Dr Shyam S Khanal: A Nepali computational chemist in Australia
Dr Shyam S Khanal represents a generation of Nepali scientists who have taken their training beyond the Himalayas and into some of the most advanced research facilities in the world. His career illustrates how a chemist trained in Kathmandu can end up contributing to projects that rely on petabyte-scale data and quantum mechanical models running on supercomputers in Canberra or Perth.
Computational chemistry has matured into a backbone of modern molecular science, and researchers of Nepali background have played a quiet but consistent role in that growth. Dr. Khanal's profile stands out because he works at the intersection of theoretical method development and practical applications, with a steady record of collaboration across Australian and South Asian institutions.
In Australia, the discipline is anchored by national facilities such as the National Computational Infrastructure (NCI) in Canberra and the Pawsey Supercomputing Centre in Perth, alongside university clusters in Melbourne, Brisbane, and Sydney. For a Nepali researcher whose early training relied on smaller university clusters, access to Australian high-performance computing has reshaped what kinds of chemical questions can be tackled.
This profile traces his academic path, his current research themes, and the way his work connects to the wider Nepali chemistry community that NepaChem serves. It also highlights the practical choices that have shaped his career, from the choice of exchange-correlation functional to the choice of supercomputing allocation.
Methods commonly used in computational chemistry
Modern computational chemistry relies on a layered toolkit, and researchers in Dr. Khanal's group draw on different methods depending on the size of the system and the property of interest. For small molecules, high-accuracy wavefunction methods remain the benchmark, while for systems with hundreds of atoms, density functional theory (DFT) offers the most practical compromise between cost and reliability.
The table below summarises the most widely used approaches, the kinds of problems they handle best, and the trade-offs that come with each choice. It is intended as a quick reference for students and early-career researchers comparing methods for their own projects.
| Method | Typical use case | Strengths | Limitations |
|---|---|---|---|
| Hartree-Fock | Small molecules, teaching baseline | Simple, interpretable, low memory | No electron correlation, poor for metals |
| DFT with B3LYP | Organic molecules, reaction pathways | Good cost-accuracy balance | Failure in strongly correlated systems |
| DFT with M06-2X or ωB97X-D | Non-covalent interactions, thermochemistry | Captures dispersion, broad applicability | Slower than pure GGA functionals |
| Coupled Cluster (CCSD(T)) | High-accuracy benchmarks | Gold standard accuracy | Cubic scaling, large memory footprint |
| Molecular Dynamics (force fields) | Proteins, solvents, soft matter | Handles millions of atoms | Limited to classical force fields |
| QM/MM | Enzymes, hybrid biomolecular systems | Quantum accuracy in region of interest | Boundary treatment complexity |
Early training in Nepal
Dr. Khanal's scientific formation began in Nepal, where the chemistry curriculum at Tribhuvan University and other institutions has long emphasised strong foundations in physical and organic chemistry. He completed his undergraduate and master's-level work in an environment where laboratory resources were often modest but teaching was rigorous, and where students were encouraged to derive equations rather than simply memorise them.
That grounding shaped his later interest in computational methods, because many early simulations in developing-country universities were run on shared departmental desktops with limited licences. He learned to write efficient input files, manage Gaussian and GAMESS jobs by hand, and interpret vibrational frequencies before he had access to dedicated high-performance computing time. Several of his early papers, co-authored with Nepali colleagues, focused on structural and spectroscopic properties of heterocyclic compounds of pharmacological interest.
A formative period during his master's research, working on density functional theory calculations of hydrogen-bonded complexes, convinced him that computation was not just a service tool but a research direction worth pursuing in its own right. He has often returned to this theme in talks for Nepali student audiences, emphasising that modest computing resources can still support publishable theoretical work.
Pathway into Australian research
After completing his doctoral research, Dr. Khanal moved to Australia, joining a research group at the University of New England before taking up subsequent positions that connected him to broader national infrastructure. Australian universities have a long tradition of recruiting chemists from South Asia into theoretical and computational groups, and campuses in Armidale, Sydney, and Melbourne have all hosted members of the Nepali diaspora at various stages of their training.
The Australian research system, structured around Australian Research Council Discovery and Linkage grants, offered him a different scale of project compared with his earlier work. Allocation schemes at NCI and Pawsey, accessed through the National Computational Merit Allocation Scheme, gave him access to hundreds of thousands of CPU-hours and, more recently, to GPU partitions suited to machine-learning-augmented quantum chemistry.
Moving between regional New South Wales and larger institutions on the eastern seaboard, including time spent at the University of Melbourne, has also given him a feel for the geographic spread of Australian chemistry. He has noted that the Australian Institute for Bioengineering and Nanotechnology in Brisbane remains an attractive collaborator for anyone working on method development.
Focus areas in computational chemistry
His main research interests cluster around excited-state calculations, non-covalent interactions, and the design of molecular systems for energy and biomedical applications. He has used time-dependent DFT, post-Hartree-Fock methods, and increasingly machine-learning potentials to study systems where electronic correlation and dispersion both play a measurable role.
A recurring topic in his publications is the accurate modelling of intermolecular interactions in biological and materials chemistry. Whether studying halogen bonding in drug-like molecules or charge-transfer excitations in organic photovoltaics, the goal has been to find a balance between accuracy and cost that suits Australian allocation budgets. Several of his papers benchmark functionals against CCSD(T) or high-level composite methods, providing practical guidance for the broader community.
He has also explored reaction mechanisms of environmental relevance, including atmospheric oxidation pathways that connect with Australian bushfire chemistry. This thread of work ties his theoretical interests to locally important questions about air quality in Sydney and Melbourne, and to the analytical work done by groups at CSIRO and ANSTO.
Collaborations and community engagement
Beyond his publications, Dr. Khanal has been active in connecting Australian computational chemistry with the Nepali scientific diaspora. He has organised joint seminars between Australian and Nepali universities, often delivered by video link from Perth or Adelaide to audiences in Kathmandu and Pokhara.
He participates regularly in NepaChem community events and has supervised several Nepali-origin postgraduate students in Australia. His group has hosted visiting students from Tribhuvan University and Kathmandu University, providing short-term access to Australian HPC environments and training in modern codes such as ORCA, Q-Chem, and Psi4.
For Australian readers, his profile is also a useful reminder that computational chemistry in the country depends on a wide international talent base. Many of the strongest groups at the Australian National University, Monash, and the University of Queensland include researchers who trained in South Asia, reinforcing the value of skilled migration pathways for science.
The next practical step for any reader inspired by this profile is to register for a Pawsey or NCI user account and run a small DFT job on a publicly available training partition during the next intake window.