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RESEARCH PAPER · Scientific Data · 2024

Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset

Aniket Chitre, Robert C. M. Querimit, Simon D. Rihm, Dogancan Karan, Benchuan Zhu, Ke Wang, Long Wang, Kedar Hippalgaonkar, Alexei A. Lapkin

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Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations DatasetOriginalEfficient discovery of new medicine formulations using a semi-self-driven robotic formulator9Opentrons for automated and high-throughput viscometry7Machine learning-guided space-filling designs for high throughput liquid formulation development8Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures18Prediction of Shampoo Formulation Phase Stability Using Large Language Models0An AI-based Process Prediction Model for SMEs in the Cosmetics Manufacturing Industry0Artificial intelligence in colloid and interface science: Current research, challenges and future directions10Printed RFID systems for sustainable IoT: synergistic advances in conductive inks, antenna architectures, and scalable manufacturing7Data-Efficient Methods for Determining Flory–Huggins χ Parameters in Multicomponent Polymer Formulations6A Deep Neural Network Framework for Prediction of Steady-Shear Flow Behavior in Multicomponent Biopolymer–Surfactant Systems0New and Future Technologies for the Creation of Cosmetics2Qubots: open-source modular laboratory robots as building blocks for flexible assembly of self-driving labs0The Future of Polymer Science: Artificial Intelligence and Beyond0A high-throughput cost-effective platform for monitoring viscoelastic transitions in hydrocolloids0Advances in the applications of machine learning for cosmetic formulation development1Improving Fidelity and Diversity in Chemical Language Transformers for Inverse Molecular Design2ResNet-KAN for Small-Sample Chemical Formulation Prediction: Hybrid Architecture and Interpretability Analysis0A High-Throughput Automation Platform for Accelerated AAV Stability Optimization0From Black-Box Prediction to White-Box Understanding: A Model-Zoo Comparison of Machine Learning Models for Predicting Rheological Properties of Emulgel Formulations0Chemical Language Transformers for the Inverse Design of Novel Surfactants0Using AI in Food Hydrocolloid Research: Evidence Quality, Mechanistic Explainability, and Real-Food Validation0AI-Guided Inverse Design of Hydrocolloid-Based Food Systems: From Molecular Descriptors to Process–Structure–Function Validation0Graph-to-Graph Inverse Design of Surfactants under Multiobjective and Structural Constraints0

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  1. Efficient discovery of new medicine formulations using a semi-self-driven robotic formulator

    Digital Discovery · 2025-01-01

    Helena Ros, Youssef Abdalla, Michael Thomas Cook, David Shorthouse

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  2. Opentrons for automated and high-throughput viscometry

    Digital Discovery · 2025-01-01

    Beatrice W. Soh, Aniket Chitre, Shu Zheng Tan, Yuhan Wang, Yinqi Yi, Wendy Soh, Kedar Hippalgaonkar, David Ian Wilson

    Read original paper ↗ ·

  3. Machine learning-guided space-filling designs for high throughput liquid formulation development

    Computers & Chemical Engineering · 2025-01-18

    Aniket Chitre, Daria Semochkina, David C. Woods, Alexei A. Lapkin

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  4. Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures

    npj Computational Materials · 2025-03-17

    Alex K. Chew, Mohammad Atif Faiz Afzal, Zachary Kaplan, Eric M. Collins, Suraj Gattani, Mayank Misra, Anand R. Chandrasekaran, Karl Leswing, Mathew David Halls

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  5. Prediction of Shampoo Formulation Phase Stability Using Large Language Models

    Cosmetics · 2025-07-10

    Erwan Bigan, Stéphane Dufour

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  6. An AI-based Process Prediction Model for SMEs in the Cosmetics Manufacturing Industry

    The Journal of Korean Institute of Information Technology · 2025-07-31

    Young‐Sik Kang, JinWoo Jung, Mingxin Guo, Gyeonghwan Jang, Hanjun Lee

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  7. Artificial intelligence in colloid and interface science: Current research, challenges and future directions

    Current Opinion in Colloid & Interface Science · 2025-09-15

    Simha Sridharan, Tom Bailey, Agnese Marcato, Elena Simone, Nicholas James Watson

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  8. Printed RFID systems for sustainable IoT: synergistic advances in conductive inks, antenna architectures, and scalable manufacturing

    Frontiers in Electronics · 2025-10-23

    Xintai Wang, Maksim Kuznetcov, Wenfeng Jiang, Zhongyu Tang, Zhangqian Wei, Aili Zhang, Naixu Wei, Xiaoying Li

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  9. Data-Efficient Methods for Determining Flory–Huggins χ Parameters in Multicomponent Polymer Formulations

    Macromolecules · 2025-11-12

    Timothy Quah, Charles Li, Noah P. Wamble, Elizabeth A. Murphy, Christopher M. Bates, Craig J. Hawker, Benny D. Freeman, M. Scott Shell, Glenn H. Fredrickson

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  10. A Deep Neural Network Framework for Prediction of Steady-Shear Flow Behavior in Multicomponent Biopolymer–Surfactant Systems

    SSRN Electronic Journal · 2026-01-01

    Foluso Akin-Ige, Maria Calderon Vaca, Kevin Padron, Yelena Yesha, Samiul Amin

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  11. New and Future Technologies for the Creation of Cosmetics

    AAPS introductions in the pharmaceutical sciences · 2026-01-01

    Perry Romanowski

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  12. Qubots: open-source modular laboratory robots as building blocks for flexible assembly of self-driving labs

    Digital Discovery · 2026-01-01

    Pablo Quijano Velasco, Chang Jie Leong, Eleen H. H. Koay, Kedar Hippalgaonkar, Jayce Jian Wei Cheng

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  13. The Future of Polymer Science: Artificial Intelligence and Beyond

    Engineering materials · 2026-01-01

    Akshita, Devang Shukla, Rohit Verma, Tejendra Kumar Gupta, Deepshikha Gupta, Ravi Kant Choubey, Sunil Kumar, Vijay Kumar

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  14. A high-throughput cost-effective platform for monitoring viscoelastic transitions in hydrocolloids

    Journal of Food Engineering · 2026-02-20

    Adar Fridman, Serafim Bakalis, Raghavendra Selvan, Remko M. Boom

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  15. Advances in the applications of machine learning for cosmetic formulation development

    Chemical Papers · 2026-02-24

    Wen Jiang, Lingyun Yao, Qingran Meng, Qianjie Zhang

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  16. Improving Fidelity and Diversity in Chemical Language Transformers for Inverse Molecular Design

    Journal of Chemical Information and Modeling · 2026-03-04

    Alexander W. Rogers, Ruediger Zillmer, Amanda Lane, Adam Kowalski, Dongda Zhang

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  17. ResNet-KAN for Small-Sample Chemical Formulation Prediction: Hybrid Architecture and Interpretability Analysis

    Precision Chemistry · 2026-04-24

    Yaochen Zhang, Yuqi Wang, Xin Ding, Liang Y. Zhao, Le Wu

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  18. A High-Throughput Automation Platform for Accelerated AAV Stability Optimization

    Pharmaceutics · 2026-05-16

    Shuai Li, Xiaoyan Wang, Li Zhi, Mohammed Shameem, Dingjiang Liu

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  19. From Black-Box Prediction to White-Box Understanding: A Model-Zoo Comparison of Machine Learning Models for Predicting Rheological Properties of Emulgel Formulations

    Research Square · 2026-06-02

    Rahul Dhaigude, Sreenath Sriram, Sonal Solanki, Nikunj Solanki, Yamsani Shravan Kumar, Sumit Sharma, Rashmi Rathi Upadhyay

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  20. Chemical Language Transformers for the Inverse Design of Novel Surfactants

    Systems and Control Transactions · 2026-06-19

    Alexander W. Rogers, Ruediger Zillmer, Amanda Lane, Adam Kowalski, Dongda Zhang

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  21. Using AI in Food Hydrocolloid Research: Evidence Quality, Mechanistic Explainability, and Real-Food Validation

    Food Reviews International · 2026-08-06

    Adnan Amin, Wajid Zaman

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  22. AI-Guided Inverse Design of Hydrocolloid-Based Food Systems: From Molecular Descriptors to Process–Structure–Function Validation

    Food Reviews International · 2026-08-14

    Wajid Zaman, Asma Ayaz

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  23. Graph-to-Graph Inverse Design of Surfactants under Multiobjective and Structural Constraints

    Journal of Chemical Information and Modeling · 2026-09-25

    Alexander W. Rogers, Ruediger Zillmer, Amanda Lane, Adam Kowalski, Dongda Zhang

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