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Ayush Kumar Pandey Computational Condensed Matter Physicist
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Headline:
Computational Physicist & AI Researcher | Math, Physics, Chemistry, Coding & Advanced Materials Design
Profile Description:
I bridge the gap between textbook science and cutting-edge research. I am a Computational Condensed Matter Physicist working on Data-Driven Materials Design using AI and Supercomputing.
Whether you are a high school student needing help with Calculus and Chemistry, or a PhD researcher struggling with Statistical Mechanics and Machine Learning, I tailor my teaching exactly to your level. I believe science should be intuitive, visual, and practical.
Choose your level:
1. School & Undergraduate Level (The Foundation)
I specialize in breaking down complex concepts into simple, logical steps. I cover the entire curriculum for Physics, Maths, and Chemistry.
Physics (All Levels): Mechanics, Electromagnetism, Optics, Thermodynamics, and Quantum Physics.
Mathematics (All Levels): Algebra, Calculus (Single & Multivariable), Linear Algebra, Differential Equations, Probability, and Statistics.
Chemistry (All Levels): Physical, Inorganic, and Organic Chemistry.
Coding: Python for beginners to advanced, Shell Scripting, Data Analysis, and Scientific Computing.
2. Graduate & Research Level (The Specialization)
I provide expert guidance on the atomistic study of complex systems (Halide Perovskites, Amorphous Materials, Supercapacitors). I help you go from theory to simulation to publication.
Advanced Theory: Condensed Matter Physics, and Statistical Physics.
Simulation Methods: DFT (Density Functional Theory), MD (Molecular Dynamics), Monte-Carlo (MC), Nudged-Elastic Band (NEB), and Ab-Initio Molecular Dynamics (AIMD).
Machine Learning for Science: ML Force Fields, Neural Networks, Deep Learning, Graph Neural Networks, and Data-Driven Materials Design.
Tools: VASP, Quantum ESPRESSO, CP2K, LAMMPS, ASE, PyTorch, and TensorFlow.
Scientific Communication: Thesis writing, LaTeX typesetting, and structuring manuscripts for high-impact journals.
My Approach:
I am currently working on the discovery of new energy materials using ML. I bring this active research experience into our sessions.
For Students: We focus on clarity, problem-solving techniques, and exam confidence.
For Researchers: We focus on debugging code, analyzing data, and polishing your final research paper.
From basic integrals to training 50,000-atom simulations—let’s solve your problem together.
Computational Physicist & AI Researcher | Math, Physics, Chemistry, Coding & Advanced Materials Design
Profile Description:
I bridge the gap between textbook science and cutting-edge research. I am a Computational Condensed Matter Physicist working on Data-Driven Materials Design using AI and Supercomputing.
Whether you are a high school student needing help with Calculus and Chemistry, or a PhD researcher struggling with Statistical Mechanics and Machine Learning, I tailor my teaching exactly to your level. I believe science should be intuitive, visual, and practical.
Choose your level:
1. School & Undergraduate Level (The Foundation)
I specialize in breaking down complex concepts into simple, logical steps. I cover the entire curriculum for Physics, Maths, and Chemistry.
Physics (All Levels): Mechanics, Electromagnetism, Optics, Thermodynamics, and Quantum Physics.
Mathematics (All Levels): Algebra, Calculus (Single & Multivariable), Linear Algebra, Differential Equations, Probability, and Statistics.
Chemistry (All Levels): Physical, Inorganic, and Organic Chemistry.
Coding: Python for beginners to advanced, Shell Scripting, Data Analysis, and Scientific Computing.
2. Graduate & Research Level (The Specialization)
I provide expert guidance on the atomistic study of complex systems (Halide Perovskites, Amorphous Materials, Supercapacitors). I help you go from theory to simulation to publication.
Advanced Theory: Condensed Matter Physics, and Statistical Physics.
Simulation Methods: DFT (Density Functional Theory), MD (Molecular Dynamics), Monte-Carlo (MC), Nudged-Elastic Band (NEB), and Ab-Initio Molecular Dynamics (AIMD).
Machine Learning for Science: ML Force Fields, Neural Networks, Deep Learning, Graph Neural Networks, and Data-Driven Materials Design.
Tools: VASP, Quantum ESPRESSO, CP2K, LAMMPS, ASE, PyTorch, and TensorFlow.
Scientific Communication: Thesis writing, LaTeX typesetting, and structuring manuscripts for high-impact journals.
My Approach:
I am currently working on the discovery of new energy materials using ML. I bring this active research experience into our sessions.
For Students: We focus on clarity, problem-solving techniques, and exam confidence.
For Researchers: We focus on debugging code, analyzing data, and polishing your final research paper.
From basic integrals to training 50,000-atom simulations—let’s solve your problem together.
Subjects
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Maths Beginner-Expert
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Physics Beginner-Expert
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Computational Physics Beginner-Expert
Experience
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Teaching Assistant (Dec, 2019–Present) at IIT Roorkee
Mentored and taught undergraduate, postgraduate, and doctoral students.
Education
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Doctor of Philosophy in Physics (Dec, 2019–now) from INDIAN INSTITUTE OF TECHNOLOGY ROORKEE, Roorkee
Fee details
₹1,500/hour (US$15.79/hour)
Rates may vary depending upon the urgency, timings, and the academic level.
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Cautley Bhawan, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
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Can travel:
No
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- Last login: Jan 19
- Registered: Dec 15, 2025
- Total Teaching exp: 6.0 yrs.
- Teaches online: Yes
- Online Teaching exp: 6.0 yrs.
- Teaches at student's home: No
- Homework Help: Yes
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Gender: Male
- Works as: Individual teacher
- Speaks: English, French, German, Hindi
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Fee:₹1,500/hour(USD 15.79/hour)
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