Active learning for accelerated design of layered materials
L Bassman Oftelie, P Rajak, RK Kalia… - npj Computational …, 2018 - nature.com
Hetero-structures made from vertically stacked monolayers of transition metal
dichalcogenides hold great potential for optoelectronic and thermoelectric devices …
dichalcogenides hold great potential for optoelectronic and thermoelectric devices …
Hunting for organic molecules with artificial intelligence: molecules optimized for desired excitation energies
This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted)
chemistry where a machine-learning-based molecule generator is coupled with density …
chemistry where a machine-learning-based molecule generator is coupled with density …
Application of DFT-based machine learning for developing molecular electrode materials in Li-ion batteries
In this study, we utilize a density functional theory-machine learning framework to develop a
high-throughput screening method for designing new molecular electrode materials. For this …
high-throughput screening method for designing new molecular electrode materials. For this …
Assessment of the GLLB-SC potential for solid-state properties and attempts for improvement
Based on the work of Gritsenko et al.(GLLB)[Phys. Rev. A 51, 1944 (1995)], the method of
Kuisma et al.[Phys. Rev. B 82, 115106 (2010)] to calculate the band gap in solids was …
Kuisma et al.[Phys. Rev. B 82, 115106 (2010)] to calculate the band gap in solids was …
Machine learning assisted optimization of electrochemical properties for Ni-rich cathode materials
Optimizing synthesis parameters is the key to successfully design ideal Ni-rich cathode
materials that satisfy principal electrochemical specifications. We herein implement machine …
materials that satisfy principal electrochemical specifications. We herein implement machine …
Prediction of interstitial diffusion activation energies of nitrogen, oxygen, boron and carbon in bcc, fcc, and hcp metals using machine learning
Study of diffusion in solids is of fundamental importance in understanding the materials
properties such as phase transformations, segregation, and corrosion in processing and …
properties such as phase transformations, segregation, and corrosion in processing and …
Density Functional Theory–Machine Learning Approach to Analyze the Bandgap of Elemental Halide Perovskites and Ruddlesden‐Popper Phases
In this study, we have developed a protocol for exploring the vast chemical space of possible
perovskites and screening promising candidates. Furthermore, we examined the factors that …
perovskites and screening promising candidates. Furthermore, we examined the factors that …
[HTML][HTML] Physics-informed machine learning for inorganic scintillator discovery
Applications of inorganic scintillators—activated with lanthanide dopants, such as Ce and
Eu—are found in diverse fields. As a strict requirement to exhibit scintillation, the 4f ground …
Eu—are found in diverse fields. As a strict requirement to exhibit scintillation, the 4f ground …
Machine learning constrained with dimensional analysis and scaling laws: simple, transferable, and interpretable models of materials from small datasets
N Kumar, P Rajagopalan, P Pankajakshan… - Chemistry of …, 2018 - ACS Publications
Machine learning (ML) from materials databases can accelerate the design and discovery of
new materials through the development of accurate, computationally inexpensive models to …
new materials through the development of accurate, computationally inexpensive models to …
Machine learning properties of binary wurtzite superlattices
The burgeoning paradigm of high-throughput computations and materials informatics brings
new opportunities in terms of targeted materials design and discovery. The discovery …
new opportunities in terms of targeted materials design and discovery. The discovery …