Perovskite Materials Simulation Engineer — End-to-End
Pipeline Architect
Company: TakaHuman LLC (US)
Location: Remote / Hybrid
Company Vision & Background
TakaHuman LLC is a U.S.-based company founded by Dan Takahashi, a serial entrepreneur +
Founder of 2 prior companies with successful exits (including a recent IPO on the Tokyo
Stock Exchange accomplished in only ~2.5 years) https://www.linkedin.com/in/dantakahashi/
Our long-term vision is to create a new "higher" level of humanity — extending healthspan,
elevating cognitive and physical performance, and enabling seamless integration between
biological and digital intelligence. Our current mission is to build a unified scientific AI
simulation platform spanning development/optimization of (1) medicine; (2) energy materials;
(3) semiconductor materials.
Our current plan is to build an enterprise solution POC for all 3 domains above, secure VC
funding this year, and immediately expand our business with a US office and generate
substantial revenue.
Role Summary
Design, build, and own the complete computational simulation pipeline for perovskite
materials — from first-principles atomistic discovery of novel compositions through devicelevel performance modeling, manufacturing process simulation, and regulatory-grade
documentation. You will translate the full perovskite R&D lifecycle into reproducible, AIaugmented software workflows that run on TakaHuman's unified simulation platform,
working alongside our drug development and battery materials simulation teams.
This role spans two modes of operation: (1) discovery — screening and predicting novel
perovskite compositions with target properties (bandgap, stability, defect tolerance, carrier
mobility); and (2) optimization — taking commercially available or experimentally validated
perovskite formulations and systematically improving device architectures, layer stacks, and
fabrication parameters for higher performance and manufacturability.
Required Experience & Technical Skills
1. Senior or strong mid-level Python — able to architect production-grade scientific
codebases. Experience with high-throughput workflow tools (AiiDA, FireWorks,
Pymatgen, JARVIS-Tools) and materials databases (Materials Project, AFLOW,
OQMD, Perovskite Database Project)
2. ML-driven materials design — familiarity with generative models, active learning
loops, or Bayesian optimization for materials property prediction and inverse design
3. Manufacturing process simulation — thin-film deposition modeling, crystallization
kinetics, or CFD for slot-die / roll-to-roll coating processes
4. Regulatory & standards knowledge — familiarity with PV certification (IEC 61215,
IEC 61730), environmental regulations for lead-containing materials (RoHS, REACH),
or experience connecting simulation to automated / robotic experimentation
5. Simulation tools — proficiency across the perovskite simulation stack, with depth in
the areas marked below: Strong experience required:
Device simulation: at least two of SCAPS-1D, wxAMPS, AFORS-HET,
Silvaco ATLAS, Sentaurus TCAD, COMSOL Multiphysics, Fluxim
SETFOS, OghmaNano, or Lumerical
Degradation & reliability: Fluxim Litos (accelerated stress simulation),
GPVDM (degradation modeling), or custom Python-based aging models
coupling ion migration, moisture ingress, and thermal decomposition
Manufacturing process / CFD: COMSOL (fluid/thermal modules), ANSYS
Fluent, or OpenFOAM for slot-die / roll-to-roll coating flow and thermal
modeling; phase-field tools (MOOSE, FEniCS) for crystallization kinetics a plus
Foundational experience required (beginner level acceptable):
First-principles (DFT): VASP, Quantum ESPRESSO, CASTEP, Gaussian,
CP2K, ABINIT, or FHI-aims
Molecular dynamics: LAMMPS, GROMACS, ASE, or ML force field
frameworks (DeePMD-kit etc.)
Key Responsibilities
Stage 1 — Materials Discovery & Screening
Design and execute high-throughput first-principles workflows to explore perovskite
compositional spaces (A-site, B-site, X-site substitutions in ABX₃ structures). Build
automated DFT pipelines for calculating formation energy, thermodynamic stability (energy
above convex hull), electronic band structure, density of states, optical absorption spectra, and
defect formation energies. Integrate machine learning surrogate models to accelerate
screening across hundreds of thousands of candidate compositions.
Stage 2 — Atomistic & Molecular Dynamics Simulation
Develop and maintain molecular dynamics simulation workflows for studying ion migration,
phase transitions, thermal stability, degradation mechanisms, and interfacial behavior of
perovskite materials. Implement and validate classical and machine-learned interatomic
potentials (MLIPs / ML force fields) for long-timescale simulations. Couple MD results with
DFT validation for accuracy assurance.
Stage 3 — Device-Level Simulation
Build device simulation pipelines that translate material-level properties into photovoltaic
device performance predictions (power conversion efficiency, open-circuit voltage, short-
circuit current density, fill factor, quantum efficiency). Model charge transport, recombination
dynamics, interface band alignment, and defect-mediated losses across multi-layer device
stacks (ETL/perovskite/HTL). Optimize layer thicknesses, doping concentrations, and contact
configurations.
Stage 4 — Manufacturing Process & Scale-Up Simulation
Develop computational models for solution-based deposition processes (spin coating, slot-die
coating, blade coating), vapor deposition, and roll-to-roll manufacturing. Simulate thin-film
crystallization kinetics, thermal annealing profiles, and morphological evolution. Model largearea uniformity, throughput, and yield to bridge the lab-to-fab gap.
Stage 5 — Stability, Degradation & Lifetime Prediction
Build accelerated aging and degradation simulation models addressing moisture ingress,
thermal decomposition, photo-degradation, and ion migration. Develop predictive models for
device lifetime under standard test conditions (IEC 61215 / IEC 61646 equivalents for
perovskite-specific protocols). Integrate environmental stress factors into performance
projections.
Stage 6 — Regulatory & Techno-Economic Documentation
Generate simulation-backed documentation for regulatory submissions, including material
safety data (lead content, encapsulation integrity), environmental impact assessments, and
performance certification packages. Produce techno-economic analysis (TEA) models
comparing cost-per-watt, levelized cost of energy (LCOE), and bill-of-materials against
incumbent silicon technologies.
Cross-Cutting — Platform Integration & Automation
Wrap all simulation stages into Python-based orchestration pipelines with standardized
input/output schemas, metadata tagging, and provenance tracking compatible with
TakaHuman's Model Control Plane. Collaborate with AI/ML engineers to integrate fine-tuned
LLMs for parameter prediction, literature extraction, and automated report generation.
Compensation + Benefits
Competitive salary
Opportunity to receive founding-level equity
Flexible/remote work arrangement
Opportunity to participate in a fast-paced startup with an experienced Founder/CEO