From Molecules to Materials Applications
Date & Time - September 11th – October 8th, 2024
- 18:00 IST
Location - Virtual
Molecular modeling is a powerful computational technique widely used in materials science to predict and understand the properties and behavior of materials at the molecular level. By simulating the interactions between atoms and molecules, researchers can explore the structural, mechanical, electronic, and thermal properties of various materials and gain a deeper understanding. Molecular modeling encompasses a range of methods, including molecular dynamics, quantum mechanics, and coarse grained simulations, each providing unique insights into material properties and guiding experimental efforts.
The integration of molecular modeling into materials science can accelerate the development of advanced materials for applications in pharmaceuticals, Fast Moving Consumer Goods, electronics, energy storage, catalysis, and more.
This webinar series “From molecules to Materials Applications” will delve into molecular modeling techniques and their transformative impact on Materials Science research using the Schrödinger Materials Science tools.
SEPT 11 | 18:00 IST Molecular Modeling: A Key to Solving Real-Life Challenges in Pharma Formulations
Speaker:
Sudharsan Pandiyan, Principal Scientist II, Schrödinger
Abstract:
The demand for innovative drug delivery methods has driven researchers to explore the intricate structure-property relationships within pharmaceutical formulations. Quantum Mechanical (QM) and Molecular Dynamics (MD) simulations are powerful tools for understanding these formulations at a molecular level.
Key areas of interest in pharmaceutical sciences include chemical stability, reactivity, molecular degradation, impurity profiling, excipient selection, and polymorph prediction. A thorough understanding of the Active Pharmaceutical Ingredient (API) is essential before embarking on the formulation development process.
The Schrödinger Materials Science Suite (MS-Suite) offers comprehensive computational workflows to predict spectra (IR, Raman, NMR, UV-Visible, XRD) and assess the API’s behavior under varying pH conditions, including its degradation pathways and chemical reactivity.
Recent advancements in GPU technology have significantly accelerated MD simulations, enabling previously unattainable time scales. This dramatic speedup, combined with predictive accuracy, is poised to revolutionize the use of MD simulations in pharmaceutical formulation development. MD-based workflows can help us address critical formulation design questions on physical stability of formulations, phase transitions, miscibility, solubility and diffusion of API through membranes, morphology, excipient compatibility, encapsulation, and coating selections.
This presentation will highlight several successful case studies that demonstrate these capabilities from a molecular perspective.
SEPT 18 | 18:00 IST Harnessing Molecular Modeling to transform innovation in Polymeric Materials and Consumer Packaged Goods
Speaker:
Sriram Krishnamurthy, Senior Scientist I, Schrödinger
Abstract:
Polymeric materials and consumer packaged goods (CPGs) hold significant importance in both industrial and everyday contexts, impacting numerous aspects of modern life. Polymers play a crucial role in various industries due to their versatility and wide range of applications like construction, electronics, healthcare, and automotive. As scientific research and innovation continue to advance, polymeric materials remain at the forefront of development. In the context of consumer packaged goods (CPG), which significantly influence our daily lives, there is an ongoing push to create products that meet evolving consumer demands, such as healthier food options and more sustainable packaging solutions. Polymeric materials and CPGs are deeply interconnected in their importance to modern society.
Molecular modeling has emerged as a transformative tool in the design and optimization of these materials. By providing deep insights into molecular interactions and material properties, molecular modeling accelerates the development of novel, efficient, and sustainable materials. This approach not only enhances our understanding of material behavior, but also facilitates the innovation of advanced solutions tailored to the specific needs of modern consumer products.
This webinar will highlight Schrödinger’s Materials Science tools that can accelerate R&D efforts in these scientific domains. We will showcase practical case studies to tackle key problems and identify areas where molecular modeling can be applied.
SEPT 25 | 18:00 IST Efficient Computation of Process Parameters for Controlling the Chemistry of Deposition or Etch
Speaker:
Simon Elliott, Research Leader, Schrödinger
Abstract:
We present a variety of computational techniques for understanding, controlling and improving deposition and etch processes. The emphasis is on choosing the right technique for the research question and time available. The same computational techniques can be used to investigate other gas-surface processes, such as catalysis or sensing.
Different chemical processes can be in competition when a solid surface is treated with a gaseous reagent and the outcome is determined by conditions such as temperature and pressure. For instance, continuous deposition (CVD) may take over from self-limiting deposition (ALD) as the temperature is raised. Or temperature may dictate which material is deposited; in the case presented here, ruthenium oxide film is deposited from RuO4+H2 in experiments at 75°C, whereas Ru metal is obtained at 100°C and above. Ru is being investigated as an electroplating seed layer in electronics, as a capacitor electrode and as a heterogeneous catalyst – all applications that require metal rather than oxide. We show that thermodynamics based on density functional theory (DFT) is a computationally-efficient approach for distinguishing between the possible surface-gas processes. The temperatures and pressures for crossover between different chemistries can be estimated, with the accuracy depending on how entropy, coverage and diffusion are treated. We use DFT to examine the conditions of stability for Ru metal, hydride, hydroxide and oxide with respect to H2 and RuO4 reagents, and so explain the crossover from oxide to metal film just below 100°C. We point out how to balance the cost (in terms of researcher time and computer time) against the benefit that each level of accuracy can offer.
In the second part of the talk, we introduce Microkinetic Modelling, a new Schrödinger capability for examining the overall kinetics of gas-surface chemistry by solving the coupled kinetic rate equations of its constituent elementary reaction steps. This allows the simulation of macroscopic parameters such as sticking coefficients that can be experimentally measured and used as inputs for fluid dynamics simulations. We first outline the computational scheme, where elementary steps and their activation free energies have been computed with DFT. The resulting microkinetic model for alumina ALD yields measurable quantities (e.g. growth rate) as a function of temperature and pressure, which are validated against experiment. Variation with pressure can account for penetration depth and conformality within high aspect ratio features.
The two cases discussed in this talk thus illustrate how atomic-scale DFT can be embedded into higher-level computational schemes for accurate and achievable prediction of the conditions and parameters for controlling chemical processes.
OCT 1 | 18:00 IST How Physics-based Modeling and Machine Learning Enable Accelerated Development of Battery Materials
Speaker:
Garvit Agarwal, Senior Scientist II, Schrödinger
Abstract:
The rapid advancements in rechargeable Li-ion battery (LIB) technology over the last decade has revolutionized several key industries such as transportation and consumer electronics. However, new battery chemistries are needed to meet the rapidly growing demand and to improve the power density, safety, reliability, and lifetime of LIBs. Molecular modeling has become an integral part of the design cycle of new battery chemistries. Accurate physics-based modeling enables rapid evaluation and screening of large chemical and material design space thereby, helping industries reduce the time required to bring the new technology to the market.
In this webinar, we will introduce the latest technological innovations in Schrödinger’s digital chemistry platform for battery materials design. In particular, the webinar will focus on examples to demonstrate the application of automated solutions for accurate prediction of thermodynamic stability and voltage profile of cathode materials, ion diffusion pathways and kinetics in electrode materials, transport properties of liquid electrolytes and modeling the nucleation and growth of solid electrolyte interphase (SEI) layers using Schrödinger’s SEI simulator module. We will also introduce an automated generalized framework for the development of customized machine learning force fields for complex materials such as liquid electrolytes, inorganic cathode coatings and solid polymer electrolytes, paving the way for efficient design of novel materials for next generation batteries.
OCT 8 | 18:00 IST Accelerating the Design of Asymmetric Catalysts with Schrödinger’s Digital Chemistry Platform
Speaker:
Saientan Bag, Senior Scientist I, Schrödinger
Abstract:
Asymmetric catalysis has become an integral part of the science-driven technological revolution in the second half of the 21st century, leading to decreased energy demands, sustainable chemical processes and the realization of “impossible” transformations. Asymmetric catalysis based on chiral transition-metal complexes plays an important role in the synthesis of single-enantiomer drugs, perfumes and agrochemicals. The importance of the field is recognized by two Nobel Prize Awards in 2001 (transition-metal catalysis) and 2021 (organocatalysis).
Asymmetric catalysts are traditionally designed by experimental trial-and-error methods, which are resource-, time- and labor-consuming, and thus extremely expensive. Digital methods offer the opportunity to expedite catalyst design. Until recently, computational chemistry, typically quantum chemical studies, indirectly contributed to asymmetric catalyst design by providing rationalization for the mechanism of generation of chirality. With the development of more advanced methods, algorithms and an included layer of automation, computational catalysis is now providing the possibility for direct asymmetric catalyst design.
In this webinar, I will demonstrate how Schrödinger’s advanced digital chemistry platform can be used to accelerate the direct design and discovery of asymmetric catalysts.