Awesome-Intelligent-Deposition-Papers

This repository contains the list of representative works in the survey "Intelligent Deposition: Artificial Intelligence in Organic Chemical Vapor Deposition for Emerging Materials Technologies", along with relevant reference materials.

CVD / oCVD Fundamentals and Reviews

Reviews, primers, and application overviews of CVD, polymer CVD, and oCVD to establish background and terminology.

• Surface Nanostructure Fabrication by Initiated Chemical Vapor Deposition and Its Combined Technologies, 2025, ACS Macro Letters. [📄 Paper]
• Digital twins for accurate prediction beyond routine operation, 2025, Computers & Chemical Engineering. [📄 Paper]
• Mechanistic study of oxidative chemical vapor deposition of polypyrrole: Effects of the inert gas and deposition time, 2025, Applied Surface Science Advances. [📄 Paper]
• Designing Organic and Hybrid Surfaces and Devices with Initiated Chemical Vapor Deposition (iCVD), 2024, Adv. Mater. 36, 2306665 (2024). [📄 Paper]
• Self-Driving Laboratories for Chemistry and Materials Science, 2024, Chemical Reviews. [📄 Paper]
• Triflate Salts as Alternative Non-Chlorinated Oxidants for the Oxidative Chemical Vapor Deposition and Electronic Engineering of Conjugated Polymers, 2024, Macromolecules. [📄 Paper]
• The rise of self-driving labs in chemical and materials sciences, 2023, Nature Synthesis. [📄 Paper]
• Kinetically Limited Bulk Polymerization of Polymer Thin Films by Initiated Chemical Vapor Deposition, 2023, Macromolecules. [📄 Paper]
• The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering, 2023, Industrial & Engineering Chemistry Research. [📄 Paper]
• A sulfur cathode design strategy for polysulfide restrictions and kinetic enhancements in Li-S batteries through oxidative chemical vapor deposition, 2023, Nano Energy. [📄 Paper]
• Credibility consideration for digital twins in manufacturing, 2023, Manufacturing Letters. [📄 Paper]
• Oxidative Molecular Layer Deposition of Amine-Containing Conjugated Polymer Thin Films, 2022, ACS Applied Polymer Materials. [📄 Paper]
• Design Strategies for Structurally Controlled Polymer Surfaces via Cyclophane‐Based CVD Polymerization and Post‐CVD Fabrication, 2022, Advanced Materials. [📄 Paper]
• Chemical vapor deposition of 2D materials: A review of modeling, simulation, and machine learning studies, 2022, iScience. [📄 Paper]
• Advances in Atomic Layer Deposition, 2022, Nanomanufacturing and Metrology. [📄 Paper]
• A viable approach to prepare 3C-SiC coatings by thermal MOCVD using commercial grade precursors, 2022, Journal of the European Ceramic Society. [📄 Paper]
• Chemical vapour deposition, 2021, Nature Reviews Methods Primers. [📄 Paper]
• Physics-informed machine learning, 2021, Nature Reviews Physics. [📄 Paper]
• Vapor-deposited functional polymer thin films in biological applications, 2020, Journal of Materials Chemistry B. [📄 Paper]
• Solvent-Less Vapor-Phase Fabrication of Membranes for Sustainable Separation Processes, 2020, Engineering. [📄 Paper]
• Nanoscale control by chemically vapour-deposited polymers, 2020, Nature Reviews Physics. [📄 Paper]
• CVD polymers for devices and device fabrication, 2017, Adv. Mater. [📄 Paper]
• 25th Anniversary Article: CVD Polymers: A New Paradigm for Surface Modifi cation and Device Fabrication, 2013, Advanced Materials. [📄 Paper]
• Transition between kinetic and mass transfer regimes in the initiated chemical vapor deposition from ethylene glycol diacrylate, 2009, Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films. [📄 Paper]

Morphology Engineering: Physical/Selective Patterning

Masks and soft masks, oblique incidence, rotating stages, area-selective deposition, and microstructuring.

• Lateral Microstructuring of oCVD PEDOT Nanolayers Fabricated by EDOT/SbCl5 Chemistry and Photoresist-Based Lift-Off, 2025, ACS Applied Polymer Materials. [📄 Paper]
• Recent progress in non-photolithographic patterning of polymer thin films, 2023, Progress in Polymer Science. [📄 Paper]
• Systematic Studies into the Area Selectivity of Chemical Vapor Deposition Polymerization, 2023, ACS Applied Materials & Interfaces. [📄 Paper]
• One‐Step Bottom‐Up Growth of Highly Liquid Repellent Worm‐Like Surfaces on Planar Substrates, 2022, Advanced Materials Interfaces. [📄 Paper]
• All-dry free radical polymerization inside nanopores: Ion-milling-enabled coating thickness profiling revealed “necking” phenomena, 2022, Journal of Vacuum Science & Technology A. [📄 Paper]
• Solventless Synthesis and Patterning of UV‐Responsive Poly(allyl methacrylate) Film, 2019, Macromolecular Chemistry and Physics. [📄 Paper]
• Surface-patterning of polymeric membranes: fabrication and performance, 2018, Current Opinion in Chemical Engineering. [📄 Paper]
• Sequential deposition of patterned porous polymers using poly(dimethylsiloxane) masks, 2017, Polymer. [📄 Paper]
• Carbon Nanotube‐Directed Polytetrafluoroethylene Crystal Growth via Initiated Chemical Vapor Deposition, 2013, Macromol. Rapid Commun. [📄 Paper]
• Initiated Chemical Vapor Deposition‐Based Method for Patterning Polymer and Metal Microstructures on Curved Substrates, 2012, Advanced Materials. [📄 Paper]
• ChemInform Abstract: Patterned Polymer Brushes, 2012, ChemInform. [📄 Paper]
• Emergent properties of spatially organized poly(p-xylylene) films fabricated by vapor deposition, 2008, Colloids and Surfaces A: Physicochemical and Engineering Aspects. [📄 Paper]
• Patterning surfaces with functional polymers, 2008, Nature Materials. [📄 Paper]
• Substrate‐Selective Chemical Vapor Deposition of Reactive Polymer Coatings, 2008, Advanced Materials. [📄 Paper]
• Transition Metals for Selective Chemical Vapor Deposition of Parylene-Based Polymers, 2000, Chemistry of Materials. [📄 Paper]
• Use of Microcontact Printing for Generating Selectively Grown Films of Poly(p-phenylene vinylene) and Parylenes Prepared by Chemical Vapor Deposition, 2000, Langmuir. [📄 Paper]

Morphology Engineering: Interfacial Effects and Liquid Substrates

Droplet condensation, wetting/spreading thermodynamics, and liquid substrates with interfacial tension control to form particles, nanocones, and textured films.

• Chemical vapor deposition of transparent superhydrophobic anti-Icing coatings with tailored polymer nanoarray architecture, 2023, Chemical Engineering Journal. [📄 Paper]
• Batch-Operated Condensed Droplet Polymerization to Understand the Effect of Temperature on the Size Distribution of Polymer Nanodomes, 2023, Organic Materials. [📄 Paper]
• Versatile and Rapid Synthesis of Polymer Nanodomes via Template- and Solvent-free Condensed Droplet Polymerization, 2022, Chemistry of Materials. [📄 Paper]
• Self‐Wrinkling Vapor‐Deposited Polymer Films with Tunable Patterns, 2022, Advanced Functional Materials. [📄 Paper]
• Accessing Thin Film Wetting Regimes during Polymer Growth by Initiated Chemical Vapor Deposition, 2022, Langmuir. [📄 Paper]
• Interactions between polymers and liquids during initiated chemical vapor deposition onto liquid substrates, 2020, Molecular Systems Design & Engineering. [📄 Paper]
• Synthesis of Functional Particles by Condensation and Polymerization of Monomer Droplets in Silicone Oils, 2017, Langmuir. [📄 Paper]
• Two-Stage Growth of Polymer Nanoparticles at the Liquid–Vapor Interface by Vapor-Phase Polymerization, 2016, Langmuir. [📄 Paper]
• Microstructured Films Formed on Liquid Substrates via Initiated Chemical Vapor Deposition of Cross-Linked Polymers, 2015, Langmuir. [📄 Paper]
• Synthesis of Polymer Nanoparticles via Vapor Phase Deposition onto Liquid Substrates, 2014, Macromolecular Rapid Communications. [📄 Paper]
• Time-series characteristics and geometric structures of drop-size distribution density in dropwise condensation, 2014, International Journal of Heat and Mass Transfer. [📄 Paper]
• Formation of Polymer–Ionic Liquid Gels Using Vapor Phase Precursors, 2013, Macromolecules. [📄 Paper]
• Formation of Heterogeneous Polymer Films via Simultaneous or Sequential Depositions of Soluble and Insoluble Monomers onto Ionic Liquids, 2013, Langmuir. [📄 Paper]
• Effect of Surface Tension, Viscosity, and Process Conditions on Polymer Morphology Deposited at the Liquid–Vapor Interface, 2013, Langmuir. [📄 Paper]
• Ultrathin Free-Standing Polymer Films Deposited onto Patterned Ionic Liquids and Silicone Oil, 2012, Macromolecules. [📄 Paper]
• Vapor-Phase Free Radical Polymerization in the Presence of an Ionic Liquid, 2011, Macromolecules. [📄 Paper]

Morphology Engineering: Dynamic Templates and Porous Structures

Dynamic templates such as sublimating ice and solid monomer deposition to realize multiscale pores, particles, and films.

• Controlling Superhydrophobicity on Complex Substrates Based on a Vapor-Phase Sublimation and Deposition Polymerization, 2023, ACS Applied Materials & Interfaces. [📄 Paper]
• Membrane Pore Size Distribution by Design via Kinetic Engineering Using Initiated Chemical Vapor Deposition, 2023, Macromolecules. [📄 Paper]
• Fabrication of Asymmetrical and Gradient Hierarchy Structures of Poly-p-xylylenes on Multiscale Regimes Based on a Vapor-Phase Sublimation and Deposition Process, 2020, Chemistry of Materials. [📄 Paper]
• Vapor sublimation and deposition to build porous particles and composites, 2018, Nature Communications. [📄 Paper]
• Systematic study of the growth and morphology of vapor deposited porous polymer membranes, 2014, Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films. [📄 Paper]
• Simultaneous Polymerization and Solid Monomer Deposition for the Fabrication of Polymer Membranes with Dual-Scale Porosity, 2013, Macromolecules. [📄 Paper]

Morphology Engineering: Molecular Templating

Liquid-crystal alignment, ordered media, or MOF nanocrystals to transfer molecular/medium order parameters into fibers, microgels, or chiral structures.

• Single-step synthesis of shaped polymeric particles using initiated chemical vapor deposition in liquid crystals, 2024, Science Advances. [📄 Paper]
• Solid and Hollow Poly(p-xylylene) Particles SynthesisviaMetal–Organic Framework-Templated Chemical Vapor Polymerization, 2022, Chemistry of Materials. [📄 Paper]
• Surfaces Decorated with Enantiomorphically Pure Polymer Nanohelices via Hierarchical Chirality Transfer across Multiple Length Scales, 2022, Advanced Materials. [📄 Paper]
• Low-dimensional assemblies of metal-organic framework particles and mutually coordinated anisotropy, 2022, Nature Communications. [📄 Paper]
• Templated nanofiber synthesis via chemical vapor polymerization into liquid crystalline films, 2018, Science. [📄 Paper]
• Synthesis of Optically Complex, Porous, and Anisometric Polymeric Microparticles by Templating from Liquid Crystalline Droplets, 2016, Advanced Functional Materials. [📄 Paper]
• Introduction to Optical Methods for Characterizing Liquid Crystals at Interfaces, 2013, Langmuir. [📄 Paper]

In Situ Monitoring and Characterization

In situ imaging and spectroscopy, residual gas analysis, ellipsometry, and QCM-D as high-rate proxy signals for state estimation and closed-loop control.

• Online Bayesian State Estimation for Real-Time Monitoring of Growth Kinetics in Thin Film Synthesis, 2025, Nano Letters. [📄 Paper]
• Fast, In Situ Gas Analysis during Atomic Layer Deposition through Optical Emission Spectroscopy and Non-Negative Matrix Factorization, 2025, ACS Sensors. [📄 Paper]
• Direct monitoring of generated particles in plasma enhanced chemical vapor deposition process using temperature compensating quartz crystal microbalance, 2025, Sensors and Actuators A: Physical. [📄 Paper]
• Evaluating large language model agents for automation of atomic force microscopy, 2025, Nature Communications. [📄 Paper]
• In situ monitoring of industrial-scale chemical vapor deposition using residual gas analysis, 2024, Surfaces and Interfaces. [📄 Paper]
• In Situ UV–Vis–NIR Absorption Spectroscopy and Catalysis, 2024, Chemical Reviews. [📄 Paper]
• Quartz crystal microbalance with dissipation monitoring for studying soft matter at interfaces, 2024, Nature Reviews Methods Primers. [📄 Paper]
• Progress on the in situ imaging of growth dynamics of two-dimensional materials, 2023, Nanoscale. [📄 Paper]
• Optical Fingerprinting of Dynamic Interfacial Reaction Pathways Using Liquid Crystals, 2023, Langmuir. [📄 Paper]
• In-situ monitoring of microwave plasma-enhanced chemical vapour deposition diamond growth on silicon using spectroscopic ellipsometry, 2023, Carbon. [📄 Paper]
• Bayesian decision analysis for optimizing in-line metrology and defect inspection strategy for sustainable semiconductor manufacturing and an empirical study, 2023, Computers & Industrial Engineering. [📄 Paper]
• In Situ Observation of Graphene Growth by Chemical Vapor Deposition Using Ultraviolet Reflection: Implications for Efficient Growth Control in the Industrial Process, 2023, ACS Applied Nano Materials. [📄 Paper]
• In Situ Monitoring of Optical Constants, Conductivity, and Swelling of PEDOT:PSS from Doped to the Fully Neutral State, 2022, Macromolecules. [📄 Paper]
• AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy, 2022, Nature Machine Intelligence. [📄 Paper]
• Molecular Insight into Real-Time Reaction Kinetics of Free Radical Polymerization from the Vapor Phase by In-Situ Mass Spectrometry, 2021, The Journal of Physical Chemistry A. [📄 Paper]
• In situ kinetic studies of CVD graphene growth by reflection spectroscopy, 2021, Chemical Engineering Journal. [📄 Paper]
• Use of optical emission spectroscopy to predict silicon nitride layer properties, 2021, Vacuum. [📄 Paper]

Microstructure Characterization and Machine Learning (Descriptors/Reconstruction/Feature Extraction)

Two-point correlation functions, microstructure reconstruction, and image-driven feature extraction/representation learning to turn pixels into optimizable metrics.

• Application of machine learning to assess the influence of microstructure on twin nucleation in Mg alloys, 2024, npj Computational Materials. [📄 Paper]
• AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy, 2022, Nature Machine Intelligence. [📄 Paper]
• A machine learning approach to map crystal orientation by optical microscopy, 2022, npj Computational Materials. [📄 Paper]
• Comparison of microstructure characterization methods by two-point correlation functions and reconstruction of 3D microstructures using 2D TEM images with high degree of phase clustering, 2021, Materials Characterization. [📄 Paper]
• Fast inverse design of microstructures via generative invariance networks, 2021, Nature Computational Science. [📄 Paper]
• Image-driven discriminative and generative machine learning algorithms for establishing microstructure–processing relationships, 2020, Journal of Applied Physics. [📄 Paper]
• An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction, 2020, Materials Characterization. [📄 Paper]
• Stochastic microstructure characterization and reconstruction via supervised learning, 2016, Acta Materialia. [📄 Paper]
• Characterization and reconstruction of 3D stochastic microstructures via supervised learning, 2016, Journal of Microscopy. [📄 Paper]

Virtual Metrology and Drift Management

Map multimodal sensing and equipment logs to quality/morphology proxy states, with explicit drift and uncertainty handling.

• A multimodal hierarchical learning approach for virtual metrology in semiconductor manufacturing, 2025, Journal of Manufacturing Systems. [📄 Paper]
• Process data-driven machine learning for non-uniformity prediction and virtual metrology in chemical mechanical planarization, 2025, Journal of Intelligent Manufacturing. [📄 Paper]
• A method to benchmark high-dimensional process drift detection, 2025, Journal of Intelligent Manufacturing. [📄 Paper]
• Handling data drift in deep learning-based quality monitoring: evaluating calibration methods using the example of friction stir welding, 2025, Journal of Intelligent Manufacturing. [📄 Paper]
• Virtual metrology in semiconductor manufacturing: Current status and future prospects, 2024, Expert Systems with Applications. [📄 Paper]
• Dynamic sparse PCA: a dimensional reduction method for sensor data in virtual metrology, 2024, Expert Systems with Applications. [📄 Paper]
• Multi-source ensemble method with random source selection for virtual metrology, 2024, Annals of Operations Research. [📄 Paper]
• Development of a virtual metrology system for smart manufacturing: A case study of spandex fiber production, 2023, Computers in Industry. [📄 Paper]
• Handling concept drift in deep learning applications for process monitoring, 2023, Procedia CIRP. [📄 Paper]
• Dynamic transfer soft sensor for concept drift adaptation, 2023, Journal of Process Control. [📄 Paper]
• Virtual metrology of material removal rate using a one-dimensional convolutional neural network-based bidirectional long short-term memory network with attention, 2023, Computers & Industrial Engineering. [📄 Paper]
• Automatic correction of performance drift under acquisition shift in medical image classification, 2023, Nature Communications. [📄 Paper]
• A survey on machine learning for recurring concept drifting data streams, 2023, Expert Systems with Applications. [📄 Paper]
• Optical metrology for digital manufacturing: a review, 2022, The International Journal of Advanced Manufacturing Technology. [📄 Paper]
• Domain-adaptive active learning for cost-effective virtual metrology modeling, 2022, Computers in Industry. [📄 Paper]
• On-line drift compensation for continuous monitoring with arrays of cross-sensitive chemical sensors, 2022, Sensors and Actuators B: Chemical. [📄 Paper]
• Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data, 2022, Scientific Reports. [📄 Paper]
• Concept drift type identification based on multi-sliding windows, 2022, Information Sciences. [📄 Paper]
• Dynamic transfer partial least squares for domain adaptive regression, 2022, Journal of Process Control. [📄 Paper]
• Adaptive virtual metrology method based on Just-in-time reference and particle filter for semiconductor manufacturing, 2021, Measurement. [📄 Paper]
• Machine Learning based CVD Virtual Metrology in Mass Produced Semiconductor Process, 2021, Preprint. [📄 Paper]
• Continuous detection of concept drift in industrial cyber-physical systems using closed loop incremental machine learning, 2021, Discover Artificial Intelligence. [📄 Paper]

Hybrid Modeling and Digital Twins

Mechanistic models, PINNs, gray-box models, and maintainable digital twins, with calibration, cross-tool transfer, and lifecycle management.

• Digital twins for accurate prediction beyond routine operation, 2025, Computers & Chemical Engineering. [📄 Paper]
• Surrogate-based flowsheet model maintenance for Digital Twins, 2025, Digital Chemical Engineering. [📄 Paper]
• When physics meets machine learning: a survey of physics-informed machine learning, 2025, Machine Learning for Computational Science and Engineering. [📄 Paper]
• AI-driven digital twin for autonomous web tension control in Roll-to-Roll manufacturing system, 2025, Scientific Reports. [📄 Paper]
• A high-accuracy deep learning framework for digital twin model development of actual chemical processes, 2025, Engineering Applications of Artificial Intelligence. [📄 Paper]
• Hybrid modeling of first-principles and machine learning: A step-by-step tutorial review for practical implementation, 2025, Computers & Chemical Engineering. [📄 Paper]
• Physics-informed Bayesian optimization suitable for extrapolation of materials growth, 2025, npj Computational Materials. [📄 Paper]
• Physics-informed neural networks in heat transfer-dominated multiphysics systems: A comprehensive review, 2025, Engineering Applications of Artificial Intelligence. [📄 Paper]
• Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine, 2025, npj Digital Medicine. [📄 Paper]
• Towards the validation of manufacturing simulations by means of digital twins: conception, implementation and data acquisition for a composite aircraft moveable manufacturing process, 2025, CEAS Aeronautical Journal. [📄 Paper]
• Advancements and challenges of digital twins in industry, 2024, Nature Computational Science. [📄 Paper]
• Boosting computational thermodynamic analysis of the CVD of SiC coating via machine learning, 2024, Journal of Crystal Growth. [📄 Paper]
• Multiscale Physics-Informed Neural Network Framework to Capture Stochastic Thin-Film Deposition, 2024, The Journal of Physical Chemistry C. [📄 Paper]
• Hybrid modeling for improved extrapolation and transfer learning in the chemical processing industry, 2024, Chemical Engineering Science. [📄 Paper]
• Integrating data assimilation and sparse sensing for updating a digital twin of a semi-industrial furnace, 2024, Proceedings of the Combustion Institute. [📄 Paper]
• Reinforcement Twinning: From digital twins to model-based reinforcement learning, 2024, Journal of Computational Science. [📄 Paper]
• Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process, 2024, npj Computational Materials. [📄 Paper]
• Systematic review of digital twin technology and applications, 2023, Visual Computing for Industry, Biomedicine, and Art. [📄 Paper]
• Development of a surrogate model of an amine scrubbing digital twin using machine learning methods, 2023, Computers & Chemical Engineering. [📄 Paper]
• Chemical reaction-mass transport model of Ga2O3 grown by TEGa in MOCVD and an intelligent system, 2023, Journal of Crystal Growth. [📄 Paper]
• The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering, 2023, Industrial & Engineering Chemistry Research. [📄 Paper]
• Digital twins for electro-physical, chemical, and photonic processes, 2023, CIRP Annals. [📄 Paper]
• A review of unit level digital twin applications in the manufacturing industry, 2023, CIRP Journal of Manufacturing Science and Technology. [📄 Paper]
• Towards live decision-making for service-based production: Integrated process planning and scheduling with Digital Twins and Deep-Q-Learning, 2023, Computers in Industry. [📄 Paper]
• Experience from implementing digital twins for maintenance in industrial processes, 2023, Journal of Intelligent Manufacturing. [📄 Paper]
• Credibility consideration for digital twins in manufacturing, 2023, Manufacturing Letters. [📄 Paper]
• Characterisation and evaluation of identicality for digital twins for the manufacturing domain, 2023, Journal of Manufacturing Systems. [📄 Paper]
• Online validation of digital twins for manufacturing systems, 2023, Computers in Industry. [📄 Paper]
• An innovative kinetic model allowing insight in the moderate temperature chemical vapor deposition of silicon oxynitride films from tris(dimethylsilyl)amine, 2022, Chemical Engineering Journal. [📄 Paper]
• Functional-Hybrid modeling through automated adaptive symbolic regression for interpretable mathematical expressions, 2022, Chemical Engineering Journal. [📄 Paper]
• Bayesian uncertainty quantification for machine-learned models in physics, 2022, Nature Reviews Physics. [📄 Paper]
• Physics-informed machine learning, 2021, Nature Reviews Physics. [📄 Paper]
• High-speed flow field prediction and process parameters optimization in a vertical MOCVD reactor based on a hybrid RSM-KNN model, 2021, International Communications in Heat and Mass Transfer. [📄 Paper]
• Bayesian optimization with adaptive surrogate models for automated experimental design, 2021, npj Computational Materials. [📄 Paper]
• Integration of feedback control and run-to-run control for plasma enhanced atomic layer deposition of hafnium oxide thin films, 2021, Computers & Chemical Engineering. [📄 Paper]
• Numerical study on chemical vapor deposition of ZrC and optimization of deposition uniformity with flexible flow controller, 2021, Materials Today Communications. [📄 Paper]
• Machine-learning-based state estimation and predictive control of nonlinear processes, 2021, Chemical Engineering Research and Design. [📄 Paper]
• Nonlinear Predictive Control of a Bioreactor by Surrogate Model Approximation of Flux Balance Analysis, 2021, Industrial & Engineering Chemistry Research. [📄 Paper]
• A Digital Twin-based Predictive Strategy for Workload Control, 2021, IFAC-PapersOnLine. [📄 Paper]

Inverse Design and Optimization

Bayesian optimization, active learning, multi-fidelity, and multi-objective Pareto learning for expensive experimental search.

• Active oversight and quality control in standard Bayesian optimization for autonomous experiments, 2025, npj Computational Materials. [📄 Paper]
• ECCBO: An inherently safe Bayesian optimization with embedded constraint control for real-time process optimization, 2025, Journal of Process Control. [📄 Paper]
• A self-driving physical vapor deposition system making sample-specific decisions on the fly, 2025, npj Computational Materials. [📄 Paper]
• Best practices for multi-fidelity Bayesian optimization in materials and molecular research, 2025, Nature Computational Science. [📄 Paper]
• Improved multi-objective decision-making in manufacturing processes through uncertainty quantification and robust pareto front modelling, 2025, Scientific Reports. [📄 Paper]
• Multi-objective Bayesian Optimization for Experimental Design in Copolymerization and Revealing Chemical Mechanism of Pareto Fronts, 2025, ACS Applied Engineering Materials. [📄 Paper]
• Physics-informed Bayesian optimization suitable for extrapolation of materials growth, 2025, npj Computational Materials. [📄 Paper]
• Bayesian Optimization for Controlled Chemical Vapor Deposition Growth of WS2, 2024, ACS Applied Materials & Interfaces. [📄 Paper]
• Sequential closed-loop Bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery, 2024, Nature Chemistry. [📄 Paper]
• De novo design of polymer electrolytes using GPT-based and diffusion-based generative models, 2024, npj Computational Materials. [📄 Paper]
• Meta-learning to calibrate Gaussian processes with deep kernels for regression uncertainty estimation, 2024, Neurocomputing. [📄 Paper]
• Bayesian optimization as a valuable tool for sustainable chemical reaction development, 2023, Nature Reviews Methods Primers. [📄 Paper]
• Autonomous experiments using active learning and AI, 2023, Nature Reviews Materials. [📄 Paper]
• Bayesian optimization with active learning of design constraints using an entropy-based approach, 2023, npj Computational Materials. [📄 Paper]
• Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI), 2023, npj Computational Materials. [📄 Paper]
• High-Temperature Polymer Dielectrics Designed Using an Invertible Molecular Graph Generative Model, 2023, Journal of Chemical Information and Modeling. [📄 Paper]
• Guided diffusion for inverse molecular design, 2023, Nature Computational Science. [📄 Paper]
• Bayesian optimization with experimental failure for high-throughput materials growth, 2022, npj Computational Materials. [📄 Paper]
• A multi-fidelity machine learning approach to high throughput materials screening, 2022, npj Computational Materials. [📄 Paper]
• A self-driving laboratory advances the Pareto front for material properties, 2022, Nature Communications. [📄 Paper]
• A Multi-Objective Active Learning Platform and Web App for Reaction Optimization, 2022, Journal of the American Chemical Society. [📄 Paper]
• Domain-adaptive active learning for cost-effective virtual metrology modeling, 2022, Computers in Industry. [📄 Paper]
• Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels, 2022, Scientific Reports. [📄 Paper]
• Bayesian optimization with reference models: A case study in MPC for HVAC central plants, 2021, Computers & Chemical Engineering. [📄 Paper]
• Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains, 2021, npj Computational Materials. [📄 Paper]
• Bayesian optimization with adaptive surrogate models for automated experimental design, 2021, npj Computational Materials. [📄 Paper]
• Flexible automation accelerates materials discovery, 2021, Nature Materials. [📄 Paper]
• Nanoparticle synthesis assisted by machine learning, 2021, Nature Reviews Materials. [📄 Paper]
• Bayesian Machine Learning for Efficient Minimization of Defects in ALD Passivation Layers, 2021, ACS Applied Materials & Interfaces. [📄 Paper]
• Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics, 2021, Machine Learning. [📄 Paper]

Control and Autonomy

Multi-rate architectures, MPC, run-to-run control, reinforcement learning, and safety shielding under drift and delayed labels.

• Active oversight and quality control in standard Bayesian optimization for autonomous experiments, 2025, npj Computational Materials. [📄 Paper]
• ECCBO: An inherently safe Bayesian optimization with embedded constraint control for real-time process optimization, 2025, Journal of Process Control. [📄 Paper]
• Integration of on-line machine learning-based endpoint control and run-to-run control for an atomic layer etching process, 2025, Digital Chemical Engineering. [📄 Paper]
• AI-driven digital twin for autonomous web tension control in Roll-to-Roll manufacturing system, 2025, Scientific Reports. [📄 Paper]
• Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots, 2024, Nature Communications. [📄 Paper]
• Bayesian Optimization for Controlled Chemical Vapor Deposition Growth of WS2, 2024, ACS Applied Materials & Interfaces. [📄 Paper]
• Integrating run-to-run control with feedback control for a spatial atomic layer etching reactor, 2024, Chemical Engineering Research and Design. [📄 Paper]
• Reinforcement Twinning: From digital twins to model-based reinforcement learning, 2024, Journal of Computational Science. [📄 Paper]
• A deep reinforcement learning based algorithm for a distributed precast concrete production scheduling, 2024, International Journal of Production Economics. [📄 Paper]
• Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing, 2024, Additive Manufacturing. [📄 Paper]
• Safe Exploration in Reinforcement Learning by Reachability Analysis over Learned Models, 2024, Lecture Notes in Computer Science. [📄 Paper]
• Almost surely safe exploration and exploitation for deep reinforcement learning with state safety estimation, 2024, Information Sciences. [📄 Paper]
• OCTOPUS: operation control system for task optimization and job parallelization via a user-optimal scheduler, 2024, Nature Communications. [📄 Paper]
• AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning, 2023, Nature Communications. [📄 Paper]
• Controlling Superhydrophobicity on Complex Substrates Based on a Vapor-Phase Sublimation and Deposition Polymerization, 2023, ACS Applied Materials & Interfaces. [📄 Paper]
• Towards live decision-making for service-based production: Integrated process planning and scheduling with Digital Twins and Deep-Q-Learning, 2023, Computers in Industry. [📄 Paper]
• In Situ Observation of Graphene Growth by Chemical Vapor Deposition Using Ultraviolet Reflection: Implications for Efficient Growth Control in the Industrial Process, 2023, ACS Applied Nano Materials. [📄 Paper]
• Multivariable run-to-run control of thermal atomic layer etching of aluminum oxide thin films, 2022, Chemical Engineering Research and Design. [📄 Paper]
• Design Strategies for Structurally Controlled Polymer Surfaces via Cyclophane‐Based CVD Polymerization and Post‐CVD Fabrication, 2022, Advanced Materials. [📄 Paper]
• Machine learning-based run-to-run control of a spatial thermal atomic layer etching reactor, 2022, Computers & Chemical Engineering. [📄 Paper]
• Chance constrained policy optimization for process control and optimization, 2022, Journal of Process Control. [📄 Paper]
• Planning for potential: efficient safe reinforcement learning, 2022, Machine Learning. [📄 Paper]
• Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials, 2021, npj Computational Materials. [📄 Paper]
• Review on model predictive control: an engineering perspective, 2021, The International Journal of Advanced Manufacturing Technology. [📄 Paper]
• Realization of closed-loop optimization of epitaxial titanium nitride thin-film growth via machine learning, 2021, Materials Today Physics. [📄 Paper]
• Bayesian optimization with reference models: A case study in MPC for HVAC central plants, 2021, Computers & Chemical Engineering. [📄 Paper]
• Integration of feedback control and run-to-run control for plasma enhanced atomic layer deposition of hafnium oxide thin films, 2021, Computers & Chemical Engineering. [📄 Paper]
• Numerical study on chemical vapor deposition of ZrC and optimization of deposition uniformity with flexible flow controller, 2021, Materials Today Communications. [📄 Paper]
• Machine-learning-based state estimation and predictive control of nonlinear processes, 2021, Chemical Engineering Research and Design. [📄 Paper]
• Nonlinear Predictive Control of a Bioreactor by Surrogate Model Approximation of Flux Balance Analysis, 2021, Industrial & Engineering Chemistry Research. [📄 Paper]
• Machine learning-based modeling and operation of plasma-enhanced atomic layer deposition of hafnium oxide thin films, 2021, Computers & Chemical Engineering. [📄 Paper]
• A fuzzy hierarchical reinforcement learning based scheduling method for semiconductor wafer manufacturing systems, 2021, Journal of Manufacturing Systems. [📄 Paper]
• Nanoscale control by chemically vapour-deposited polymers, 2020, Nature Reviews Physics. [📄 Paper]

Defect Detection and Quality Control

Visual detection, segmentation, grading, and online QC; emphasizes dataset drift robustness and actionable defect taxonomy.

• Generative AI in industrial machine vision: a review, 2025, Journal of Intelligent Manufacturing. [📄 Paper]
• In-situ real-time defect detection, mitigation and self-supervised adaptation based on visual foundation model for material extrusion additive manufacturing, 2025, Additive Manufacturing. [📄 Paper]
• Rapid identification of defects in doped organic crystalline films via machine learning-enhanced hyperspectral imaging, 2025, Chemical Engineering Journal. [📄 Paper]
• Localization, inspection, and reasoning (LIRA) module for autonomous workflows in self-driving laboratories, 2025, Communications Chemistry. [📄 Paper]
• Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing, 2024, Additive Manufacturing. [📄 Paper]
• Surface defect inspection of industrial products with object detection deep networks: a systematic review, 2024, Artificial Intelligence Review. [📄 Paper]
• Bayesian decision analysis for optimizing in-line metrology and defect inspection strategy for sustainable semiconductor manufacturing and an empirical study, 2023, Computers & Industrial Engineering. [📄 Paper]
• A review of in-situ monitoring and process control system in metal-based laser additive manufacturing, 2023, Journal of Manufacturing Systems. [📄 Paper]
• Optoelectronic perovskite film characterization via machine vision, 2023, Solar Energy. [📄 Paper]
• Optical wafer defect inspection at the 10 nm technology node and beyond, 2022, International Journal of Extreme Manufacturing. [📄 Paper]
• Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks, 2022, Journal of Intelligent Manufacturing. [📄 Paper]
• Bayesian Machine Learning for Efficient Minimization of Defects in ALD Passivation Layers, 2021, ACS Applied Materials & Interfaces. [📄 Paper]
• Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2, 2019, npj Computational Materials. [📄 Paper]
• Image-based manufacturing analytics: Improving the accuracy of an industrial pellet classification system using deep neural networks, 2018, Chemometrics and Intelligent Laboratory Systems. [📄 Paper]

Safety, Governance, and Auditability

Safety for self-driving experiments, auditability, traceable data threads, human-in-the-loop, and risk governance.

• Steering towards safe self-driving laboratories, 2025, Nature Reviews Chemistry. [📄 Paper]
• ECCBO: An inherently safe Bayesian optimization with embedded constraint control for real-time process optimization, 2025, Journal of Process Control. [📄 Paper]
• Probing out-of-distribution generalization in machine learning for materials, 2025, Communications Materials. [📄 Paper]
• Personalized uncertainty quantification in artificial intelligence, 2025, Nature Machine Intelligence. [📄 Paper]
• Multimodal out-of-distribution individual uncertainty quantification enhances binding affinity prediction for polypharmacology, 2025, Nature Machine Intelligence. [📄 Paper]
• Risks of AI scientists: prioritizing safeguarding over autonomy, 2025, Nature Communications. [📄 Paper]
• Towards the validation of manufacturing simulations by means of digital twins: conception, implementation and data acquisition for a composite aircraft moveable manufacturing process, 2025, CEAS Aeronautical Journal. [📄 Paper]
• Human-in-the-loop in smart manufacturing (H-SM): A review and perspective, 2025, Journal of Manufacturing Systems. [📄 Paper]
• Performance metrics to unleash the power of self-driving labs in chemistry and materials science, 2024, Nature Communications. [📄 Paper]
• Safe Exploration in Reinforcement Learning by Reachability Analysis over Learned Models, 2024, Lecture Notes in Computer Science. [📄 Paper]
• Almost surely safe exploration and exploitation for deep reinforcement learning with state safety estimation, 2024, Information Sciences. [📄 Paper]
• Understanding the provenance and quality of methods is essential for responsible reuse of FAIR data, 2024, Nature Medicine. [📄 Paper]
• Robust and privacy-preserving federated learning with distributed additive encryption against poisoning attacks, 2024, Computer Networks. [📄 Paper]
• Federated learning enables privacy-preserving and data-efficient dimension prediction and part qualification across additive manufacturing factories, 2024, Journal of Manufacturing Systems. [📄 Paper]
• Fault diagnosis and self-healing for smart manufacturing: a review, 2023, Journal of Intelligent Manufacturing. [📄 Paper]
• Review of interpretable machine learning for process industries, 2023, Process Safety and Environmental Protection. [📄 Paper]
• Process safety consequence modeling using artificial neural networks for approximating heat exchanger overpressure severity, 2023, Computers & Chemical Engineering. [📄 Paper]
• A systematic review of federated learning: Challenges, aggregation methods, and development tools, 2023, Journal of Network and Computer Applications. [📄 Paper]
• Cross-silo heterogeneous model federated multitask learning, 2023, Knowledge-Based Systems. [📄 Paper]
• Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework, 2022, Reliability Engineering & System Safety. [📄 Paper]
• Planning for potential: efficient safe reinforcement learning, 2022, Machine Learning. [📄 Paper]
• Perceived safety in physical human–robot interaction—A survey, 2022, Robotics and Autonomous Systems. [📄 Paper]
• Governing AI safety through independent audits, 2021, Nature Machine Intelligence. [📄 Paper]
• Explainable artificial intelligence: a comprehensive review, 2021, Artificial Intelligence Review. [📄 Paper]
• Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods, 2021, Machine Learning. [📄 Paper]
• Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics, 2021, Machine Learning. [📄 Paper]

Autonomous Experimentation and Orchestration Infrastructure

Self-driving lab platforms, task orchestration, executable protocol languages, knowledge graphs, and privacy/federated learning infrastructure.

• Transforming the synthesis of carbon nanotubes with machine learning models and automation, 2025, Matter. [📄 Paper]
• Active oversight and quality control in standard Bayesian optimization for autonomous experiments, 2025, npj Computational Materials. [📄 Paper]
• Steering towards safe self-driving laboratories, 2025, Nature Reviews Chemistry. [📄 Paper]
• AI-driven digital twin for autonomous web tension control in Roll-to-Roll manufacturing system, 2025, Scientific Reports. [📄 Paper]
• MLOps best practices, challenges and maturity models: A systematic literature review, 2025, Information and Software Technology. [📄 Paper]
• Localization, inspection, and reasoning (LIRA) module for autonomous workflows in self-driving laboratories, 2025, Communications Chemistry. [📄 Paper]
• A roadmap toward closed-loop autonomous experimentation for engineered nanomaterials, 2025, Nature Chemical Engineering. [📄 Paper]
• IvoryOS: an interoperable web interface for orchestrating Python-based self-driving laboratories, 2025, Nature Communications. [📄 Paper]
• Evaluating large language model agents for automation of atomic force microscopy, 2025, Nature Communications. [📄 Paper]
• A digital laboratory with a modular measurement system and standardized data format, 2025, Digital Discovery. [📄 Paper]
• Probing the limitations of multimodal language models for chemistry and materials research, 2025, Nature Computational Science. [📄 Paper]
• Autonomous Synthesis and Inverse Design of Electrochromic Polymers with High Efficiency and Accuracy, 2025, Journal of the American Chemical Society. [📄 Paper]
• Performance metrics to unleash the power of self-driving labs in chemistry and materials science, 2024, Nature Communications. [📄 Paper]
• Self-Driving Laboratories for Chemistry and Materials Science, 2024, Chemical Reviews. [📄 Paper]
• ChemOS 2.0: An orchestration architecture for chemical self-driving laboratories, 2024, Matter. [📄 Paper]
• OCTOPUS: operation control system for task optimization and job parallelization via a user-optimal scheduler, 2024, Nature Communications. [📄 Paper]
• A dynamic knowledge graph approach to distributed self-driving laboratories, 2024, Nature Communications. [📄 Paper]
• Augmenting large language models with chemistry tools, 2024, Nature Machine Intelligence. [📄 Paper]
• Universal chemical programming language for robotic synthesis repeatability, 2024, Nature Synthesis. [📄 Paper]
• An integrated self-optimizing programmable chemical synthesis and reaction engine, 2024, Nature Communications. [📄 Paper]
• AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning, 2023, Nature Communications. [📄 Paper]
• Autonomous experiments using active learning and AI, 2023, Nature Reviews Materials. [📄 Paper]
• The rise of self-driving labs in chemical and materials sciences, 2023, Nature Synthesis. [📄 Paper]
• Large language models for chemistry robotics, 2023, Autonomous Robots. [📄 Paper]
• The future of chemistry is language, 2023, Nature Reviews Chemistry. [📄 Paper]
• Community Resource for Innovation in Polymer Technology (CRIPT): A Scalable Polymer Material Data Structure, 2023, ACS Central Science. [📄 Paper]
• Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials, 2021, npj Computational Materials. [📄 Paper]
• Flexible automation accelerates materials discovery, 2021, Nature Materials. [📄 Paper]
• Automated Experimentation Powers Data Science in Chemistry, 2021, Accounts of Chemical Research. [📄 Paper]
• Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2, 2019, npj Computational Materials. [📄 Paper]