Bhusal, A. computational physicist

Bhusal, A. (2016 – present). Pokhara, Nepal.

I use molecular dynamics and DFT
to understand how materials behave at the atomic scale.

Eleven papers since 2016 trace that work through CO2 capture in metal–organic frameworks, nanopore fluid behavior in shale, and the machine-learning models that screen candidate materials.

Keywords molecular dynamics · CO2 sequestration · metal–organic frameworks · HPC · machine learning · scientific Python

View publications Selected work Get in touch
  • 11journal articles
  • 2016–nowresearch practice
  • CO2capture & simulation

Fig. 1 — A live Lennard-Jones fluid: velocity-Verlet integration, ~60 steps/s. Move your cursor through the system to perturb it. The observatory beside it re-reads the same particles as structure and thermal history. Open the full playground →

Introduction

My career began in physics and grew into a cross-disciplinary practice: designing computational experiments, interpreting simulation data, building the tools in between — and helping students turn research questions into defensible results.

At Physics Research Initiatives I contribute to research on CO2 huff-n-puff and enhanced oil recovery, fluid behavior, carbon capture, and molecular-scale materials. Alongside the research I supervise dissertations, edit the Himalayan Physics Journal, and run Python training programs for scientific audiences.

  • BasePokhara, Nepal
  • AffiliationPhysics Research Initiatives
  • EditorialEditor-in-Chief · Himalayan Physics Journal
  • TeachingScientific Python programs
Portrait of Aabiskar Bhusal in the Himalayas
Fig. 2 — The author. Pokhara, Nepal

Expertise

A research stack built for computation-heavy science — from atomistic simulation to the web pages that publish it.

LAMMPS workflows end to end: system construction, selection, , production runs, and post-processing of classical molecular systems.

  • LAMMPS
  • Material Studio
  • OVITO

Machine Learning for Materials

Regression, classification, and clustering for materials questions — data-driven screening of porous materials and gas-adsorption behavior.

  • scikit-learn
  • Pandas
  • NumPy

High-Performance Computing

SLURM job scripting, multi-node resource management, and large-scale simulation and analysis pipelines on shared clusters — built for reliable, reproducible throughput.

  • SLURM
  • bash
  • MPI

Electronic Structure

Gaussian-based quantum chemistry: molecular orbitals, charge density, electrostatic potential, and mechanochemical response.

  • Gaussian
  • Quantum ESPRESSO

Scientific Python

Research automation, analysis, and publication-grade visualization — and the training programs that teach others to do the same.

  • NumPy
  • SciPy
  • Matplotlib
  • Seaborn

Software & Web

Data-facing applications and research tools with Python, Django, and JavaScript — supported by LaTeX authoring, Fortran workflows, and maintainable deployment practices.

  • Python
  • Django
  • JS
  • LaTeX
  • Fortran

Chip-Level Laptop Repair

Certified technician

Board-level diagnostics and precision microsoldering for power rails, charging circuits, embedded controllers, and component-level fault isolation.

  • Multimeters
  • Oscilloscopes
  • Soldering stations

Read the lab notes → — short technical write-ups from this stack, including LAMMPS/MOF setup and SLURM job patterns.

From methods to applied research follow the chronology →

Chronology

Appointments, editorial service, teaching, and education.

  1. 2023 — now

    Editor-in-ChiefHimalayan Physics Journal

    Overseeing peer review, coordinating authors and reviewers, and preparing manuscripts through LaTeX-based publication workflows.

  2. 2016 — now

    Research AssociatePhysics Research Initiatives

    Computational modeling, CO2 huff-n-puff collaboration, molecular simulation, dissertation supervision, and Python training programs.

  3. 2022 — 2023

    Assistant ProfessorPokhara University

    Taught “Programming for Bioinformatics” to graduate students of Bioinformatics.

  4. 2019 — 2023

    Co-EditorHimalayan Physics Journal

    Editorial operations, manuscript handling, peer-review quality, and journal production.

  5. 2018 — 2019

    Data AnalystPokhara Investment Company

    NEPSE market analysis with fundamental and technical indicators, Python tooling, and the company's web presence.

  6. 2016

    M.Sc. PhysicsTribhuvan University

    Prithvi Narayan Campus. Graduated with a 3.59 / 4.0 CGPA.

  7. 2013

    B.Sc. PhysicsTribhuvan University

    Prithvi Narayan Campus. First Division.

From experience to applied outcomes see selected work →

Professional record

Formal learning, training faculty work, and service to the physics community.

Verified learning

Professional certificates

Applied foundations in Python, data science, machine learning, artificial intelligence, and computational materials physics.

Training faculty

Programs facilitated

Expert-led Python and data-analysis instruction delivered for university and research audiences.

  1. Modern Approaches to Data Analysis

    Facilitated a two-day expert workshop for the Faculty of Science and Technology, Pokhara University.

  2. Training on Python Programming

    Facilitated as an expert for the Department of Electronics and Computer Engineering, Paschimanchal Campus, Tribhuvan University.

  3. Python for Master-Level Faculties

    Facilitated as an expert for the Institute of Engineering, Paschimanchal Campus, Tribhuvan University.

  4. Three-Day Immersion Course on Python

    Facilitated as an expert for Physics Research Initiatives.

Community service

Professional memberships

Contributing to the local physics community through society service, editorial engagement, and research leadership.

  • Editorial memberNepal Physical Society · Gandaki Chapter
  • Executive memberPhysics Research Initiatives
  • MemberNepal Physical Society

From professional practice to applied outcomes see selected work →

Selected Work

Projects where the research, the data, and the software had to work together.

research record · molecular simulation

CO2 huff-n-puff in shale nanopores

Collaborative modeling of CO2 behavior in inorganic nanopores — supporting enhanced oil recovery insight and minimum-miscibility-pressure estimation, with China-based research partners.

Outcome: molecular insight into minimum miscibility pressure in shale oil/CO2 systems.

Read publication
Study details
Question
How do confinement and CO2 injection conditions shape shale-oil behaviour?
Method
Molecular simulation of inorganic nanopores and fluid interactions.
Outcome
Interfacial-tension and minimum-miscibility-pressure insight for confined fluids.
  • LAMMPS
  • nanopore fluids
  • EOR
research programme · computational materials

MOF adsorption research programme

Related GCMC/MD studies across MOF-177 and MOF-74 examine adsorption, molecular mobility, and framework–adsorbate interactions. The definitive humidity-dependent MOF-177 record appears once above as the flagship publication.

Programme: comparative molecular-simulation work on CO2 capture in porous frameworks.

Read publication
Study details
Question
How does humidity alter CO2 adsorption?
Method
Hybrid GCMC/MD simulations of MOF-177 and MOF-74.
Outcome
Humidity-dependent changes in uptake, structure, and molecular mobility.
  • GCMC
  • MD
  • gas adsorption
scientific tool · data product

CO2 uptake predictor

Browser-local XGBoost model estimating MOF CO2 adsorption from eight structural and thermodynamic descriptors, with exact TreeSHAP explanations for each prediction.

Boundary: trained on the study archive through 16 bar; treat predictions beyond that pressure range as extrapolation, not measurement.

  • 773 trees
  • R² 0.979 reported test metric
  • local-only execution
Inspect predictor and methodology
  • XGBoost
  • TreeSHAP
  • MOF adsorption
scientific tool · data product

Market analysis tooling for NEPSE

Python workflows for spotting under- and over-valued stocks from live Nepal Stock Exchange data — financial ratios, trend analysis, and web publishing for investors. The pipeline turns raw market data into repeatable, publication-ready signals.

Focus: repeatable analysis workflows that make market signals easier to inspect.

  1. 01
    Collectmarket prices and company fundamentals
  2. 02
    Assessratios, trends, and valuation signals
  3. 03
    Publishclear views for investor review
Study details
Question
Which NEPSE signals merit closer review?
Method
Reusable Python workflows for ratios, trends, and publication-ready analysis.
Outcome
Repeatable signals that remain inspectable instead of becoming opaque recommendations.
  • Python
  • financial data
  • automation
institutional system · educational software

CIME CBT — complete examination platform

An institution-ready Django platform covering enrollment, question banks, exam scheduling, secure test delivery, live monitoring, automatic scoring, analysis, and multi-format reporting.

Outcome: one role-based system for the complete examination lifecycle, delivered for 200+ students and packaged for local Windows and macOS deployment.

  • 200+ students supported
  • Live invigilation
  • PDF · XLSX · CSV reports
Learn more
Build details
Question
How can an institution manage the full digital-examination lifecycle without stitching together separate tools?
Method
Dedicated administrative, teacher, and student experiences with autosaving, invigilation, scoring, reporting, and backups.
Outcome
An institution-ready platform delivered for more than 200 students.
  • Django
  • secure exams
  • live monitoring
  • reporting
teaching and publishing

Python programs for researchers

Designed and taught training that moves students and professionals from basic syntax to real scientific workflows — analysis, visualization, and hands-on projects.

Focus: turning research questions into reproducible analysis and visualisation workflows.

Ask about training
Study details
Question
How can researchers move beyond syntax?
Method
Guided practice in data analysis, visualisation, and reproducible scientific workflows.
Outcome
Training organised around real research tasks and repeatable working habits.
  • curriculum
  • data analysis
  • mentorship

The published record behind these projects browse references →

Research Library

Published work across molecular simulation, materials, and energy systems.

Showing all 13 publications and talks.

  1. [1]

    Adhikari, B., Bhusal, A., Sun, Q., & Adhikari, K. (2025). Humidity-dependent CO2 capture in ultraporous MOF-177: insights from hybrid GCMC/MD simulations. Computational and Theoretical Chemistry, 1253.

    doi:10.1016/j.comptc.2025.115419 Explore the research
  2. [2]

    Bhusal, A., Adhikari, K., & Sun, Q. (2024). A hybrid density functional study on mechanochemistry of silicon carbide nanotubes. RSC Mechanochemistry.

    doi:10.1039/D4MR00043A
  3. [3]

    Subedi, A., Adhikari, B., Bhusal, A., & Adhikari, K. (2024). Molecular insights into CO2 sequestration in MOF-74. Journal of Institute of Science and Technology, 29(2), 65–73.

    doi:10.3126/jist.v29i2.66020
  4. [4]

    Adhikari, B., Subedi, A., Bhusal, A., & Adhikari, K. (2024). Molecular simulation of H2O adsorption in Mg-MOF-74. Journal of Engineering and Sciences, 3(2), 1–13.

    doi:10.3126/jes2.v3i2.72184
  5. [5]

    Sun, Q., Zhang, N., Zhu, P., Li, W., Guo, L., Fu, S., Bhusal, A., & Wang, S. (2024). Confined fluid interfacial tension and minimum miscibility pressure prediction in shale nanopores. Fuel, 364.

    doi:10.1016/j.fuel.2024.130949
  6. [6]

    Sun, Q., Bhusal, A., Zhang, N., & Adhikari, K. (2023). Molecular insight into minimum miscibility pressure estimation of shale oil/CO2 in inorganic nanopores using CO2 huff-n-puff. Chemical Engineering Science, 280, 119024.

    doi:10.1016/j.ces.2023.119024
  7. [7]

    Bhusal, A., & Adhikari, K. (2023). Melting curve of cobalt using molecular dynamics simulation. Prithvi Academic Journal, 6(1), 1–10.

    doi:10.3126/paj.v6i1.54570
  8. [8]

    Bhusal, A., Gurung, S., & Adhikari, K. (2023). Setting research priority areas for the Gandaki Province. Journal of Engineering and Sciences, 1(1), 50–56.

    doi:10.3126/jes2.v1i1.58402
  9. [9]

    Sun, Q., Zhang, N., Liu, W., Li, B., Li, S., Bhusal, A., Wang, S., & Li, Z. (2023). Insights into enhanced oil recovery by thermochemical fluid flooding for ultra-heavy reservoirs: an experimental study. Fuel, 331.

    doi:10.1016/j.fuel.2022.125651
  10. [10]

    Sun, Q., Zhang, N., Liu, W., Li, B., Li, S., Bhusal, A., Wang, S., & Li, Z. (2023). Experimental study on thermochemical composite system huff-n-puff process in ultra-heavy oil production. Fuel, 331.

    doi:10.1016/j.fuel.2022.126014
  11. [11]

    Tiwari, M., Bhusal, A., & Adhikari, K. (2019). Study of mechanochemistry of carbon nanotube using first principle. Himalayan Physics, 8, 39–46.

    doi:10.3126/hp.v8i0.30000
  12. [12]

    Bhusal, A. (2023). Estimating minimum miscibility pressure of shale oil/CO2 in inorganic nanopores using CO2 huff-n-puff. Virtual LAMMPS Workshop & Symposium, August 8–11.

    conference talk
  13. [13]

    Bhusal, A. (2022). Molecular insight into CO2 huff-n-puff EOR performance and minimum miscibility pressure estimation in organic nanopores. National Conference on Recent Trends in Science, Technology and Innovation, Pokhara, May 29–30.

    conference talk

Correspondence

Open to research collaboration, scientific computing, and technical consulting. If your work involves molecular simulation, data-heavy engineering, or practical tools for research teams — write to me.

Propose a collaborationaabiskar@pri.org.np

A useful first note includes your question, current evidence, and intended timeline.

Or file your decision the way reviewers do:

Accept Minor revisions Major revisions

Each opens a prefilled draft — every author knows which button you'll pick.

Ways to collaborate

Available worldwide
  • Research collaborationJoint studies, methods, and publications
  • Simulation consultingMD, GCMC, DFT, and reproducible workflows
  • Scientific-Python trainingPractical programs for research teams
  • Dissertation supervisionTechnical guidance from question to results