Schrödinger User Group Meeting –Materials Science Japan 2024
AbstractCombined Physics-Based and Machine Learning
Approaches in the Design of Complex Materials
Anand Chandrasekaran
Product Manager Materials Science Informatics, Schrödinger
The simulation of materials properties using physics-based approaches, such as density
functional theory (DFT) and molecular dynamics (MD), has long been successful in providing
insights into structure-property relationships and subsequently aiding in the design of novel
materials. More recently, machine learning (ML) has been used extensively in conjunction withphysics-based modeling techniques to greatly accelerate materials innovation. The accuracy andgeneralizability of physics-based modeling improves the performance of ML models and enables
them to be used effectively even in small-data regimes. Conversely, the speed and flexibility of
ML help bridge the time- and spatial- scale limitations of physics-based models, creating asynergistic approach that optimizes both predictive accuracy and computational efficiency. In thistalk, we demonstrate the application of this combined approach in designing materials andformulations across diverse applications, from battery electrolytes and fuel mixtures tothermoplastics and OLED devices. For instance, we demonstrate how DFT descriptors greatly
improve the accuracy of ML models for optoelectronic molecules and battery electrolytes while
descriptors from MD simulations can lead to better models for viscosity of organic molecules. We
use Schrodinger’s automated Formulation ML solution, which takes into account both chemistryand composition, to train ML models for solubility of APIs in binary solvents and for the predictionof motor octane number of hydrocarbons. Additionally, we highlight advances in our machinelearning force field technology (QRNN) and its application in modeling the bulk properties ofinorganic cathode coating materials.

Automated Identification of Chemical Reaction
Products with Nanoreactor
Pavel A. Dub
Product Manager Catalysis and Reactivity, Schrödinger
Predicting the outcomes of chemical reactions is considered the Holy Grail of computational
chemistry. To advance this capability, various computational methods for automated reaction
space exploration are continually being developed. These methods generally fall into three maincategories: graph enumeration of products and reaction coordinates, reaction template methods,
and accelerated ab initio molecular dynamics. Each of these strategies faces specific challenges:
graph-based methods suffer from combinatorial explosion, reaction template methods lack
generalization, and accelerated ab initio molecular dynamics are hindered by steep numericalscaling.
Schrödinger’s Nanoreactor introduces a novel approach for the automated digital identification ofchemical (small molecules) reaction products. The approach is based on automated potential
energy surface sampling through GFN2-xTB metadynamics, landscape refinement and density
functional theory-based sorting. By analyzing products based on their quantum energies andapplying chemical intuition, a user can infer potential reaction products and intermediates.
Additionally in this presentation, we will present preliminary results demonstrating the use of theNanoreactor machinery for the direct prediction of the most kinetically probable products ofelementary chemical reactions. Sequentially, this new feature of Nanoreactor could enable thedirect identification of a minimum energy reaction channel or an ensemble of low-energy reaction
pathways for a complex multi-stage reaction. This advancement paves the way for a conceptuallynew, ultrafast strategy for the direct prediction of chemical reactions.

Polymers and Formulations in Schrödinger Materials
Science Suite: From electronics to pharmaceuticals,
new properties bring new applications
Andrea Browning
Director - Polymers and Soft Matter, Schrödinger
For materials in applications such as batteries, electronic packaging, and pharmaceuticalformulations, a series of properties must be optimized in order to make a successful product. The
computational solutions available in the Schrödinger Materials Science Suite have been used
across these applications to develop new materials through computational screening of target
properties. With the latest releases in 2024, new property workflows are available to extend thedesign considerations for new materials; such as thermal conductivity and the electrode film
formation producing a solid electrolyte interphase (SEI) layer. The optimization of constituent
proportions in an active formulation is further enhanced with the inclusion of automatic particle
identification in the Coarse Grained Force Field Builder, allowing for the creation of suggested
particles for use in the automatic parameterization workflow. This presentation will describe these
new solution workflows and show how they are enabling improved design for polymers andcomplex formulations.