Anjana Talapatra
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Anjana Talapatra has a total of 112 co-authors. Here are the top co-authors by number of shared publications:
- Raymundo Arróyave
Department of Materials Science and Engineering, Texas A&M University, College Station, TX 77843, USA
8 shared publications Raymundo Arróyave
6 shared publicationsThien Duong
Department of Materials Science and Engineering, Texas A&M University , College Station, Texas 77843-3123, USA
5 shared publications- Blas Pedro Uberuaga
Los Alamos National Laboratory
4 shared publications Miladin Radovic
Department of Materials Science and Engineering, Texas A&M University , College Station, Texas 77843-3123, USA
4 shared publicationsNavdeep Singh
University of Houston
4 shared publications
Latest Publications
- Open Access
- 16 September 2026
- Published by Royal Society of Chemistry (RSC) in Digital Discovery
- Vol. 5 (9) , 3584-3597
Data-driven insights to accelerate advanced biomanufacturing
Integrating literature data with ML and experimental validation enables new insights to advance biomanufacturing of PHB biopolymers.
- 16 September 2026
- Published by Royal Society of Chemistry (RSC) in Digital Discovery
- Vol. 5 (9) , 3584-3597
Open AccessData-driven insights to accelerate advanced biomanufacturing
Integrating literature data with ML and experimental validation enables new insights to advance biomanufacturing of PHB biopolymers.
- 15 October 2025
- Published by American Physical Society (APS) in Physical Review Materials
- Vol. 9 (10)
Relationship between atomic and electronic structure in Ln-bearing oxides
- 15 October 2025
- Published by American Physical Society (APS) in Physical Review Materials
- Vol. 9 (10)
Relationship between atomic and electronic structure in Ln-bearing oxides
- 16 July 2025
- Published by Elsevier in Computer Physics Communications
- Vol. 316
Best of both worlds: Enforcing detailed balance in machine learning models of transition rates
- 16 July 2025
- Published by Elsevier in Computer Physics Communications
- Vol. 316
Best of both worlds: Enforcing detailed balance in machine learning models of transition rates
- Open Access
- 6 November 2024
- Published by Office of Scientific and Technical Information (OSTI)
Linking Spatiotemporal Biological Data to Predict Harmful Algal Blooms
The U.S. Department of Energy's Office of Scientific and Technical Information
- 6 November 2024
- Published by Office of Scientific and Technical Information (OSTI)
Open AccessLinking Spatiotemporal Biological Data to Predict Harmful Algal Blooms
The U.S. Department of Energy's Office of Scientific and Technical Information
- 21 September 2024
- Published by Elsevier in International Journal of Plasticity
- Vol. 182
Rapid assessment of the creep rupture life of metals: A model enabling experimental design
- 21 September 2024
- Published by Elsevier in International Journal of Plasticity
- Vol. 182
Rapid assessment of the creep rupture life of metals: A model enabling experimental design
- Open Access
- 2 October 2023
- Published by Office of Scientific and Technical Information (OSTI)
LANL end of the year report (2023)
The U.S. Department of Energy's Office of Scientific and Technical Information
- 2 October 2023
- Published by Office of Scientific and Technical Information (OSTI)
Open AccessLANL end of the year report (2023)
The U.S. Department of Energy's Office of Scientific and Technical Information
- Open Access
- 10 June 2023
- Published by Springer Nature in Communications Materials
- Vol. 4 (1) , 1-14
Band gap predictions of double perovskite oxides using machine learning
The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap Eg, a key material parameter for these applications, can be optimally controlled by varying the composition. Here, we implement a hierarchical screening process in which two cross-validated and predictive machine learning models for band gap classification and regression, trained using exhaustive datasets that span 68 elements of the periodic table, are applied sequentially. The classification model separates wide band gap materials, with Eg ≥ 0.5 eV, from materials which have zero or relatively small band gaps, namely Eg < 0.5 eV, and the second regression model quantitatively predicts the gap value of the wide band gap compounds. The study down-selects 13,589 cubic oxide perovskite compositions that are predicted to be experimentally formable, thermodynamically stable, and have a wide band gap. Of these, a subset of 310 compounds, which are predicted to be stable and formable with a confidence greater than 90%, are identified for further investigation. Our models are methodically analyzed via performance metrics and inter-dependence of model features to gain physical insight into the band gap prediction problem. Design maps to identify the variation of band gap with substitution of different elements are also presented.
- 10 June 2023
- Published by Springer Nature in Communications Materials
- Vol. 4 (1) , 1-14
Open AccessBand gap predictions of double perovskite oxides using machine learning
The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap Eg, a key material parameter for these applications, can be optimally controlled by varying the composition. Here, we implement a hierarchical screening process in which two cross-validated and predictive machine learning models for band gap classification and regression, trained using exhaustive datasets that span 68 elements of the periodic table, are applied sequentially. The classification model separates wide band gap materials, with Eg ≥ 0.5 eV, from materials which have zero or relatively small band gaps, namely Eg < 0.5 eV, and the second regression model quantitatively predicts the gap value of the wide band gap compounds. The study down-selects 13,589 cubic oxide perovskite compositions that are predicted to be experimentally formable, thermodynamically stable, and have a wide band gap. Of these, a subset of 310 compounds, which are predicted to be stable and formable with a confidence greater than 90%, are identified for further investigation. Our models are methodically analyzed via performance metrics and inter-dependence of model features to gain physical insight into the band gap prediction problem. Design maps to identify the variation of band gap with substitution of different elements are also presented.
- 30 July 2021
- Published by Office of Scientific and Technical Information (OSTI)
A Machine Learning aided hierarchical screening strategy for materials discovery [Slides]
The U.S. Department of Energy's Office of Scientific and Technical Information
- 30 July 2021
- Published by Office of Scientific and Technical Information (OSTI)
A Machine Learning aided hierarchical screening strategy for materials discovery [Slides]
The U.S. Department of Energy's Office of Scientific and Technical Information
- Open Access
- 16 July 2021
- Published by Springer Nature in Journal of Materials Science
- Vol. 56 (28) , 15812-15823
Barriers to carriers: faults and recombination in non-stoichiometric perovskite scintillators
Tuning the efficiency and speed of charge carrier recombination in inorganic scintillators can potentially improve their performance in diverse applications. Recent work suggests that this maybe be achieved via a two-phase scintillator AB that naturally phase separates into A-rich and B-rich domains. In addition, a favorable electronic structure and band-edge alignment such that the charge carriers are confined or are thermodynamically driven to preferentially accumulate in one of the two domains, might lead to an improved radiative recombination rate. Here, we use density functional theory computations and ab initio molecular dynamics (AIMD), including non-adiabatic molecular dynamics (NAMD) simulations, to examine an alternative phase structure and its potential impact on recombination. Using a model perovskite SrTiO $$_3$$ system with one-, two- and three-dimensional Ruddlesden–Popper (RP) phases, we demonstrate that RP faults induce band structure changes in the material that can act as barriers to carrier transport. Our AIMD/NAMD simulations indicate competing effects of a lower mean free path (potentially enhancing the desired radiative recombination and overall scintillating efficiency) and faster non-radiative recombination (undesired) due to enhanced electron–phonon coupling in the faulted system. Full exploitation of such a rational design approach would require tuning of the effective scintillation efficiency by varying the perovskite chemistry using appropriate arrangements of RP faults in the bulk material. Finally, other effects, such as the tendency of point defects to segregate at the interface, that might affect the overall performance, are briefly discussed. We expect the basic results found here to apply to other nanostructured scintillators. Graphical Abstract- 16 July 2021
- Published by Springer Nature in Journal of Materials Science
- Vol. 56 (28) , 15812-15823
Open AccessBarriers to carriers: faults and recombination in non-stoichiometric perovskite scintillators
Tuning the efficiency and speed of charge carrier recombination in inorganic scintillators can potentially improve their performance in diverse applications. Recent work suggests that this maybe be achieved via a two-phase scintillator AB that naturally phase separates into A-rich and B-rich domains. In addition, a favorable electronic structure and band-edge alignment such that the charge carriers are confined or are thermodynamically driven to preferentially accumulate in one of the two domains, might lead to an improved radiative recombination rate. Here, we use density functional theory computations and ab initio molecular dynamics (AIMD), including non-adiabatic molecular dynamics (NAMD) simulations, to examine an alternative phase structure and its potential impact on recombination. Using a model perovskite SrTiO $$_3$$ system with one-, two- and three-dimensional Ruddlesden–Popper (RP) phases, we demonstrate that RP faults induce band structure changes in the material that can act as barriers to carrier transport. Our AIMD/NAMD simulations indicate competing effects of a lower mean free path (potentially enhancing the desired radiative recombination and overall scintillating efficiency) and faster non-radiative recombination (undesired) due to enhanced electron–phonon coupling in the faulted system. Full exploitation of such a rational design approach would require tuning of the effective scintillation efficiency by varying the perovskite chemistry using appropriate arrangements of RP faults in the bulk material. Finally, other effects, such as the tendency of point defects to segregate at the interface, that might affect the overall performance, are briefly discussed. We expect the basic results found here to apply to other nanostructured scintillators. Graphical Abstract - 15 January 2021
- Published by American Chemical Society (ACS) in Chemistry of Materials
- Vol. 33 (3) , 845-858
A Machine Learning Approach for the Prediction of Formability and Thermodynamic Stability of Single and Double Perovskite Oxides
- 15 January 2021
- Published by American Chemical Society (ACS) in Chemistry of Materials
- Vol. 33 (3) , 845-858
A Machine Learning Approach for the Prediction of Formability and Thermodynamic Stability of Single and Double Perovskite Oxides