Alexei A. Lapkin

15 papers Journal 15
YearRankTypeTitle / Venue / Authors
2026 J jnl
Automated generation of mechanistic models for chemical process digital twins using reinforcement learning part II: Compartmentalization and learning-based recalibration.
Comput. Chem. Eng.
Jan-Frederic Laub, Jiyizhe Zhang, Mathis Heyer, Alexei A. Lapkin
2025 J jnl
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions.
CoRR
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price, Victor Posligua, Stephan Roche, O. Anatole von Lilienfeld, Konstantin S. Novoselov, Xavier Bresson, Gianmarco Mengaldo, Xudong Chen, Terence J. O'Kane, Emily R. Lines, Matthew J. Allen, Amandine E. Debus, Clayton Miller, Jiayu Zhou, Hiroko H. Dodge, David Rousseau, Andrey Ustyuzhanin, Ziyun Yan, Mario Lanza, Fabio Sciarrino, Ryo Yoshida, Zhidong Leong, Teck Leong Tan, Qianxiao Li, Adil Kabylda, Igor Poltavsky, Alexandre Tkatchenko, Sherif Abdulkader Tawfik, Prathami Divakar Kamath, Théo Jaffrelot Inizan, Kristin A. Persson, Bryant Y. Li, Vir Karan, Chenru Duan, Haojun Jia, Qiyuan Zhao, Hiroyuki Hayashi, Atsuto Seko, Isao Tanaka, Omar M. Yaghi, Tim Gould, Bun Chan, Stefan Vuckovic, Tianbo Li, Min Lin, Zehcen Tang, Yang Li, Yong Xu, Amrita Joshi, Xiaonan Wang, Leonard W. T. Ng, Sergei V. Kalinin, Mahshid Ahmadi, Jiyizhe Zhang, Shuyuan Zhang, Alexei A. Lapkin, Ming Xiao, Zhe Wu, Kedar Hippalgaonkar, Limsoon Wong, Lorenzo Bastonero, Nicola Marzari, Dorye Luis Esteras Cordoba, Andrei Tomut, Alba Quinones Andrade, Jose-Hugo Garcia
2025 J jnl
Automated generation of mechanistic models for chemical process digital twins using reinforcement learning part I: Conceptual framework and equation generation.
Comput. Chem. Eng.
Mathis Heyer, Jiyizhe Zhang, Naoto Sugisawa, Jan-Frederic Laub, Alexei A. Lapkin
2025 J jnl
Machine learning-guided space-filling designs for high throughput liquid formulation development.
Comput. Chem. Eng.
Aniket Chitre, Daria Semochkina, David C. Woods, Alexei A. Lapkin
2025 J jnl
Multi-objective reaction optimization under uncertainties using expected quantile improvement.
Comput. Chem. Eng.
Jiyizhe Zhang, Daria Semochkina, Naoto Sugisawa, David C. Woods, Alexei A. Lapkin
2024 J jnl
ORDerly: Data Sets and Benchmarks for Chemical Reaction Data.
J. Chem. Inf. Model.
Daniel S. Wigh, Joe Arrowsmith, Alexander Pomberger, Kobi C. Felton, Alexei A. Lapkin
2023 J jnl
Boosting autonomous process design and intensification with formalized domain knowledge.
Comput. Chem. Eng.
Jan Raphael Seidenberg, Ahmad A. Khan, Alexei A. Lapkin
2023 J jnl
Computer-aided design of formulated products: A bridge design of experiments for ingredient selection.
Comput. Chem. Eng.
Liwei Cao, Danilo Russo, Emily Matthews, Alexei A. Lapkin, David Woods
2023 J jnl
Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction.
CoRR
Jan G. Rittig, Kobi C. Felton, Alexei A. Lapkin, Alexander Mitsos
2023 J jnl
Heuristic optimisation of multi-task dynamic architecture neural network (DAN2).
Neural Comput. Appl.
Sushen Zhang, Vassilios S. Vassiliadis, Zhimian Hao, Liwei Cao, Alexei A. Lapkin
2020 J jnl
Searching for optimal process routes: A reinforcement learning approach.
Comput. Chem. Eng.
Ahmad Khan, Alexei A. Lapkin
2019 J jnl
Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filtering.
CoRR
Carl Poelking, Yehia Amar, Alexei A. Lapkin, Lucy J. Colwell
2018 J jnl
Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm.
J. Glob. Optim.
Eric Bradford, Artur M. Schweidtmann, Alexei A. Lapkin
2018 J jnl
Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm.
J. Glob. Optim.
Eric Bradford, Artur M. Schweidtmann, Alexei A. Lapkin
2017 J jnl
A possible extension to the RInChI as a means of providing machine readable process data.
J. Cheminformatics
Philipp-Maximilian Jacob, Tian Lan, Jonathan M. Goodman, Alexei A. Lapkin