arXiv subjects

Akhil S. Nair

Publications and source records attributed to Akhil S. Nair.

5 recordsLinked to original sources

arXiv2026

A Critical Examination of Active Learning Workflows in Materials Science

Active learning (AL) plays a critical role in materials science, enabling applications such as the construction of machine-learning interatomic potentials for atomistic simulations and the operation of self-driving laboratories. Despite its widespread use, the reliability and effectiveness of AL workflows depend on implicit design assumptions that are rarely examined systematically. Here, we critically assess AL workflows deployed in materials science and investigate how key design choices, such as surrogate models, sampling strategies, uncertainty quantification and evaluation metrics, relate to their performance. By identifying common pitfalls and discussing practical mitigation strategies, we provide guidance to practitioners for the efficient design, assessment, and interpretation of AL workflows in materials science.

cond-mat.mtrl-sci↗

arXiv2025

Materials Database from All-electron Hybrid Functional DFT Calculations

Materials databases built from calculations based on density functional approximations play an important role in the discovery of materials with improved properties. Most databases thus constructed rely on the generalized gradient approximation (GGA) for electron exchange and correlation. This limits the reliability of these databases, as well as the artificial intelligence (AI) models trained on them, for certain classes of materials and properties which are not well described by GGA. In this paper, we describe a database of 7,024 inorganic materials presenting diverse structures and compositions generated using hybrid functional calculations enabled by their efficient implementation in the all-electron code FHI-aims. The database is used to evaluate the thermodynamic and electrochemical stability of oxides relevant to catalysis and energy related applications. We illustrate how the database can be used to train AI models for material properties using the sure-independence screening and sparsifying operator (SISSO) approach.

cond-mat.mtrl-sci↗

arXiv2024

Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for Electrocatalysis

The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are unknown if the property is governed by a high intricacy of many atomistic processes. Here, we develop an AL workflow based on the sure-independence screening and sparsifying operator (SISSO) symbolic-regression approach. SISSO identifies the few, key parameters correlated with a given materials property via analytical expressions, out of many offered primary features. Crucially, we train ensembles of SISSO models in order to quantify mean predictions and their uncertainty, enabling the use of SISSO in AL. By combining bootstrap sampling to obtain training datasets with Monte-Carlo feature dropout, the high prediction errors observed by a single SISSO model are improved. Besides, the feature dropout procedure alleviates the overconfidence issues observed in the widely used bagging approach. We demonstrate the SISSO-guided AL workflow by identifying acid-stable oxides for water splitting using high-quality DFT-HSE06 calculations. From a pool of 1470 materials, 12 acid-stable materials are identified in only 30 AL iterations. The materials property maps provided by SISSO along with the uncertainty estimates reduce the risk of missing promising portions of the materials space that were overlooked in the initial, possibly biased dataset.

cond-mat.mtrl-sci↗

arXiv2020

Unravelling Single Atom Electrocatalytic Activity of Transition Metal Doped Phosphorene

Developing single atom catalysts (SACs) for chemical reactions of vital importance in renewable energy sector has emerged as a need of the hour. In this perspective, transition metal based SACs with monolayer phosphorous (phosphorene) as the supporting material are scrutinized for their electrocatalytic activity towards oxygen reduction reaction (ORR), oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) from first principle calculations. The detailed screening study has confirmed a breaking of scaling relationship between ORR/OER intermediates resulting in varied activity trends across the transition metal series. Group 9 and 10 transition metal based SACs are identified as potential catalyst candidates with platinum single atom offering bifunctional activity for OER and HER with diminished overpotentials. Ambient condition stability analysis of SACs confirmed a different extent of interaction towards oxygen and water compared to pristine phosphorene suggesting room for improving the stability of phosphorene via chemical functionalization.

cond-mat.mtrl-sci↗

arXiv2019

Importance of Dispersion and Relativistic Effects for ORR Overpotential Calculation on Pt(111) surface

Density functional theory (DFT) has been used as an important tool for studying activity of oxygen reduction reaction (ORR) catalysts. The dispersion effects, which are not encountered in many of the previous DFT studies for periodic Pt(111), are scrutinized for their role in predicting ORR activity on Pt (111) surface. Spin orbit coupling is employed to account for relativistic effects expected for heavy metal platinum, which has not been addressed in any of the previous studies on Pt(111). Adsorption behavior of intermediates and free energy changes of elementary reactions of ORR are analyzed with commonly used dispersion methods. A cumulative enhancement of ORR energetics and a maximum of 25% improvement in theoretical limiting potential are observed. The study illustrates the importance of consideration of these effects for better prediction of electrocatalytic activity for platinum based catalysts.

cond-mat.mtrl-sci↗