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Research

Three research fields on one compute foundation.

Chemical and pharmaceutical modelling, complex systems, and financial modelling—each with its own methods, sharing one high-performance computing layer.

Riverains Labs

Centre for Interdisciplinary Simulation Research

Research notes · Vol. 1

Doc. RL-RN-2026

Working paper

Three research fields on a shared compute foundation

Prepared by Riverains Labs Private Limited

Correspondence: admin@riverainslabs.com

Abstract.

This note describes the three research fields operated by Riverains Labs: chemical and pharmaceutical modelling (Ewald), complex systems modelling (Ising), and financial modelling (Monte Carlo). Each field maintains its own methods and client programmes. All three rely on a shared high-performance computing layer and a common Research-as-a-Service engagement model. Named dedications for each field appear as footnotes at the close of the corresponding section.

Keywords: computational chemistry; complex systems; quantitative finance; high-performance computing; Research-as-a-Service

Section 1 · Series Ewald

1.Chemical and pharmaceutical modelling

1.1 Abstract.

Ewald runs computational chemistry and biologics studies for clients who need virtual screening, formulation physics, or process modelling without expanding a permanent in-house HPC team.

Figure 1: Chemical and pharmaceutical modelling
Figure 1. Schematic for the Ewald field (chemical and pharmaceutical modelling).

1.2 Methods of work.

  1. 1.1

    Virtual screening capacity.

    GPU-backed campaigns over large compound libraries, with ranked hits and documentation that R&D teams can take into the next experimental round.

  2. 1.2

    Biologics and LNP physics.

    Coarse-grained molecular dynamics for lipid nanoparticle assembly, mRNA encapsulation, and concentrated antibody behaviour.

  3. 1.3

    Continuous manufacturing models.

    Coupled CFD and chemistry models used to check residence time, mixing, and thermal behaviour from pilot scale toward production.

1.3 Research avenues.

  1. (a)QM/MM studies of enzyme–inhibitor kinetics and reaction barriers
  2. (b)All-atom and coarse-grained molecular dynamics of biologics and formulations
  3. (c)Generative molecular design checked against physics-based validation
  4. (d)Multiphase simulation of reactors and bioreactors

1.4 Instruments.

Principal software used in this field: LAMMPS, GROMACS, OpenMM, and PySCF.

†Kohlmeyer-Zentrum. Named for contributions to open molecular simulation practice.

Section 2 · Series Ising

2.Complex systems modelling

2.1 Abstract.

Ising builds tractable models of organisations, traffic, and cyber-physical systems so clients can test policy and operations questions before committing capital or regulation.

Figure 2: Complex systems modelling
Figure 2. Schematic for the Ising field (complex systems modelling).

2.2 Methods of work.

  1. 2.1

    Collective behaviour models.

    Large agent-based simulations of logistics networks, organisational dynamics, and autonomous fleets where local rules produce system-level outcomes.

  2. 2.2

    Spatial spread analysis.

    Cellular automata and network models for traffic, agricultural spread, and the geographic reach of policy measures.

  3. 2.3

    Uncertainty in critical systems.

    Bayesian and polynomial-chaos methods used with sociophysical models to stress-test infrastructure before major investment.

2.3 Research avenues.

  1. (a)Organisational dynamics and societal modelling
  2. (b)Traffic and spatial propagation with agent-based and Ising-style models
  3. (c)Digital twins for grids, factories, and sensor-rich feedback loops
  4. (d)Emergent behaviour in decentralised swarm systems

2.4 Instruments.

Principal software used in this field: Mesa, NetLogo, SUMO, igraph, and Chaospy.

†Stauffer-Zentrum. Focused on statistical mechanics and agent-based methods for organisations and infrastructure.

Section 3 · Series Monte Carlo

3.Financial modelling

3.1 Abstract.

Monte Carlo supplies quantitative research for yield dynamics, foreign-exchange and derivatives pricing, and valuation questions in illiquid private credit.

Figure 3: Financial modelling
Figure 3. Schematic for the Monte Carlo field (financial modelling).

3.2 Methods of work.

  1. 3.1

    Treasury and macro dynamics.

    Stochastic and entropy-based frameworks for yield curves, rate shocks, and portfolio stress under changing regimes.

  2. 3.2

    FX and exotic derivative pricing.

    Physics-informed and deep BSDE-style solvers for high-dimensional pricing problems where classical grids are impractical.

  3. 3.3

    Private credit and microstructure.

    Valuation engines for illiquid credit, together with multi-agent order-book simulation for impact and liquidity stress.

3.3 Research avenues.

  1. (a)Treasury metrics and yield dynamics
  2. (b)Foreign exchange and currency evolution
  3. (c)Private credit market estimation
  4. (d)High-frequency microstructure simulation

3.4 Instruments.

Principal software used in this field: QuantLib, PyMC, Stan, and NetworkX.

†Kolmogorov Research Centre. Probability, entropy methods, and stochastic simulation for markets and risk.

A.Adjacent capabilities

The following supporting practice is available alongside the three primary fields when a programme requires it.

  1. A.1

    Fluid dynamics and aerodynamics.

    CFD, heat transfer, and multiphysics support for engineering programmes.

  2. A.2

    Geospatial and remote sensing analytics.

    Satellite and environmental data workflows for climate, agriculture, and infrastructure studies.

  3. A.3

    Scientific language systems.

    Domain-focused NLP and retrieval tools for literature review and knowledge management.

  4. A.4

    Emerging quantum simulation.

    Exploratory quantum–classical workflows where they usefully extend classical HPC.

Correspondence.

For scoped studies or retainers, write to admin@riverainslabs.com, or see Contact and Infrastructure.

— End of research notes —