I am now a fifth-year Ph.D. student in Chemical Engineering at University of Masschusetts Amherst, advised by Prof. Peng Bai. Previously, I earned my Master of Science in Chemical Enginnering from Oregon State University, where I was advised by Prof. Cory M. Simon.

My research interest focus on leveraging machine learning and Bayesian optimization to efficiently guide the search for porous materials with target properties. Outside of research, I enjoy playing vedio games, board games, and cooking.

🔥 News

• 2023.06:  🎉🎉 Passed qualification exam.
• 2023.01:  🎉🎉 Joined Bai group.
• 2022.09:  🎉🎉 Officially Head to UMass Amherst.

📝 Publications

Cover of JCP

ACSML TOC

Y. Liu, E. Wu, P. Yang, A. Sun, S. Maji, W. Fan, P. Bai. Assessment of the Synthetic Feasibility of Hypothetical Zeolite-like Materials Based on ZeoNet. ACS Materials Lett. 8, 1695–1701 (2026).

Citation

JMCA TOC

Y. Liu, P. Yang, G. Perez, A. Sun, W. Fan, S. Maji, and P. Bai. Representation Learning for Long-Chain Hydrocarbon Adsorption in Zeolites. J. Mater. Chem. A 13, 39229-39242 (2025).

Citation

Code Repo

JPCC TOC

S. Sharma=, P. Yang=, Y. Liu, K. Rossi, P. Bai, M. S. Rigutto, E. Zuidema, U. Agarwal, R. Baur, S. Calero, D. Dubbeldam, and T. J.H. Vlugt. Machine Learning-Based Predictions of Henry Coefficients for Long-Chain Alkanes in One-Dimensional Zeolites: Application to Hydroisomerization. J. Phys. Chem. C 129, 18234-18249 (2025).

Citation

JCP TOC

P. Yang, E. A. Henle, X. Z. Fern, and C. Simon. Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels. J. Chem. Phys. 157, 034102 (2022).

note: on the cover; JCP Editors’ Choice 2022; covered by Scilight “The buzz about machine learning and pesticide toxicity”.

Citation

Code Repo

🎖 Honors and Awards

• 2025 – 2026, PPG Foundation Fellowship.
• 2020 – 2022, Oregon State University Continued Success Scholarship.

📖 Educations

• Doctor of Philosophy in Chemical Engineering (2022.09 – present) | University of Massachusetts Amherst | Amherst, MA, USA.
• Graduate Certificate in Statistical and Computational Data Science (Awarded on 2025.09.01) | University of Masschusetts Amherst | Amherst, MA, USA.
• Master of Science in Chemical Engineering (2019.09 – 2022.06) | Oregon State University | Corvallis, OR, USA.
• Bachelor of Engineering in Chemical Engineering and Technology (2013.08 – 2017.07) | East China University of Science and Technology | Shanghai, China.

💬 Conferences

• P. Yang, Y. Liu, D. Dubbeldam, T. Vlugt, S. Maji, and P. Bai, “Machine learning of shape selective separation of long-chain hydrocarbon isomers in zeolites” (Talk), 2026 AIChE Annual Meeting, Symposium on ‘Molecular and Data Science Modeling of Adsorption,’ Minneapolis, MN (11/2026).
• M. Balogun, P. Yang, T. Whynot, S. Aiwekhoe, P. Bai, and W. Fan. “Machine learning–assisted rapid and green synthesis of porous materials” (Talk), 2026 AIChE Annual Meeting, Symposium on ‘Synthesis and Application of Inorganic Materials I: Synthesis,’ Minneapolis, MN (11/2026).
• Y. Liu, P. Yang, A. Karande, and P. Bai, “Machine-learning prediction of adsorption in nanoporous zeolites” (Talk), 2026 Southeastern Regional Meeting of the ACS, Symposium on ‘AI for Materials and Molecules,’ Memphis, TN (11/2026).
• P. Yang, Y. Liu, S. Sharma, D. Dubbeldam, T. Vlugt, S. Maji, and P. Bai, “Host/guest representations for long-chain hydrocarbon adsorption in zeolites” (Talk), 2026 Computational Materials Science and Engineering Gordon Research Seminar, Newry, ME (08/2026).
• P. Yang, Y. Liu, S. Sharma, D. Dubbeldam, T. Vlugt, S. Maji, and P. Bai, “Host/guest representations for long-chain hydrocarbon adsorption in zeolites” (Poster), 2026 Computational Materials Science and Engineering Gordon Research Conference, Newry, ME (08/2026).
• Y. Liu, E Wu, P. Yang, A. Sun, W. Fan, S. Maji, and P. Bai, “Assessing the synthetic feasibility of zeolite-like materials using zeonet” (Talk), 2025 AIChE Annual Meeting, Symposium on ‘Machine Learning for Soft and Hard Materials II: Hard Matter and Methods,’ Boston, MA (11/2025).
• Y. Liu, P. Yang, A. Sun, Z. Cheng, G. Pérez, W. Fan, S. Maji, and P. Bai, “Representation learning for predicting shape selectivity in nanoporous zeolites” (Talk), The 29th North American Catalysis Society Meeting, Symposium on ‘AI - Using AI to Navigate Large Catalyst Design Spaces,’ Atlanta, GA (06/2025).
• Y. Liu, P. Yang, G. Pérez, A. Sun, W. Fan, S. Maji, and P. Bai, “Representation learning for all-silica zeolites: model representations, transfer learning, and multi-task learning” (Talk), 2024 AIChE Annual Meeting, Symposium on ‘Machine Learning for Soft and Hard Materials II,’ San Diego, CA (10/2024).
• Y. Liu, G. Pérez, Z. Cheng, A. Sun, P. Yang, S. Hoover, W. Fan, S. Maji, and P. Bai, “Zeonet: 3D convolutional neural networks for predicting long-Chain hydrocarbon adsorption in nanoporous zeolites” (Poster), Foundations of Molecular Modeling and Simulation, Snowbird, UT (07/2024).
• P. Yang, E.. A. Henle, X. Z. Fern, and C. Simon, “Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels” (Talk), 2022 AIChE Annual Meeting, Symposium on ‘Applications of Data Science in Molecular Sciences II,’ Phoenix, AZ (11/2022).

🏫 Teaching

• At University of Massachusetts Amherst
  1. Fall 2025, ChE 621 Thermodynamics I, with Prof. Peng Bai.
  2. Fall 2025, ChE 625 Reactor Design, with Prof. Wei Fan.
  3. Spring 2025, ChE 231 Math Modeling, with Prof. Peng Bai.
  4. Fall 2024, ChE 446 Process Control, with Dr. Anna Marie LaChance.
  5. Spring 2024, ChE 231 Math Modeling, with Prof. Peng Bai.
• At Oregon State University
  1. Spring 2022 ENGR 103 Engineering Computation and Algorithmic Thinking, with Paula De Szoeke.
  2. Winter 2022, CHE 333 Transport Phenomena III, with Prof. Líney Árnadóttir.
  3. Fall 2021, CHE 599 Introduction to Data Science to Engineers, with Prof. Cory M. Simon.

🤝 Academic Service

• 2026, Lecture Content Reviewer, “AI for Accelerated Materials Science,” Climate Change AI Virtual Summer School.

💻 Internships