth intermetallics RX
- Revealing hidden patterns through chemical intuition and interpretable machine learning: A case study of binary rare-earth intermetallics RX
- Searching for Missing Binary Equiatomic Phases: Complex Crystal Chemistry in the Hf–In System
- Solving the Colouring Problem in Half- Heusler Structures: Machine-Learning Predictions and Experimental Validation
- Solving the Colouring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation
- Synthesis, structure, and properties of rare-earth germanium sulfide iodides RE3Ge2S8I (RE = La, Ce, Pr)
- Ternary Germanides in Ce-M-Ge System (M=Rh, Co)
- Ternary Germanides in the Ce–M–Ge (M = Rh, Co) Systems
- Ternary phases in the Yb-Cu-Ga and Yb-Ni-Ga systems
- Ternary rare-earth-metal nickel indides RE23Ni7In4 (RE = Gd, Tb, Dy) with Yb23Cu7Mg4-type structure
- Three Rh-rich ternary germanides in the Ce-Rh-Ge system
- USchool: Materials and Informatics