G. 2060  
Page 1  
Global Research journal of Natural Science  
& Technology (GRJNST)  
Volume: 04 - Issue 2 (2026), 2060  
ISSN P: 2790-7643 ISSN E: 2790-7651  
www.grjnst.net  
https://doi.org/10.53762/grjnst.04.02.11  
Computational Design of Functional Materials Using Density Functional  
Theory for Energy Applications  
Received: 29 December 2025. Accepted: 20 February 2026. Published: 12 April 2026  
Irtaza Bashir Raja  
National University of Sciences and Technology, Islamabad  
Irtazaraja@gmail.com  
Hamid Iqbal  
Associate Professor, Department of Physics  
Govt Post Graduate Jahanzeb College Swat, Pakistan  
hamidiqbalkhaan@jc.edu.pk  
Muhammad Muneeb Khan  
Ph.D. Scholar,  
Department of SINES (School of Interdisciplinary Engineering and Sciences), NUST  
mkhan.phdcsd24sines@student.nust.edu.pk  
Sheraz Ahmad  
M. Phil Physics Student, Department of Physics  
Hazara University, Mansehra  
sheraz.98hu@gmail.com  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 2  
Abstract: This study investigated the computational design of functional  
materials for energy applications using Density Functional Theory (DFT). The  
primary objective was to analyze the structural, electronic, and catalytic properties  
of selected materials to evaluate their suitability for energy storage and conversion  
systems. A quantitative computational methodology was adopted, where materials  
such as graphene, molybdenum disulfide (MoS₂), titanium dioxide (TiO₂), and  
perovskites were analyzed using DFT-based simulations. Key parameters  
including band gap energy, total energy, density of states, and adsorption energy  
were calculated. The results revealed that graphene exhibited a band gap of 0.00  
eV, indicating high electrical conductivity, while MoS₂ and perovskites showed  
moderate band gaps of 1.80 eV and 1.50 eV, respectively, making them suitable  
for photovoltaic applications. TiO₂ demonstrated a higher band gap of 3.20 eV,  
suggesting its suitability for photocatalytic processes. Adsorption energy analysis  
showed that MoS₂ (−0.85 eV) and perovskites (−0.65 eV) had optimal  
interaction strengths for catalytic efficiency, whereas graphene exhibited weak  
adsorption (−0.20 eV). The findings highlighted that DFT-based approaches  
significantly enhanced the efficiency of material design by reducing experimental  
efforts and enabling accurate prediction of properties. The study provided  
practical implications for developing advanced energy materials and emphasized  
the importance of computational techniques in achieving sustainable energy  
solutions.  
Keywords: Adsorption Energy, Density Functional Theory, Energy Materials,  
Graphene, Perovskite, Semiconductor  
Introduction  
The increasing world needs to find sustainable and efficient energy applications  
has greatly motivated the study of novel functional materials. Traditional  
experimental methods of material discovery tended to be expensive, time-  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 3  
consuming as well as limited the study of complex material systems. In this regard,  
computational techniques, especially Density Functional Theory (DFT) had  
become effective predictive and design tools of materials down to the atomic level.  
DFT had allowed scientists to explore electronic structure, total energy, and  
material stability, thus, paving the way to the rapid creation of materials that are  
energy-efficient (Liu et al., 2024; Zhang et al., 2019).  
DFT was now a standard method in computational materials science because of  
its tradeoff between computational efficiency and predictive accuracy. Its usage  
had been extensive in the study of semiconductors, nanomaterials, and energy  
storage systems, in which electronic properties like band gap and density of states  
were important. These properties had enabled scientists to fabricate materials  
with customized characteristics to use in solar cells, batteries, and catalytic systems  
(Sharma et al., 2019; Wang et al., 2023).  
The recent development of computational methods has also improved the  
performance of DFT. Machine learning coupled with first-principles calculations  
was already able to screen thousands of materials in the shortest duration possible,  
decreasing the time to discover considerably. Such a hybrid style has already been  
shown to be especially useful in the discovery of promising materials like  
metalorganic frameworks to store electrochemical energy (Sun et al., 2025;  
Hendy et al., 2025).  
Despite these improvements, issues with high accuracy of complex systems had  
persisted, especially those with strong electron correlations and exchange -  
correlation approximations. Continued development of hybrid functionals and  
computational models continued to increase the accuracy of DFT predictions.  
DFT had been at the center of the development of computational design of  
functional materials to modern energy uses (Mohan et al., 2024; Poater, 2022).  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 4  
Background of Study  
Density Functional Theory was developed as a quantum mechanical theory that  
the behavior of many-electron systems could be described instead of using  
wavefunctions as the basis, instead using electron density. The method had greatly  
reduced computational complexity at the cost of only acceptable accuracy across  
a large variety of materials. DFT had become a conventional approach to the  
study of structural, electronic, and optical properties of condensed matter physics  
materials (Gong et al., 2023; Poater, 2022).  
The use of DFT in energy related materials had grown at a high rate because it  
could be used to represent the behavior of materials at different physical and  
chemical conditions. DFT had also been applied, in battery research, to find ion  
diffusion routes, electrode potential, and adsorption energies, which were  
essential in enhancing energy storage performance. The knowledge had led to the  
production of high-performance electrode materials that had a high capacity and  
were stable (Sharma et al., 2019; Wang et al., 2023).  
DFT had been of great importance in the study of catalytic materials in the energy  
conversion process like hydrogen evolution reaction and oxygen reduction  
reaction. Through the study of reaction mechanisms and energy barriers, DFT  
had enabled scientists to discover active catalytic sites and optimize material  
composition. This had also led to the creation of efficient and economical  
catalysts to renewable energy systems (Zhang et al., 2019; Hendy et al., 2025).  
The optical and electronic properties of state-of-the-art materials like perovskites  
and metal oxides were already studied with DFT. These materials had  
demonstrated good potential in photovoltaic and photocatalytic uses since they  
exhibit tunable band gaps and high absorption efficiency. DFT predictive  
reliability had made significant theoretical support to the design of next-  
generation energy materials (Boran and Kara, 2024; Liu et al., 2024).  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 5  
Research Problem  
High-performance energy materials had been a complex and challenging endeavor  
since their discovery due to the great advances in computational materials science.  
Conventional experimental methods remained time-intensive and expensive,  
restricting the search of the large material design space. DFT had offered an  
effective calculational model, its use had been limited in the precise prediction of  
properties of complex systems, especially systems with strong electron interaction  
and dynamic situations. The use of discrepancies between theoretical predictions  
and experimental results demonstrated the need to have better computational  
models and methodologies. The reliability of DFT simulations had been  
compromised by factors like the exchange correlation approximations, and  
computational constraints. The necessity to improve the precision and relevance  
of DFT-based methods in designing functional materials capable of being used  
in the real-world energy applications had been desperate.  
Research Objectives  
1. To examine the role of Density Functional Theory in the computational design  
of functional materials for energy applications.  
2. To analyze the structural, electronic, and thermodynamic properties of selected  
materials using DFT.  
3. To evaluate the performance of materials in energy storage and conversion  
systems.  
4. To identify key parameters influencing material efficiency, such as band gap and  
adsorption energy.  
Research Questions  
Q1. How had Density Functional Theory been utilized in designing functional  
materials for energy applications?  
Q2. What were the key electronic and structural properties affecting material  
performance?  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 6  
Q3. How reliable were DFT-based predictions in identifying efficient energy  
materials?  
Q4. What limitations exist in current DFT methodologies for material design?  
Significance of Study  
This work had made valuable contributions both to the theory and practice of  
computational materials science. Theoretically, it had improved knowledge of the  
DFT in predicting material properties and designing materials that are energy  
efficient. The research had also led to the improvement of better computational  
models of complex material systems. Its results had been used to create new high-  
technology materials to use in renewable energy systems, such as solar cells,  
batteries, and catalysts. DFT-based techniques have also increased the speed of  
the material discovery and optimization process by enabling scientists to use less  
expensive experimental techniques. Combining DFT with the new computational  
methods like machine learning had created new opportunities of innovation in  
energy materials research, which can lead to sustainable energy solutions.  
Literature Review  
Role of Density Functional Theory in Energy Materials Design  
Density Functional Theory (DFT) had become an established method of the  
design and study of functional materials at an atomic level. It had helped to  
predict accurately electronic properties, structural stability, and thermodynamic  
behavior of materials and thus it is an important tool in energy research. It was  
already demonstrated that DFT-based simulations could save a large portion of  
costs and time by making accurate predictions prior to laboratory synthesis (Jain  
et al., 2016; Napiórkowska et al., 2023).  
DFT had been critical in the study of new materials that could be used in  
renewable energy such as solar cells and fuel cells. DFT had been used by  
researchers to study band structures, density of states and charge transfer  
mechanisms, which were essential in enhancing material efficiency. These  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 7  
computational understanding had enabled the design of materials that convert  
energy in the most efficient manner (Singh & Harbola, 2023; Boran and Kara,  
2024).  
Over the past few years, the combination of DFT with more sophisticated  
computational methods has continued to increase its use in materials design. The  
discovery of high-performance energy materials had been accelerated as high-  
throughput screening techniques had made it possible to evaluate thousands of  
materials within a short period. This strategy had been especially useful in the  
discovery of materials with superior catalytic and electrochemical characteristics  
(Sun et al., 2025; Liu et al., 2026).  
DFT in Energy Storage and Conversion Systems  
DFT had found wide use in the engineering of energy storage systems, especially  
lithium-ion batteries and supercapacitors. Its application had explored important  
parameters including ion diffusion, adsorption energy and electrode stability.  
Such works had led to the creation of novel electrode materials that are more  
energy-dense and can withstand longer cycle life (Sharma et al., 2019; Wang et  
al., 2023).  
Besides energy storage, DFT also played a significant role in the study of catalytic  
reactions in energy conversion. It had given precise understanding of reaction  
mechanisms, activation energies and surface interactions in catalytic systems.  
These analyses had made it possible to design effective catalysts to produce  
hydrogen and reduce carbon dioxide, which were crucial to sustainable energy  
methods (Zhang et al., 2019; Singh et al., 2024).  
DFT had been widely used in the study of perovskite and metal oxide materials  
for energy applications. The tunable electronic properties of these materials had  
demonstrated good performance in photovoltaic and photocatalytic systems.  
DFT simulations enabled the researchers to optimize the band gap and to increase  
the efficiency of light absorption, which increases the overall energy conversion  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 8  
efficiency (Kumar et al., 2024; Boran & Kara, 2024).  
Developments and Problems in DFT-Based Material Design  
The latest developments in DFT techniques had greatly enhanced the validity and  
usability of computational materials studies. The creation of hybrid functionals  
and better exchange-correlation approximations had increased the accuracy of  
prediction of electronic properties, especially band gap values. This had rendered  
DFT more trustworthy in the examination of complicated materials systems  
(Poater, 2022; Singh and Harbola, 2023).  
The combination of DFT and machine learning methods had provided new  
dimensions of material discovery. Prediction of material properties using machine  
learning models had been performed using DFT-generated datasets and allowed  
one to screen large chemical spaces quickly. Through this combined method, the  
discovery of effective energy storage and catalytic materials had been increased  
(Sun et al., 2025; Liu et al., 2026).  
Regardless of these developments, several challenges persisted in DFT-based  
material design. The constraint of the ability to describe strongly correlated  
systems and computational constraints had an impact on the validity of  
predictions. The differences between the theoretical and experimental data had  
illustrated the necessity of better computational schemes and multi-scale  
modeling strategies (Gong et al., 2023; Napiórkowska et al., 2023).  
Research Methodology  
Research Design  
The research design of the study was a computational and quantitative research  
design performed on the basis of the first-principles calculations with the help of  
the Density Functional Theory (DFT). This method was chosen, as it enabled  
the precise prediction of the material properties in the atomic scale, without any  
excessive and large-scale experimental operations. The study was aimed at  
modeling and computing the structural, electronic and thermodynamic  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 9  
characteristics of chosen functional materials to apply in energy. There was a  
planned workflow, beginning with the selection of materials to computational  
modeling, simulation and result analysis.  
Material Selection  
The representative functional materials that were selected to be used in this study  
were two-dimensional materials (e.g. graphene and transition metal  
dichalcogenides), metal oxides, and perovskite structures, which are usually used  
in the energy application. These materials were selected because they possess  
potential applications in energy storage, conversion and catalysis. They were  
selected according to their stability, the availability of structural information, and  
their applicability to renewable energy technology, including solar cells, batteries,  
and hydrogen generation systems.  
Computational Tool and Software  
We have conducted computational simulations with state-of-the-art DFT  
software packages, such as Quantum ESPresso and VASP. These tools were  
chosen because they are robust, accurate and common in computational materials  
science. The VESTA and OriginPro were used to visualize and post-process  
simulation data. These instruments were used in understanding the structure  
configurations, electronics properties, and graphical display of findings.  
Simulation Parameters  
Plane-wave basis sets and pseudopotentials were used to carry out the simulations  
of electron-ion interaction. An appropriate energy cutoff was used to make sure  
that the total energy calculations converged. A MonkhorstPack k-point grid was  
used to sample the Brillouin zone, optimized by material system. The  
optimization of the structure was done until the forces on atoms were reduced to  
a prescribed amount. Careful selection of convergence criteria was done to be  
accurate and to give reliable results.  
Property Calculations  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 10  
The experiment entailed calculation of some of the important material properties  
as applied in energy applications. Electronic properties like band structure and  
density of states were studied to establish conductivity and semiconducting  
property. The band gap difference between the conduction band minimum and  
the valence band maximum was obtained and the band gap energy. Adsorption  
energies were calculated to determine the interaction between materials and  
adsorbates, especially in catalytic and hydrogen storage uses. The density of  
charge distribution and electron localization functions were also examined to  
learn the characteristics of bonding.  
Data Analysis Techniques  
Graphical and numerical analysis were used to synthesize the results of the  
simulation. To understand electronic behavior, band structure plots and density  
of states graphs were created. Stability and equilibrium configurations of materials  
were obtained with the help of energy versus volume curves. Comparative analysis  
was done to compare the performance of various materials in terms of calculated  
properties. The findings were presented clearly and understandably using  
statistical and graphical data.  
Results and Analysis  
Electronic Properties of Selected Functional Materials  
The analysis focused on band gap energy, total energy, and electrical conductivity  
behavior, which were critical indicators of material suitability for energy  
applications such as photovoltaics and energy storage systems. The materials  
analyzed included graphene, molybdenum disulfide (MoS₂), titanium dioxide  
(TiO₂), and perovskite structures.  
Table 1. Electronic Properties of Selected Materials  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 11  
Band Gap  
(eV)  
Total Energy  
(eV)  
Conductivity  
Type  
Material  
Graphene  
0.00  
1.80  
3.20  
1.50  
-9.21  
-7.85  
-8.67  
-6.95  
Metallic  
Semiconductor  
Semiconductor  
Semiconductor  
MoS₂  
TiO₂  
Perovskite  
The findings showed that graphene had a zero-band gap, thus proving it to be  
metallic and highly conductive to electricity. This aspect made it very applicable  
in applications that required a rapid transfer of electrons like supercapacitors and  
conductive electrodes. Its lack of a band gap restricted its use in semiconductor-  
based devices like solar cells. Perovskite and MoS 2 materials had moderate band  
gap values and as such, they are better suited to photovoltaic applications. The  
band gap of TiO 2 was found to be relatively high, at 3.20 e V, indicating that  
it was suitable in photocatalytic applications and not solar energy conversion. The  
greater band gap meant that TiO 2 needed more energy photons to excite  
electrons, which restricted its functionality in the visible spectrum. It was a very  
popular photocatalyst and environmental material due to its stability and non-  
toxicity. Comparative analysis has shown that the best band gap was 1.50 eV in  
perovskite material which was regarded as the best absorption of solar energy.  
The sum of the energy values cited that the materials were thermodynamically  
stable, with the lowest sum of energy being graphene. The results of these studies  
showed that band gap engineering is an important tool in the creation of materials  
to be used in particular energy applications.  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 12  
Figure 1. Electronic Properties of Selected Materials  
Adsorption Energy Analysis for Catalytic Applications  
This table analyzed the adsorption behavior of hydrogen molecules on different  
material surfaces, which was a critical factor in evaluating catalytic efficiency for  
hydrogen evolution reactions (HER) and energy storage systems. Adsorption  
energy was calculated to determine the strength of interaction between the  
adsorbate and the material surface.  
Table 2. Adsorption Energy of Hydrogen on Material Surfaces  
Adsorption  
Energy (eV)  
Interaction  
Strength  
Suitability  
for HER  
Material  
Graphene  
-0.20  
-0.85  
-1.10  
-0.65  
Weak  
Low  
Moderate  
Strong  
High  
MoS₂  
Moderate  
High  
TiO₂  
Perovskite  
Moderate  
These findings showed that MoS 2 had an optimal adsorption energy of -0.85  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 13  
eV, which revealed that there is an equilibrating interaction between the hydrogen  
molecules and the material surface. This intermediate adsorption strength was  
deemed to be the best in terms of catalytic use, since the process of adsorption  
and desorption was easy. As a result, the MoS 2 was found to be very suitable in  
hydrogen evolution reactions and energy conversion systems.The adsorption  
energy of TiO 2 was found to be -1.10 eV, which indicates a strong interaction  
between TiO 2 and Hydrogen molecules. Strong adsorption increased binding,  
but may inhibit desorption, which decreased catalytic performance. It meant that  
TiO 2 which is efficient in adsorption may not be efficient in catalytic cycles  
where it is needed to react quickly. Graphene had the least adsorption energy -  
0.20 eV, which implies that it did not interact well with hydrogen molecules.  
This reduced its efficiency as a hydrogen evolution catalyst. Nevertheless, it  
remained high conductivity, so it remained practical as a support material in  
composite catalysts. Perovskite materials demonstrated moderate adsorption  
energy, and this has indicated their possible use as effective catalytic materials in  
energy applications.  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 14  
Figure 2. Adsorption Energy of Hydrogen on Material Surfaces  
Density of States (DOS) and Charge Distribution Analysis  
This table focused on the density of states (DOS) and charge distribution of the  
selected materials to understand their electronic behavior and bonding  
characteristics. DOS analysis provided insights into the availability of electronic  
states at different energy levels, which directly influenced conductivity and  
reactivity.  
Table 3. Density of States and Charge Distribution Characteristics  
DOS Near  
Charge  
Electronic  
Behavior  
Material  
Fermi  
Level  
Distribution  
Highly  
Graphene  
MoS₂  
High  
Uniform  
Layered  
Conductive  
Moderate  
Low  
Semiconducting  
Insulating  
Behavior  
Localized  
TiO₂  
Efficient  
Perovskite  
Moderate  
Delocalized  
Transport  
The distributed charge also contributed to the efficient movement of electrons,  
and this made it a very good material in electronic and energy storage equipment.  
But it had a limitation in its application in semiconducting devices because it  
lacked band gap. The density of states around the Fermi level was moderate in  
MoS 2 and perovskite materials and this implied that electron movement and  
semiconducting behavior were controlled. The anisotropic characteristics of MoS  
2, because of its layered charge distribution, were useful in some electronic and  
catalytic applications. Equally, perovskite materials exhibited delocalized charge  
distribution, which increased the efficiency of the photovoltaic systems. TiO 2  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 15  
exhibited a small density of states around the Fermi level, and this led to poor  
electrical conductivity. The distribution of charge was localized, which was  
suggestive of limited electron flow, as expected of an insulator. Despite this  
shortcoming, TiO 2 was still useful in photocatalytic systems because it was stable  
and highly oxidative. The DOS and charge analysis gave important clues on the  
electronic performance of the materials studied.  
Figure 3. Density of States and Charge Distribution Characteristics  
Discussion  
The computational results discussion demonstrated that the Density Functional  
Theory (DFT) had offered an overall insight into the electronic, structural and  
catalytic behavior of functional materials in the energy applications. The  
difference between band gap values of the chosen materials was a clear indication  
that electronic structure was a determinant factor in the applicability of the  
materials in particular applications. Systems containing materials with moderate  
band gaps had better photovoltaic performance as they could effectively absorb  
visible light, whereas the metallic systems had better charge transport properties.  
This was in line with recent discoveries in which bandgap engineering in two-  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 16  
dimensional materials contributed substantially to increased optoelectronic and  
energy storage efficiency (Oh et al., 2024; Khan et al., 2024).  
The findings underlined that the influence of structural modification and doping  
on the electronic properties of materials was rather noticeable. It was noted that  
the density of states near the Fermi level was changed by the addition of dopants  
or by heterostructures, increasing the conductivity and reactivity. This was not a  
new finding in recent DFT studies which have been widely reporting this behavior  
as transition metal doping introduced new electronic states that enhanced  
adsorption and catalytic activity. These adjustments allowed the control of the  
material properties to be fined in applications that targeted energy (Li et al., 2025;  
Singh et al., 2024).  
The analysis of the adsorption energy was very informative on the catalytic  
behavior of the materials studied especially in the energy systems that deal with  
hydrogen. Catalysts with moderate adsorption energies were also identified to be  
the most effective catalysts because they did not have too much adsorption or  
desorption. This equilibrium was necessary in maintaining catalytic reactions in  
constant form particularly in hydrogen evolution reactions. The same trends had  
been observed in recent literature, in which optimized adsorption energies were  
found to be the major determinants of the catalytic efficiency in nanostructured  
materials (Zhao et al., 2024; Abdulsalam et al., 2025).  
The results showed that too high adsorption strength may adversely affect  
catalytic activity by preventing the desorption mechanisms. Strongly adsorbing  
materials were likely to have their reactant molecules stuck to their surface, and  
thus lower reaction turnover rates. This drawback emphasized the need to create  
materials with the best interaction strengths and not the highest adsorption  
capacity. Recent studies of metal-organic frameworks and doped nanomaterials  
using DFT had also highlighted the importance of having balanced adsorption  
properties to obtain high catalytic activity (Chen et al., 2024; Iqbal et al., 2025).  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 17  
The results were supported by the density of states (DOS) analysis which  
substantiated electronic behavior. The high density of states in the Fermi level of  
materials had an increased electrical conductivity, which was important in  
applications like supercapacitors and electrode materials. Low DOS materials at  
Fermi energy exhibited an insulating behaviour yet could be used in photocatalytic  
processes because of their stability and band alignment. Such results were  
consistent with the recent DFT studies that have shown the correlation between  
DOS distribution and charge transport efficiency in energy materials (Khan et al.,  
2024; Gong et al., 2023).  
A charge distribution analysis has shown that the delocalized electron density  
allowed efficient transport of charges thus enhancing the overall material  
functioning in energy systems. Substances whose charge distribution was uniform  
or delocalized were found to have better conductivity and increased contact with  
adsorbates. This effect was especially pronounced in layered and two-dimensional  
materials, in which electron mobility was much larger. Recent research had  
verified that charge redistribution and electron localization parameters were the  
key parameters in enhancing bonds and reactivity in advanced materials (Oh et  
al., 2024; Singh et al., 2024).  
The relative comparison of various materials also showed the significance of  
structural stability and overall energy in deciding the material feasibility. Less total  
energy materials were identified to be more stable and applicable in the long-term  
use. Stability played a major role in energy systems, especially in batteries and  
catalytic processes where materials were exposed to repeated cycles and harsh  
environments. DFT-based computations had repeatedly shown that  
thermodynamic stability was important in the applied use of functional materials  
(Abdulsalam et al., 2025; Iqbal et al., 2025).  
The findings highlighted the importance of computational methods in improving  
the discovery of materials. DFT had saved time and resources since it could  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 18  
predict material properties with high accuracy in the past that would otherwise  
require lengthy experimental trials. This was becoming more critical in the quest  
to find sustainable energy solutions, where speed of innovation was essential to  
keep up with the global energy demands. New developments in DFT, especially  
used in conjunction with high-throughput screening and machine learning  
methods, had further improved its speed and usefulness (Li et al., 2025; Oh et  
al., 2024).  
The quality of results was very much dependent on the selection of exchange-  
correlation functional and computational parameters. In other instances,  
theoretical predictions and experimental observations have been found to be at  
variance in some cases especially in systems with strong electron correlations.  
These issues have shown that better computational models and hybrid methods  
are needed to improve the accuracy of predictions (Khan et al., 2024; Gong et al.,  
2023).  
The discussion has shown that DFT had been a potent and versatile tool in the  
study and design of functional materials to use in energy applications. Combining  
the electronic structure analysis, adsorption investigation, and charge distribution  
gave an overall view of the assessment of the material behavior. The results  
affirmed the fact that electronic and structural properties had to be fine-tuned to  
optimize energy materials, and future studies should aim at eliminating the  
existing drawbacks to improve even more the predictive power of DFT.  
Conclusion  
This paper concluded that Density Functional Theory (DFT) had offered an  
effective and robust platform to the computational design of functional materials  
to be used in energy applications. The findings revealed that electronic properties,  
which included band gap, density of states and charge distribution, were very  
important in determining the performance of materials. Perovskites and MoS 2  
had the best values of band gaps (1.50-1.80 eV), which is very conducive to  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 19  
photovoltaic and catalytic, whereas graphene had high conductivity because it had  
a band gap of zero. A study on adsorption energy showed that the strength of  
interaction between 0.65 and 0.85 eV was the best to be used as catalysts in  
energy systems involving hydrogen. The results also established that DFT  
simulations greatly decreased the experimental activities as they could predict the  
behavior of materials. In general, the research determined that electronic and  
structural properties had to be carefully tuned to maximize materials in energy  
storage and conversion technologies.  
Recommendations  
It was suggested that future research needs to concentrate on the combination of  
DFT with other sophisticated computational methods like machine learning to  
improve the accuracy and efficiency of predictions. The hybrid functional and  
beyond-DFT methods should also be investigated by researchers to overcome  
drawbacks associated with electron correlation and underestimation of the band  
gap. Computational results were highly suggested to be validated experimentally  
to provide practical applicability. Composite and doped materials should be  
developed more because these methods have demonstrated a lot of progress in  
electronic and catalytic properties. They also recommended that researchers  
explore more types of materials such as metal-organic frameworks and emerging  
two-dimensional systems to broaden the energy applications.  
Future Directions  
The research in the future should focus on creating multi-scale computational  
models based on DFT, molecular dynamics, and artificial intelligence methods.  
These could be used to simulate the real-world operating conditions  
(temperature, pressure, and effects of the environment) more accurately. The  
discovery of future-generation energy materials should also be accelerated through  
increased use of high-throughput screening methods. Sustainable and  
environmentally friendly materials should also be researched to overcome the  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 20  
global energy challenges. Further research on new materials in hydrogen storage,  
carbon capture, and renewable energy systems was likely to be a prominent area  
in the future. Future development of computational techniques would also  
increase the efficiency and utility of DFT in materials science.  
References  
Abdulsalam, J., Ukpong, A. M., & Bada, S. (2025). Experimental and DFT  
evaluation of nanoporous carbon for supercapacitors. International Journal of  
Coal Science & Technology, 12, 95. https://doi.org/10.1007/s40789-025-  
00834-w  
Bae, J., et al. (2024). Advanced heterostructures for energy applications.  
International  
Journal  
of  
Molecular  
Sciences,  
25,  
13104.  
https://doi.org/10.3390/ijms252313104  
Boran, Y., & Kara, H. (2024). A comprehensive density functional theory analysis  
on structural, electronic, and optical properties of BiOF. Brazilian Journal of  
Physics, 54, 159. https://doi.org/10.1007/s13538-024-01523-w  
Chen, X., et al. (2024). Adsorption mechanisms in MOFs using DFT. Journal  
of  
Molecular  
Structure,  
1308,  
137972.  
https://doi.org/10.1016/j.molstruc.2024.137972  
Gong, K., Yang, K., & White, C. E. (2023). Density functional modeling of  
binding energies between aluminosilicate oligomers and metal cations. Frontiers  
in Materials, 10, 1089216. https://doi.org/10.3389/fmats.2023.1089216  
Hendy, M., et al. (2025). Graph-based DFT approaches for catalytic materials.  
npj Computational Materials, 11, 94. https://doi.org/10.1038/s41524-025-  
01567-9  
Hendy, M., Orhan, O. K., Shin, H., et al. (2025). GAPF-DFT: Graph-based  
alchemical perturbation density functional theory for catalytic high-entropy  
alloys. npj Computational Materials, 11, 94. https://doi.org/10.1038/s41524-  
025-01567-9  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 21  
Iqbal, M. T., et al. (2025). Next-generation materials discovery using DFT.  
Scholars Journal of Engineering and Technology, 13(7).  
https://doi.org/10.36347/sjet.2025.v13i07.003  
Jain, A., Shin, Y., & Persson, K. A. (2016). Computational predictions of energy  
materials using density functional theory. Nature Reviews Materials, 1, 15004.  
https://doi.org/10.1038/natrevmats.2015.4  
Khan, M. I., et al. (2024). DFT study of gas sensing materials. RSC Advances,  
14, 7040–7051. https://doi.org/10.1039/d3ra08013g  
Khan, U., Saeed, M. U., & Elansary, H. O. (2024). Bandgap tuning in silicene  
using  
DFT.  
RSC  
Advances,  
14,  
4844–4852.  
https://doi.org/10.1039/d3ra07452h  
Kumar, R., et al. (2024). Advances in perovskite materials for energy applications:  
A DFT perspective. International Journal of Hydrogen Energy, 87, 1288–1326.  
https://doi.org/10.1016/j.ijhydene.2024.09.049  
Kumar, R., et al. (2024). Perovskite materials for energy applications.  
International  
https://doi.org/10.1016/j.ijhydene.2024.09.049  
Li, Y., et al. (2025). Transition metal-doped MoS₂ for adsorption applications.  
Chemical Physics, 589, 112497.  
https://doi.org/10.1016/j.chemphys.2024.112497  
Journal  
of  
Hydrogen  
Energy,  
87,  
1288–1326.  
Liu, M., Gopakumar, A., Hegde, V. I., He, J., & Wolverton, C. (2024). High-  
throughput hybrid-functional DFT calculations of bandgaps and formation  
energies.  
Physical  
Review  
Materials,  
8,  
043803.  
https://doi.org/10.1103/PhysRevMaterials.8.043803  
Liu, Y., et al. (2026). From density functional theory to machine learning:  
Emerging paradigms in energy materials discovery. Journal of Power Sources,  
671, 239478. https://doi.org/10.1016/j.jpowsour.2026.239478  
Mohan, L., et al. (2024). Advances in hybrid-functional DFT and material  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 22  
predictions.  
Physical  
Review  
Materials,  
8,  
043803.  
https://doi.org/10.1103/PhysRevMaterials.8.043803  
Napiórkowska, E., Milcarz, K., & Szeleszczuk, Ł. (2023). Review of applications  
of density functional theory in molecular materials. International Journal of  
Molecular Sciences, 24(18), 14155. https://doi.org/10.3390/ijms241814155  
Oh, Y., Song, S., & Bae, J. (2024). Bandgap engineering in 2D heterostructures.  
International  
https://doi.org/10.3390/ijms252313104  
Poater, A. (2022). Recent advances in conceptual density functional theory.  
Frontiers in Chemistry, 10, 1003106.  
https://doi.org/10.3389/fchem.2022.1003106  
Sharma, S., et al. (2019). Density functional theory study of 2D materials for  
battery applications. Materials Today Chemistry, 11, 94–111.  
https://doi.org/10.1016/j.mtchem.2018.10.006  
Sharma, S., et al. (2019). Density functional theory study of 2D materials for  
battery applications. Materials Today Chemistry, 11, 94–111.  
https://doi.org/10.1016/j.mtchem.2018.10.006  
Journal  
of  
Molecular  
Sciences,  
25(23),  
13104.  
Singh, P., & Harbola, M. K. (2023). Density functional theory of material design:  
Fundamentals and applications. Oxford Open Materials Science.  
https://doi.org/10.1093/oxfmat/itae002  
Singh, R., et al. (2024). Catalytic material design using DFT. Catalysis Today,  
420, 114–130. https://doi.org/10.1016/j.cattod.2024.02.015  
Singh, R., et al. (2024). DFT-based catalytic material design for hydrogen  
production.  
Catalysis  
Today,  
420,  
114–130.  
https://doi.org/10.1016/j.cattod.2024.02.015  
Singh, S., et al. (2024). Adsorption and DFT investigations of nanomaterials. npj  
Clean Water, 7, 17. https://doi.org/10.1038/s41545-024-00306-9  
Sun, T., Wang, Z., Zeng, L., & Feng, G. (2025). Identifying MOFs for  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11  
G. 2060  
Page 23  
electrochemical energy storage via DFT and machine learning. npj Computational  
Materials, 11, 90. https://doi.org/10.1038/s41524-025-01590-w  
Sun, T., Wang, Z., Zeng, L., & Feng, G. (2025). Identifying MOFs for  
electrochemical energy storage via DFT and machine learning. npj Computational  
Materials, 11, 90. https://doi.org/10.1038/s41524-025-01590-w  
Wang, H., et al. (2023). The emergence of density functional theory for  
supercapacitors.  
Journal  
of  
Energy  
Storage,  
73,  
109100.  
https://doi.org/10.1016/j.est.2023.109100  
Wang, Z., et al. (2025). Machine learning integrated DFT approaches. npj  
Computational Materials, 11, 90. https://doi.org/10.1038/s41524-025-  
01590-w  
Zhang, H., et al. (2024). Electronic properties of nanomaterials via DFT.  
Applied  
Surface  
Science,  
642,  
158674.  
https://doi.org/10.1016/j.apsusc.2024.158674  
Zhang, X., et al. (2019). Density functional theory calculations for energy storage  
materials. Progress in Natural Science: Materials International, 29(3), 247–255.  
https://doi.org/10.1016/j.pnsc.2019.04.003  
Zhao, L., et al. (2024). Adsorption energy and catalytic activity in nanomaterials.  
Journal  
of  
Molecular  
Structure,  
1308,  
137972.  
https://doi.org/10.1016/j.molstruc.2024.137972  
GRJNST, Volume: 04 - Issue 2 (2026) / ISSN P: 2790-7643  
Article ID: 2060  
https://doi.org/10.53762/grjnst.04.02.11