The importance of human know-how in AI execution for R&D
How Schrödinger’s materials science domain experts ensure partner success
Overview
As artificial intelligence (AI) and machine learning (ML) technologies rapidly advance, materials scientists, executives, and R&D professionals are being tasked with developing an AI/ML strategy to drive innovation. Yet while AI/ML tools may advertise the appeal of push-button innovation, this is almost never the reality. Even the term AI itself can be a misleading buzzword.
Schrödinger is uniquely positioned to partner with materials R&D teams to execute AI/ML strategies that deliver true business value because we leverage the proven accuracy of physics-based modeling, the speed and scale of machine learning, and the deep domain expertise of our materials scientists. So while AI/ML technologies can be transformative, gaining a competitive advantage with AI is only possible with the talent, vision, and know-how of people who can utilize and direct these technologies towards meaningful, impactful outcomes.
Personalized human support is critical for project efficiency and productivity
Schrödinger’s professional software support is unique in providing domain expertise, personalized assistance, and reliable service. This human difference ensures that users can fully leverage the potential of digital tools to address complex issues through tailored guidance and support.
Schrödinger Support
Benefits
24-7 global support from a team of experts
Faster project timelines
On-site and virtual assistance with expert application scientists
Technical discussions, troubleshooting, and knowledge transfer
Extensive online training, tutorials, and resources
Skilled users to maximize outcomes
Expert-led customized modeling service packages
Novel state-of-the-art research, knowledge transfer and improved success rates
“”As a former industrial modeler at Boeing, I understand that the needs of each client are unique. That’s why we focus on delivering personalized support that addresses their specific challenges in R&D digitization.””
Andrea BrowningDirector of Polymers and Soft Matter
Dr. Andrea Browning is leading the efforts related to polymer and soft matter simulation at Schrödinger. Prior to joining Schrödinger in 2017, she was a lead research engineer and project manager at The Boeing Company. She brings over a decade of experience in connecting simulations to industrial decisions, and has helped industry innovators on challenging projects such as developing bio-based polymer formulations, optimizing polymer matrix in carbon fiber composites, and designing electrolyte molecules.
Schrödinger technical experts increase capacity and capability
Through strategic partnerships or customized contract research, Schrödinger’s team of expert scientists work closely with customers to tackle challenging problems by deploying digital chemistry strategies to guide rapid materials design and optimization. By working as a team, we share the same challenges and goals. These expert-driven collaborations transform challenges into opportunities, driving innovation and delivering exceptional results.
“”Over the course of 20 years working with companies in this sector, I am convinced that collaboration is the key to delivering value, by listening to industry needs and tailoring the modeling solution.””
Simon ElliottDirector of Atomic Level Process Simulation
Dr. Simon Elliott is a pioneer in applying atomic-scale models to the chemistry of thin film material deposition and etch. In this field he has chaired international conferences, coordinated transnational networks and authored about 100 peer-reviewed papers. He is recognized in the semiconductor industry for his work with chemical companies on design of precursor gases and with equipment suppliers on optimizing atomic layer deposition processes. He was the 2023 recipient of the ALD Innovator Award.
Expertise and industry-grade software can be the difference between project success and failure
It is common for materials science researchers to piece together a variety of modeling and simulation tools. Free software packages are often limited by outdated documentation, insufficient support, and obstacles to integration and automation. The human element is one of the key differentiators between industry-grade software like Schrödinger and less sophisticated software packages.
Schrödinger has a large team working on user experience, technical support, and education. This team ensures that clients can seamlessly access the science underpinning the simulations and use it at a scale that allows them to get their job done.
“”Having used free software extensively in the past, I now realize how much time I was spending on troubleshooting rather than actual research. Schrödinger’s solutions are a game-changer for productivity.””
Pavel DubSenior Principal Scientist and Product Manager
Dr. Pavel Dub is an expert in Catalysis & Reactivity. With a robust foundation built through dual doctoral studies in Russia (2009) and France (2010), followed by postdoctoral research at the Tokyo Institute of Technology and Los Alamos National Laboratory, Pavel’s research has evolved from experimental organometallic chemistry and homogeneous catalysis to computational chemistry and materials science, utilizing both classical and quantum computing platforms. Before joining Schrödinger in 2022, Pavel served as a Staff Scientist at Los Alamos National Laboratory, where he led the development of quantum algorithms for solving complex chemical problems. He has worked with clients on advanced topics, including molecular catalyst design, automated reactivity screening, and reaction network generation.
Recent Testimonials
“By working closely with Schrödinger experts, we were impressed by how fast we were able to learn to apply molecular simulations, even with no prior modeling experience. Our collaborations have been very successful, not only because of our satisfaction with Schrödinger’s advanced technologies, but also because of their level of scientific expertise, support, and collaborative openness.”
Martin SettleSenior Research Manager, Reckitt
The resin design and incubation team at SABIC worked closely with Schrödinger’s material science team to build accurate machine learning (ML) models to speed up the discovery of new polymers. “These computational results are highly promising and can potentially shorten our polymer innovation timelines from traditionally a couple of years to only a couple of months.”
Vaidya RamakrishnanStaff Scientist, SABIC
“Schrödinger provides us with more than just software as part of our service agreement—they are a true partner in our research. With an office here in Japan, Schrödinger scientists and engineers are easily accessible and able to collaborate in-person with our team.”
Nobuyuki N. MatsuzawaExecutive Engineer, Panasonic Industry Co., Ltd.
Software and services to meet your organizational needs
Software Platform
Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.
Andrea Browning Director of Polymers and Soft Matter
Recent Testimonials