Atul Thakur Email & Phone Number
@rutgers.edu
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Atul Thakur is listed as Development of Machine Learning Methods For Semiconductor Modeling and Discovery at UC San Diego, a company with 31563 employees, based in Somerset, New Jersey, United States. AeroLeads shows a work email signal at rutgers.edu and a matched LinkedIn profile for Atul Thakur.
Atul Thakur previously worked as Long-range Linear Scaling Machine Learning Interatomic Potentials For Materials Modeling at Rutgers University–New Brunswick and Q-Self-Consistent Field Neural Network for Long-Ranged Machine Learning Interatomic Potentials at Rutgers University–New Brunswick. Atul Thakur holds Doctor Of Philosophy - Phd, Chemical Physics from Rutgers University–New Brunswick.
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About Atul Thakur
As a PhD candidate in Chemical Physics at Rutgers University, I am passionate about developing and applying machine learning interatomic potentials for materials and electrolytes modeling. I have a strong background in computational chemistry, materials characterization, and coding. My current research focuses on exploring the dielectric response tensor of nanoconfined polar fluids, extending the transferability of short-ranged models with linear scaling electrostatic methods, and implementing an equivariant charge-based self-consistent field neural network model. I have also developed a machine learning pipeline and an attention-based graph neural network architecture to predict coformers in organic co-crystals. I am passionate about applying machine learning to solve challenging problems in physical sciences and engineering, and I aspire to contribute to the advancement of knowledge and innovation in this field. I value collaboration, creativity, and diversity, and I believe that I can bring unique perspectives and experiences to the team.
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Atul Thakur work experience
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Long-Range Linear Scaling Machine Learning Interatomic Potentials For Materials Modeling
Current- Explored the dielectric response tensor of nanoconfined polar fluids at the electrode interface using Gaussian truncated and machine learning models to understand long-ranged dipolar correlations.
- Extended the transferability of short-ranged models with local molecular field (LMF) and symmetry-preserving mean field (SPMF) linear scaling electrostatic methods for long-range interactions.
Q-Self-Consistent Field Neural Network For Long-Ranged Machine Learning Interatomic Potentials
Current- Implemented an equivarient Charge-based (Q) Self-Consistent Field Neural Network (SCFNN) model using symmetry function vectors for accurately modeling materials and electrolytes.
- Interfaced the Q-SCFNN approach with multi-layer perceptron models (N2P2, DeePMD) and graph neural networks (NequIP, Allegro) for developing ML models with exact long-ranged electrostatics.
Leveraging Graph Neural Networks For Predictive Modeling Of Molecular Co-Crystal Formation
Current- Implemented data augmentation techniques and feature representation using Cambridge database’s Python API.
- Developed a machine learning pipeline using TensorFlow and designed an attention-based graph neural network architecture to predict coformers in binary and ternery organic co-crystals.
Characterizing Dynamically Disordered Phases Of Molecular Crystals With Machine Learning
Current- Explored the dynamics and thermodynamics of the dynamically disordered rotor phases of molecular crystals using ab initio molecular dynamics simulations (AIMD).
- Quantified the impact of Nuclear Quantum Effects (NQEs) on the plastic phase using ML-based molecular dynamics simulations combined with thermostatted ring polymer molecular dynamics (TRPMD).
Ab-Initio Machine Learning Evaluation Of Mechanical Properties Of Plastic Molecular Crystals
Current- Computed the full ab initio elasticity tensor of the molecular crystals using ML potentials combined with stress-strain fluctuation relations and finite deformation protocols.
- Illustrated that machine learning potentials can provide accurate estimation of material’s mechanical constants.
Protein Stabilization In Salt Solutions: Specific Ion Effects
- Thesis Title: Protein Stabilization In Salt Solutions: Specific Ion Effects
- Studied the unfolding dynamics and thermodynamics of Ubiquitin protein using well-temperedmetadynamics in the presence of Hofmeister salts.Studied the sensitivity of Ubiquitin unfolding to changes in protein and water.
- Modified the GROMACS source (in C++) to monitor the dynamics of water in custom-sized and shaped patches around the residues of the protein.
Atul Thakur education
Doctor Of Philosophy - Phd, Chemical Physics
Master Of Science - Ms, Chemical Physics
Bachelor Of Science - Bs, Chemical Physics
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What company does Atul Thakur work for?
Atul Thakur works for UC San Diego.
What is Atul Thakur's role at UC San Diego?
Atul Thakur is listed as Development of Machine Learning Methods For Semiconductor Modeling and Discovery at UC San Diego.
What is Atul Thakur's email address?
AeroLeads has found 1 work email signal at @rutgers.edu for Atul Thakur at UC San Diego.
Where is Atul Thakur based?
Atul Thakur is based in Somerset, New Jersey, United States while working with UC San Diego.
What companies has Atul Thakur worked for?
Atul Thakur has worked for Uc San Diego, Rutgers University–New Brunswick, and Indian Institute Of Science Education And Research (Iiser), Pune.
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What schools did Atul Thakur attend?
Atul Thakur holds Doctor Of Philosophy - Phd, Chemical Physics from Rutgers University–New Brunswick.
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