Research Projects
The A2E research program investigates next-generation materials across multiple technology domains: clean energy, quantum materials, semiconductors, and optoelectronics. We use density functional theory (DFT), ab initio molecular dynamics (AIMD), machine learning, and direct experimental validation to design, test, and understand materials at the atomic scale. Each project is chosen not only for scientific impact but for its relevance to US energy policy, supply chain sovereignty, and global technology competitiveness.
The following are three recently published works, shown as representative projects from AhmedLab. There are currently 17 such projects in progress.
Intellectual Property
"Machine Learning-Guided and Experimentally Validated Methodology for Predictive Fabrication and Optimization of Photovoltaic Devices"
"Real-Time Image Analytics Platform for Rolling Mill Stability Management"
"Predictive Analytics Framework for Clean-Energy Firm Market Entry Under New York State Net-Zero Policies"
Designing the Solar Cell the World Can Actually Build
Almost all solar panels today depend on polysilicon tied to forced labor in Xinjiang. We combined machine learning with lab fabrication to build a working, lead-free, polysilicon-free solar cell with a clean supply chain.
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A Battery Without Cobalt, Without Lithium, and Better Than Graphite
Lithium-ion batteries depend on cobalt linked to child labor and lithium refined mostly in China. We discovered a 2D material that stores more energy than graphite — with zero cobalt and zero lithium.
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Computing with Quantum Valleys to Cut AI's Energy Cost
AI data centers could consume 12% of US electricity by 2030. We demonstrated a new 2D material system that could make computer chips dramatically more energy-efficient — and stable at room temperature.
READ FULL PROJECT →Designing the Solar Cell the World Can Actually Build
Overview
Almost all solar panels today are made from polysilicon manufactured overwhelmingly in China's Xinjiang province. The US government has documented that this production involves forced labor. Our research focuses on perovskite solar cells — a next-generation photovoltaic technology — that can be made without polysilicon at all.
In our most recent work (Solar Energy, 2026), we combined a machine learning model trained on 26,000 experimental data points with direct laboratory fabrication to optimize a lead-free, bismuth-based perovskite solar cell. The result is a device with a verified power conversion efficiency of 3.73% — achieved without lead, without polysilicon, and with a supply chain that has no exposure to conflict minerals or sanctioned entities.
Technical Highlights
- Active material: Cs₃Bi₂I₉ — bismuth-based, fully lead-free
- ML model: Random Forest trained on 26,440 experimental data points from the Perovskite Database Project; 10 algorithms benchmarked; R² = 0.88 on training, 0.71 on test
- SHAP analysis: identified device architecture (ETL/HTL selection) and quenching method as the primary drivers of cell performance
- Champion device: PCE = 3.73%, VOC = 0.69 V, JSC = 12.22 mA/cm², FF = 43.94%
- Film stability: unchanged color for 100+ days in ambient conditions
Supply Chain & Policy Relevance
Bismuth (Bi): primary sources are Vietnam and Mexico. Cesium (Cs): primarily from Canada. Iodine (I): Chile and Japan. None appear on the USGS Critical Minerals list. None are sourced from Foreign Entities of Concern. A commercial solar technology built on this chemistry would satisfy the IRA's FEOC requirements automatically — unlike current silicon-based panels.
Key Publication
Choudhary, Rumman, Sahriar, Islam, Ahmed*, Efstathiadis. "Machine learning assisted development of lead-free Cs₃Bi₂I₉ perovskite solar cells." Solar Energy 315 (2026) 114743.
doi.org/10.1016/j.solener.2026.114743A Battery Without Cobalt, Without Lithium, and Better Than Graphite
Overview
Lithium-ion batteries — the batteries in every phone, laptop, and electric vehicle — depend on cobalt, 72% of which is mined in the Democratic Republic of Congo under conditions that include child labor. They also depend on lithium from a supply chain increasingly dominated by Chinese refiners.
Our research develops an entirely different kind of anode material: MBenes, two-dimensional sheets of chromium and boron just a few atoms thick. In our published work (Colloids and Surfaces A, 2024), we showed computationally using DFT and AIMD that fluorine-functionalized Cr₂B₂ achieves a sodium-ion storage capacity of 655 mAh/g — the highest of any MBene or MXene in the published literature — while chlorine-functionalized Cr₂B₂ reaches 818 mAh/g for lithium-ion storage. For comparison, commercial graphite anodes max out at 372 mAh/g.
Technical Highlights
- Materials: Cr₂B₂Cl₂ and Cr₂B₂F₂ — 2D transition metal borides (MBenes)
- Stability validated: dynamic (phonon), thermal (AIMD at 300 K for 10 ps), and mechanical (elastic constants)
- Metallic band structure: confirmed intrinsic conductivity, no barrier to electron transport
- Li diffusion barrier: 0.13 eV (Cr₂B₂F₂) and 0.20 eV (Cr₂B₂Cl₂) — lower than graphene (0.27 eV), MoS₂ (0.25 eV), and Ti₃C₂O₂ (0.28 eV)
- Na diffusion barrier: 0.14 eV (Cr₂B₂F₂) — comparable to best-in-class MXene structures
- Li storage capacity: 818 mAh/g (Cr₂B₂Cl₂) — 2.2× commercial graphite
- Na storage capacity: 655 mAh/g (Cr₂B₂F₂) — highest reported for any MBene or MXene
- Favorable OCV range for NIB: 0.20–0.71 V (Cr₂B₂F₂) — within desired 0.20–1.00 V window
Supply Chain & Policy Relevance
Chromium (Cr): primary producers are Kazakhstan, South Africa, India, and the United States. Boron (B): Turkey and the United States are the world's largest producers. Sodium carbonate (the electrolyte basis for NIBs): Wyoming's Green River Basin holds the world's largest trona deposit — a purely domestic US resource. Zero cobalt. Zero lithium. Zero FEOC exposure. The IRA's 45X Advanced Manufacturing Credit and 30D Clean Vehicle Credit do not currently recognize sodium-ion as a distinct battery chemistry. Our research provides the published technical foundation for correcting that classification gap.
Key Publication
Nirjhar, Tan-Ema, Sahriar, Dipon, Abed, Shorowordi, Ahmed*. "Tuning the Electrochemical Performance of Cr₂B₂ MBene Anodes for Li and Na-Ion Batteries through F and Cl-Functionalization: A DFT and AIMD Study." Colloids and Surfaces A 684 (2024) 133194.
doi.org/10.1016/j.colsurfa.2024.133194Computing with Quantum Valleys to Cut AI's Energy Cost
Overview
Every AI query generates heat. That heat requires water to cool. By 2030, AI data centers are projected to consume up to 12% of all US electricity — and most of that electricity is still generated from fossil fuels. The long-term solution is not just cleaner energy sources. It is more efficient chips.
Valleytronics is an approach to computing that exploits a quantum mechanical property of ultra-thin two-dimensional materials — called the valley degree of freedom — to perform computations with dramatically less energy than conventional silicon chips. In our work published in ACS Applied Electronic Materials (2025), we demonstrated that a WTe₂/MnSe₂ van der Waals heterostructure achieves a colossal valley splitting of 236 meV — one of the highest values ever reported for a 2D/2D system, equivalent in effect to applying an external magnetic field of 787 Tesla. This valley splitting is stable at room temperature and tunable by strain, opening a realistic path toward room-temperature valleytronic devices.
Technical Highlights
- System: monolayer 2H-WTe₂ on monolayer MnSe₂ (van der Waals heterostructure), lattice mismatch 3.1%
- Valley splitting: 236 meV in the most stable (A2) configuration — among the highest reported for any 2D/2D vdW system
- Mechanism: magnetic proximity effect via interfacial orbital hybridization; induced magnetic moment on W atom (0.041 μB in A2)
- Berry curvature: confirms broken time-reversal symmetry responsible for lifting valley degeneracy
- Curie temperature: 457.57 K at 6% tensile strain (well above room temperature), with valley splitting retaining 191 meV
- Stability confirmed dynamically (phonon spectrum), thermally (AIMD at 300 K), and mechanically (elastic constants)
- Device design: anomalous valley Hall effect device proposed for experimental realization; spin-valley indices switchable via MnSe₂ magnetization direction
- Applications pathway: quantum computing, neuromorphic computing, integrated photonics
Supply Chain & Policy Relevance
Post-silicon computing materials like valleytronic semiconductors do not appear on any FEOC supply chain list. WTe₂ and MnSe₂ require tungsten (primarily from China, but also significant US deposits), manganese (globally distributed, including US sources), and selenium (US, Bolivia, China). None of these carry the same supply chain vulnerabilities as the cobalt/lithium/Xinjiang inputs of the current AI hardware paradigm. At the hardware level, more energy-efficient chips reduce the water and electricity demand of every data center running on them. This is the materials science answer to the AI energy problem.
Key Publication
Nirjhar, Ahmed*. "Magnetic Proximity-Induced Colossal Valley Splitting in WTe₂ for Room Temperature Valleytronics." ACS Applied Electronic Materials 7(5) (2025) 2012–2021.
doi.org/10.1021/acsaelm.4c02276