Advanced materials (smart materials) are superior in properties e.g., materials that can sense,
lighter in weight but stronger, super-hydrophobic, deliver drugs to the specific targets, and so
on. We help industries in design and innovate such materials by performing computer simulation
at the atomic-molecular scale. We develop and perform atomic-level quantum chemical calculations
and take it forward to the nanoscale to mesoscale simulations to predict the properties of these
newly designed materials. We build highly predictive models to design new/smart advanced
materials for industries e.g., rubber (tire), additive, energy (oil and gas), lubricants,
adhesives, coatings, membrane for gas seperations, drug delivery vectors, etc.
The development of new materials with applications in energy, medicine, structural materials and
composites, and many others is an essential part of our R&D. Empirical screening of combinations
of materials is still a widely used approach for optimization of properties. But the discovery
of new and smart materials, and combinations of materials, could be accelerated through the
application of combinatorial approaches such as materials genomics. We routinely use artificial
intelligence to address such problems. Our software platform is designed to handle materials
genomics and traditional computational schemes for property and composition predictions.
Additionally, we also provide a mechanism for structure-property relationships, which help
scientists and engineers in designing new materials.
The oil and gas industry is at a tipping point and the world is moving towards renewable energy leaving fossil fuels behind. Simulation and modeling are helping industries unlocking the true potential of alternate energy, e.g., hydrogen, solar, and wind. Alternative energy needs a large amount of storage and batteries are playing that intermediate role between production and utilization. Computational modeling and simulations are also helping in finding ways of utilizing natural gas by making carbon-based products like polymers, synthetic lubricants, carbon fiber, and nanotube. At Prescience, we are working on a solution-driven computational platform for the computation of different components for these energy harvesting materials for better performance to meet the global and economic needs. We are pioneers in developing applications for computational modeling of batteries and fuel cells (anode, cathode, electrolyte), solar photovoltaic materials (quantum dots, nanomaterials, organic light-harvesting dyes), catalysis (e.g., polymerization reactions, water spitting, etc.). Our computational tools are based on automated quantum chemical, atomic-level molecular dynamics methods and we apply several analysis methods for the prediction of properties and mechanisms.
The chemical industry is vast encompasses all small, medium large scale companies working on chemicals and their synthesis. These companies cater to the need of all other industries that are in manufacturing e.g., semiconductors, drugs, materials, drug delivery, fast-moving consumer goods, packaging, paint, adhesives, metals, nanomaterials, solvents. The impact of modeling on these industries can be traced from the utilization of the information in the development of new products that benefit society at large. Therefore molecular modeling and simulation are accepted as a mainstream tool that is useful, practical, and highly accessible because of the use of modern hardware. The improved know-how from these computations could focus the industry on innovative products. The methods vary from quantum chemistry to molecular simulations dependent on the application areas such as catalysis polymers and chemical engineering. Prescience is dedicated to developing tools and servicing industries for the development of innovative products. Our chemical screening tools based on ab initio, molecular simulations, and AI methods could predict properties and screen chemicals for a specific target.
Over the last several years, insilico drug discovery approaches have enabled the identification, selection and optimisation of pharmacological potential drug candidates through high throughput screening techniques driven by massive computing power and advancement in data mining and deep learning techniques. Interestingly, over 30% of the total investment in the drug discovery process for a single drug can be saved using insilico services. Here at Prescience, we aim to reduce the gap in the computational cost and the time required for the existing drug designing approaches. The company has developed several platforms that focus on target-based drug identification and molecular design.