Conference

MRS Fall 2024

CalendarDate & Time
  • December 1st-6th, 2024
LocationLocation
  • Boston, Massachusetts

Schrödinger is excited to be participating in the MRS Fall 2024 conference taking place on December 1st – 6th in Boston, Massachusetts. Join us for presentations by Schrödinger scientists on Dec 4th and 5th. Additionally, attend a presentation on Dec 3rd by Panasonic, co-authored by Schrödinger, titled “Discovering Low-Viscosity Molecules Using an Integrated Physics-Based Modeling, High-Throughput Screening, and Active Learning Approach (2)— Screening from PubChem Database.”

 

icon time DEC 3 | 8:00 PM
icon location Hynes, Level 1, Hall A
Discovering Low-Viscosity Molecules Using an Integrated Physics-Based Modeling, High-Throughput Screening, and Active Learning Approach (2)— Screening from PubChem Database

Presenters:
Nobuyuki Matsuzawa, Hiroyuki Maeshima, Tatsuhito Ando, Atif Afzal, Benjamin Coscia, Andrea Browning, Mathew Halls, Karl Leswing, Tsuguo Morisato

Schrödinger collaborated with Panasonic on this presentation

icon time DEC 4 | 3:45 PM
icon location Hynes, Level 3, Ballroom C
Discovering Low-Viscosity Molecules Using an Integrated Physics-Based Modeling, High-Throughput Screening and Active Learning Approach (1)— Screening from the GDB Database

Speaker:
Atif Afzal, Principal Scientist

Abstract:
The discovery of low-viscosity molecules is crucial for the development of next-generation batteries and capacitors. Large molecular libraries available in the literature provide a valuable resource for identifying promising candidates. In this study, we utilized the GDB database1, one of the largest repositories of small molecules, to identify low-viscosity molecules. We employed and benchmarked molecular dynamics methods to accurately compute the dynamic properties without the need for synthesis or empirical testing, validating our calculations against experimental data. However, the number of molecules of interest from the GDB database is too large (several hundreds of thousands), making it impractical to identify promising candidates using purely physics-based models due to computational costs. Therefore, we implemented advanced machine learning (ML) techniques and smart selection approaches to dramatically reduce the number of physics-based calculations needed. Physics-based simulations of viscosity included both Green-Kubo and Einstein-Helfand approaches allowing for robust calculation across the selected molecules. By employing an active learning approach, we optimized the selection of molecules, enhancing the efficiency of the ML model while targeting low-viscosity candidates. Additionally, we computed the boiling points (BP) of the molecules using ML models trained on experimental BP data. As a result, we identified more than 100 molecules with viscosities less than 0.35 cP and BP above 80°C. We demonstrate that by integrating accurate physics-based models with advanced ML techniques, we can effectively identify top molecular candidates while significantly reducing computational costs.

icon time DEC 5 | 11:15 AM
icon location Sheraton, Second Floor, Constitution B
Prediction of aqueous and non-aqueous solubility using machine learning

Speaker:
Lihua Chen, Senior Scientist

Abstract:
Solubility, the capacity of a solute to dissolve in a solvent, forming a solution, is a crucial design parameter across various materials and life science applications. Due to the high cost of experimental measurements, we have developed quantitative structure-property relationship (QSPR) models to rapidly and accurately predict aqueous solubility in water and non-aqueous solubility in organic solvents. For this purpose, we gathered 14,485 room temperature aqueous solubility data points and 45,313 temperature-dependent non-aqueous solubility data points from literature and open-source databases. Additionally, we incorporated advanced cheminformatics-based, graph-based, and physics-based descriptors computed through classical molecular dynamics to optimize machine learning performance. These models can significantly streamline molecular discovery by providing rapid, accurate solubility predictions, reducing the need for costly experiments, and accelerating the identification and optimization of promising candidates.