Campus Recruitment at GCT

EinNel recently conducted a campus drive at the Government College of Technology (GCT) campus in Coimbatore. The interviews were attended by around 25 postgraduate students from the thermal engineering, engineering design, and manufacturing engineering departments. The purpose of the interviews was to identify highly talented engineers to extend EinNel’s research in electric vehicles, hydrogen combustion engines, and fuel cells.

Dr. Ramesh, Head of the Department, and Dr. K. Manonmani, Principal, were delighted to have the EinNel team on their campus and expressed their interest in signing an MoU for research projects.

During the campus drive, Ms. Mercy, an alumna of GCT and a member of EinNel’s Data Science team, presented how AI/ML can be implemented in mechanical systems using Model-Based Systems Engineering for better product development and optimization. Her presentation was well received by the students and provided great encouragement for them to learn data science and implement modern problem-solving procedures.

After a careful evaluation of all the candidates, EinNel Technologies selected five students for employment based on their exceptional skills and knowledge in their respective fields, as well as their enthusiasm and passion for learning.


Automotive Testing Expo

Asia’s largest automotive expo took place in Chennai Trade Centre from April 18-20. The event showcased technologies and equipment for vehicle testing from various world-renowned companies.

A team of engineers from EinNel specializing in full-vehicle design and embedded systems visited the expo and had fruitful discussions with representatives from companies such as D-Space, AVL, Dewesoft, Vector, and Siemens. They also observed the latest vehicle testing procedures, hardware setups, and HIL & SIL validation methods.

As a result of the visit, EinNel’s embedded system team has decided to expand the testing lab with more vehicle testing prototypes, focusing mainly on electric vehicles and batteries, and incorporating new data acquisition systems.

EinNel plans to collaborate with organizations such as GARC, NATRIP, ARAI, NIAIMT, and ICAT to develop new solutions for the vehicle testing process. We aim to incorporate AI/ML methods and develop new dashboards to enhance the vehicle testing process at the OEMs end.


EinNel Technologies at the British Deputy High Commission, Chennai

EinNel Technologies

EinNel leaders were invited for a roundtable meeting with the British Deputy High Commissioner along with London & Partners. The purpose of the meeting was to discuss opportunities for EinNel to establish its business in the UK.

During the meeting, Mr. Einstein presented EinNel’s capabilities in automobile and software development to the team. The London & Partners team recommended that EinNel’s executives attend two upcoming events - the London Tech Week event on June 12-16 and the MOVE conference on June 21-22.

EinNel team agreed to attend the events and take advantage of the business networks coordinated by the British Deputy High Commission team and supported by London & Partners. The ultimate goal of these initiatives is to explore opportunities to promote EinNel’s business in the UK and establish an office for future expansion.


Mechanistic Approach in Engineering Problem Solving

Prabhakaran Veeramani, Lead Project Engineer

Engineering problems can be highly complex, and while some simpler ones may be resolved through analytical calculations, the advent of computational technology has given rise to Finite Element Modeling (FEM), which utilizes numerical calculations to solve complex problems that were previously unsolvable through basic mathematical computations.

Engineering problems can be highly complex, and while some simpler ones may be resolved through analytical calculations, the advent of computational technology has given rise to Finite Element Modeling (FEM), which utilizes numerical calculations to solve complex problems that were previously unsolvable through basic mathematical computations.

Mechanistic modeling is a fusion of modern data science techniques and mathematical science employed to tackle complex engineering problems.

The following illustration provides a clearer explanation:


To build a Mechanistic model, the following steps should be taken:

Step 1 Identify the problem that needs to be solved.
Step 2 Perform a root cause analysis to determine the underlying factors contributing to the problem.
Step 3 Identify the major influencing factors among the contributing factors.
Step 4 Extract mechanistic features in the form of a dataset corresponding to the major influencing factors.
Step 5 Perform exploratory data analysis to gain insights into the data.
Step 6 Perform feature engineering to prepare the data for machine learning models.
Step 7 Build and evaluate appropriate machine learning models using the features extracted with underlying physics in consideration.


Since machine learning models are built with features extracted from underlying physics, the reliability of a mechanistic model is higher than that of a plain statistical model. The study of system characterization can be classified into three categories: single system characterization, homogeneous multi-system characterization, and heterogeneous multi-system characterization.

The major advantage of mechanistic modeling is its ability to capture characterization from different forms of inputs, such as design inputs, process inputs, and manual inputs, in engineering problems. In a Finite Element Analysis (FEA) analysis, it is nearly impossible to study the combined effect of all kinds of inputs. However, mechanistic modeling can capture this effect in the form of a data model.


Another advantage of mechanistic modeling is that, unlike traditional methods, the scientific efforts required are concentrated primarily in the initial stages of the product or project. As the project progresses, the scientific effort required significantly reduces. In contrast, traditional methods require constant effort throughout the product or project lifetime. Thus, mechanistic modeling is a powerful approach for solving complex engineering problems.


Quantum Chemistry, AI and Drug Development

Dr. Dhatchana Moorthy, Director, EinNext Biosciences

Computational chemistry and quantum chemistry are scientific fields that use computer simulations and calculations to study molecules and materials at the atomic and molecular levels. Computational chemistry predicts chemical systems’ properties and behaviour, while quantum chemistry applies quantum mechanics to understand the electronic structure and properties of molecules. These fields play a crucial role in drug discovery, material science, and environmental science by providing insights into the fundamental principles that govern chemical phenomena.

Computational chemistry is essential in drug development as it helps scientists model the behaviour of solvents and active pharmaceutical ingredients (API) / drugs at a quantum level. Understanding the quantum properties of these molecules is crucial for designing safe, effective drugs with desired properties. Computational chemistry is also crucial in developing new drug formulations and optimizing existing ones by improving the solubility and stability of drugs.

However, quantum chemistry can be complex, making it challenging for bench-level research scientists to apply it to drug solubility problems. To alleviate this challenge, a user-friendly dashboard interface with quantum chemical calculations running in the background can provide a comprehensive view of a drug’s molecular properties and thermodynamic parameters, making it easier to predict and compare the solubility of different drugs in a solvent.


A screenshot of a user interface prototype for calculating the total activity of Aspirin in different solvent mixtures.


EinNext Biosciences offers customized dashboard solutions powered by advanced AI/ML techniques to cater to various industries such as pharmaceuticals, chemicals, paints, perfumeries, petrochemicals, and distilleries. These solutions are built using python frameworks, task management libraries, and a broker service that employs implicit solvation models to compute quantum chemical properties. The dashboards display valuable insights such as activity and partition coefficients of a given API in a solvent mixture, presented through visually engaging data visualizations.

Our EinNext Biosciences team has recently completed a few projects for our clients, and it is evident that our dashboard solutions will drastically reduce the time and cost associated with standardizing solutions and formulations.