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17/03/2021 – AI3SD Winter Seminar Series: Property Prediction
17th March 2021 @ 2:00 pm - 3:45 pmFree
Eventbrite Link: https://ai3sd-winter-series-170321.eventbrite.co.uk
This seminar forms part of the AI3SD Online Seminar Series that will run across the winter (from November 2020 to April 2021). This seminar will be run via zoom, when you register on Eventbrite you will receive a zoom registration email alongside your standard Eventbrite registration email. Where speakers have given permission to be recorded, their talks will be made available on our AI3SD YouTube Channel. The theme for this seminar is Property Prediction.
- 14:00-14:45: Generating a Machine-Learned Equation of State for Fluid Properties – Professor Erich Müller (Imperial College London)
- 14:45-15:00: Coffee Break
- 15:00-15:45: Machine Learning with Causality: Solubility Prediction in Organic Solvents and Water – Dr Bao Nguyen (University of Leeds)
Abstracts & Speaker Bios
- Generating a Machine-Learned Equation of State for Fluid Properties – Professor Erich Müller: Equations of state (EoS) for fluids have been a staple of engineering design and practice for over a century. Available EoS are based on the fitting of a closed-form analytical expression to suitable experimental thermophysical data of fluids. The mathematical structure and the underlying physical model significantly restrain the applicability and accuracy of the resulting EoS. This contribution explores the issues surrounding the substitution of machine-learned models for analytical EoS. In particular, we describe, as a proof of concept, the effectiveness of a machine-learned model to replicate the statistical associating fluid theory (SAFT-VR Mie) EoS for pure fluids. To quantify the effectiveness of machine-learning techniques, a large set of pseudodata is obtained from the EoS and used to train the machine-learning models. We employ artificial neural networks and Gaussian process regression to correlate and predict thermodynamic properties such as critical pressure and temperature, vapor pressures, and densities of pure model fluids; these are performed on the basis of molecular descriptors. The comparisons between the machine- learned EoS and the surrogate data set suggest that the proposed approach shows promise as a viable technique for the correlation and prediction of thermophysical properties of fluids. This work opens a pathway for employing classical molecular simulations with classical force fields as feeder of pseudo-data of fluids in the search for ML physical property prediction.
Bio: Erich A Müller currently works as a Professor of Thermodynamics at the Department of Chemical Engineering, Imperial College London. Erich does research in Molecular simulation, Chemical Engineering and Thermodynamics.
- Machine Learning with Causality: Solubility Prediction in Organic Solvents and Water – Dr Bao Nguyen: Solubility prediction remains a critical challenge in drug development, synthetic route and chemical process design, extraction and crystallisation. Here we report a successful approach to solubility prediction in organic solvents and water using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry. Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions which are independent of the applied machine learning method. These models gave significantly more accurate predictions compared to benchmarked open-access and commercial tools, achieving accuracy close to the expected level of noise in training data (LogS ± 0.7). Finally, they reproduced physicochemical relationship between solubility and molecular properties in different solvents, which led to rational approaches to improve the accuracy of each models.
Bio: Dr Bao Nguyen is a Lecturer in Physical Organic Chemistry at University of Leeds, where he has been from September 2012. He actively collaborates with colleagues from both the School of Chemistry and School of Chemical and Process Engineering to address current challenges in process chemistry. He is a core member of the Institute of Process Research and Development (iPRD), a flagship institute set up by the Leeds Transformation Fund. Dr Nguyen did his PhD in Organic Chemistry at the University of Oxford, under the supervision of Dr John M. Brown FRS. He then moved to Dr Michael C. Willis’ group, where he developed the first Pd-catalysed coupling reaction employing sulfur dioxide by suppressing catalyst deactivation. Afterward, he joined Imperial College London, working in Dr King Kuok Hii’s group to delineate the nature of the palladium species in different catalytic reactions and developing separation methods for these species. He was awarded his first independent position as a Ramsay Memorial Fellow at Department of Chemistry, Imperial College London.