ML-Driven Flow Chemistry & Autonomous Materials Discovery

Research Interests

My research interests lie at the intersection of flow chemistry, in-situ and in-line analytics, and machine-learning–guided experimentation for data-rich reaction screening and materials discovery. I'm developing the foundations for self-driving lab platforms that integrate real-time characterization with adaptive synthesis to accelerate the exploration and optimization of complex chemical and materials systems.

AI-Driven Flow Chemistry

Developing machine learning algorithms for real-time optimization and control of continuous flow chemical synthesis, enabling autonomous materials discovery.

Microfluidic Synthesis

Designing and implementing microfluidic platforms for precise control of nanomaterial synthesis with enhanced reproducibility and scalability.

In-Line and In-Situ Characterization

Developing multimodal in-line and in-situ analytics, including UV/Vis, Raman, FTIR, and other orthogonal sensors, to generate rich data streams for real-time monitoring and AI-driven experimentation in flow systems.

Nanomaterials Discovery

Investigating flow synthesis routes for advanced nanomaterials, including metallic and multimetallic nanoparticles, quantum dots, single-atom catalysts, and high-entropy alloy nanomaterials for catalytic and energy applications.

Small, 2026

Decoding α-MoC₁₋ₓ Nanoparticle Formation in Continuous Flow via Machine Learning

Bin Pan*, Allison P. Forsberg*, Ricki Chairil, Majed S. Madani, Susan E. Habas, Frederick G. Baddour, Richard L. Brutchey, and Noah Malmstadt

* Equal contribution

A mild continuous-flow synthesis of α-MoC₁₋ₓ nanoparticles from Mo(CO)₆, coupled with in-line spectroscopic monitoring and machine learning analysis to quantify precursor conversion and product formation in real time. A multilayer perceptron model deconvolutes complex, nonlinear UV-vis spectra to reveal a two-step pathway—precursor conversion to an amorphous intermediate followed by intraparticle crystallization—with the first step being rate limiting. Ex situ SAXS and XRD validate the predicted concentration profiles, paving the way for self-driving, flow-based platforms for nanoparticle synthesis.

Chemistry of Materials, 2026

Mechanistic Insights into the Aminolytic Decomposition of Mo(CO)₆ to Form MoC₁₋ₓ Nanoparticles

Brendan Ward-O'Brien, Allison Forsberg, Yizhen Chen, Noah Malmstadt, Majed S. Madani, and Richard L. Brutchey

First mechanistic insight into α-MoC₁₋ₓ nanoparticle formation by investigating the aminolytic decomposition of Mo(CO)₆ in oleylamine (OAm) and N,N-dimethyloctadecylamine (DODA). Ex situ FT-IR, XRD, and in situ synchrotron SAXS reveal stepwise carbonyl ligand substitution, conversion to an isolable amorphous intermediate, and reaction-controlled crystallization, highlighting the critical role of solvent-dependent precursor–ligand interactions in controlling decomposition temperature, kinetics, and final nanoparticle size.

Experiment performed at SLAC National Accelerator Laboratory

ACS Nano, 2024

Solvent Dependence of Ionic Liquid-Based Pt Nanoparticle Synthesis: Machine Learning-Aided In-Line Monitoring in a Flow Reactor

Bin Pan*, Majed S. Madani*, Allison P. Forsberg, Richard L. Brutchey, and Noah Malmstadt

* Equal contribution

Machine learning-based approach to analyze in-line UV-vis spectrophotometric data to determine Pt NP product concentrations in ionic liquid solvents using flow chemistry.

ACS Applied Nano Materials, 2022

Throughput Optimization of Molybdenum Carbide Nanoparticle Catalysts in a Continuous Flow Reactor Using Design of Experiments

Lania R. Karadaghi*, Majed S. Madani*, Emily M. Williamson*, Anh T. To, Susan E. Habas, Frederick G. Baddour, Joshua A. Schaidle, Daniel A. Ruddy, Richard L. Brutchey, and Noah Malmstadt

* Equal contribution

Statistical design of experiments in tandem with response surface methodology for parametric screening analysis to optimize the throughput of MoC nanoparticle synthesis in a millifluidic flow reactor.

In-situ X-ray scattering studies of nanoparticle nucleation and growth kinetics in flow reactors

Real-time characterization of nanoparticle formation mechanisms using synchrotron-based X-ray scattering techniques integrated with continuous flow synthesis platforms.

AI-Optimized Flow Chemistry for Nanoparticle Synthesis

Novel approach using reinforcement learning to optimize continuous flow synthesis of monodisperse nanoparticles.

Conference Presentations

AIChE Annual Meeting, 2022

Evaluation of Nucleation and Growth Kinetics of Ionic Liquid-Based Pt Nanoparticles Synthesis in a Millifluidic Reactor By in Situ Small-Angle X-Ray Scattering

M. Madani, N. Malmstadt

AIChE Annual Meeting, 2021

Application of a Continuous Flow Millifluidic Reactor Towards Optimizing Manufacturing Throughput for Molybdenum Carbide Nanoparticles

M. Madani, L.R. Karadaghi, E.M. Williamson, S.E. Habas, F. Baddour, J.A. Schaidle, D.A. Ruddy, R.L. Brutchey, N. Malmstadt

Virtual AIChE Annual Meeting, 2020

Flow Reactors for Rapid Screening of Reaction Parameters to Synthesize Molybdenum Carbide Catalysts for Biomass Conversion Processes

M. Madani, L. Wang, L.R. Karadaghi, R. Brutchey, N. Malmstadt

Research Interests

AI-Driven Flow Chemistry

Developing machine learning algorithms for real-time optimization and control of continuous flow chemical synthesis, enabling autonomous materials discovery.

Microfluidic Synthesis

Designing and implementing microfluidic platforms for precise control of nanomaterial synthesis with enhanced reproducibility and scalability.

In-Line and In-Situ Characterization

Nanomaterials Discovery

Publications

Small, 2026

Decoding α-MoC₁₋ₓ Nanoparticle Formation in Continuous Flow via Machine Learning

Bin Pan*, Allison P. Forsberg*, Ricki Chairil, Majed S. Madani, Susan E. Habas, Frederick G. Baddour, Richard L. Brutchey, and Noah Malmstadt

* Equal contribution

Chemistry of Materials, 2026

Mechanistic Insights into the Aminolytic Decomposition of Mo(CO)₆ to Form MoC₁₋ₓ Nanoparticles

Brendan Ward-O'Brien, Allison Forsberg, Yizhen Chen, Noah Malmstadt, Majed S. Madani, and Richard L. Brutchey

Experiment performed at SLAC National Accelerator Laboratory

ACS Nano, 2024

Solvent Dependence of Ionic Liquid-Based Pt Nanoparticle Synthesis: Machine Learning-Aided In-Line Monitoring in a Flow Reactor

Bin Pan*, Majed S. Madani*, Allison P. Forsberg, Richard L. Brutchey, and Noah Malmstadt

* Equal contribution

Machine learning-based approach to analyze in-line UV-vis spectrophotometric data to determine Pt NP product concentrations in ionic liquid solvents using flow chemistry.

ACS Applied Nano Materials, 2022

Throughput Optimization of Molybdenum Carbide Nanoparticle Catalysts in a Continuous Flow Reactor Using Design of Experiments

Lania R. Karadaghi*, Majed S. Madani*, Emily M. Williamson*, Anh T. To, Susan E. Habas, Frederick G. Baddour, Joshua A. Schaidle, Daniel A. Ruddy, Richard L. Brutchey, and Noah Malmstadt

* Equal contribution

In-situ X-ray scattering studies of nanoparticle nucleation and growth kinetics in flow reactors

Real-time characterization of nanoparticle formation mechanisms using synchrotron-based X-ray scattering techniques integrated with continuous flow synthesis platforms.

AI-Optimized Flow Chemistry for Nanoparticle Synthesis

Novel approach using reinforcement learning to optimize continuous flow synthesis of monodisperse nanoparticles.

Conference Presentations

AIChE Annual Meeting, 2022

Evaluation of Nucleation and Growth Kinetics of Ionic Liquid-Based Pt Nanoparticles Synthesis in a Millifluidic Reactor By in Situ Small-Angle X-Ray Scattering

M. Madani, N. Malmstadt

AIChE Annual Meeting, 2021

Application of a Continuous Flow Millifluidic Reactor Towards Optimizing Manufacturing Throughput for Molybdenum Carbide Nanoparticles

M. Madani, L.R. Karadaghi, E.M. Williamson, S.E. Habas, F. Baddour, J.A. Schaidle, D.A. Ruddy, R.L. Brutchey, N. Malmstadt

Virtual AIChE Annual Meeting, 2020

Flow Reactors for Rapid Screening of Reaction Parameters to Synthesize Molybdenum Carbide Catalysts for Biomass Conversion Processes

M. Madani, L. Wang, L.R. Karadaghi, R. Brutchey, N. Malmstadt