Controlling Deformation in Al/Ti: How Interface Roughness and Rotation Drive Bimetal Mechanics
A. Mishra, J. S. Carpenter, and S. J. Fensin
Physical Review Materials 10, 063602
Hello, I am Avanish Mishra
Staff Scientist at Los Alamos National Laboratory — integrating first-principles methods and molecular dynamics with machine learning and quantum computing to accelerate materials discovery
Atomistic to continuum
AI/ML-driven discovery
For chemistry & materials
Simulation ↔ experiment
I am a staff scientist in the Theoretical Division at Los Alamos National Laboratory, specializing in the design of structural materials for extreme environments and functional materials for energy-to-electronics applications.
My work integrates first-principles methods (DFT, VASP, Quantum ESPRESSO) and molecular dynamics (LAMMPS) with emerging paradigms such as machine learning — including graph neural networks, generative AI, active learning, and large language models — and quantum computing (Qiskit, pySCF) to accelerate materials discovery.
I have contributed to open data infrastructure (aNANt database, JARVIS-Leaderboard, URSA) and authored 38+ peer-reviewed publications with 2,538+ citations and an h-index of 19.
Ph.D. Materials Science
Indian Institute of Science, Bangalore
(2014–2019)
M.Sc. Physics · B.Sc. Physics, Math & Chemistry — DDU Gorakhpur University
Director's Postdoc Fellow — LANL, 2022
Science in 3 (Top 30 postdocs) · Kawazoe Prize · GATE AIR 36 · CSIR-NET AIR 58
DFT · MD · VASP · LAMMPS · Quantum ESPRESSO
pySCF · Qiskit · ASE · GenAI · LLMs · Agentic AI
TMS · APS · MRS · ICME
Symposium co-organizer: TMS 2025 & 2026 AI-ICME
Leading research in materials informatics, quantum computing for chemistry, generative AI for process-structure mapping, and virtual characterization tools.
Developed physics-informed ML for grain boundary property prediction using strain functional descriptors. Selected as one of 30 postdocs for "Science in 3" at LANL.
Pioneered virtual characterization tools for shock-induced deformations, phase transformation mechanisms, and diffraction fingerprinting in dynamic loading.
Dissertation: "Exploration of exfoliation, functionalization and properties of MXenes via first-principles and machine learning."
Our interdisciplinary team at Los Alamos National Laboratory
Staff Scientist, Theoretical Division, LANL
04/2026 - present
08/2025 - present
Co-advised with Dr. Yu Zhang, LANL
02/2026 - present
Co-advised with Dr. Tyler Reddy, LANL
06/2026 - present
Carnegie Mellon University
Co-advised with Dr. Saryu Fensin, LANL
06/2026 - present
Purdue University
Please add names, roles, dates, and current positions in data/people.json.
Leveraging artificial intelligence, machine learning, and quantum computing to solve the hardest problems in materials science
Data-driven discovery fusing physics-based descriptors with ML models. Active learning, graph neural networks, and explainable AI for accelerated materials design.
Learn more →Hybrid quantum–classical strategies for correlated materials. Variational algorithms, error mitigation, and domain-specific workflows for catalysis and energy storage.
Learn more →Predictive models for extreme environments — fusion reactors, shock deformation, and radiation damage. Multiscale simulations from atoms to continuum.
Learn more →Engineering materials from bulk to 2D — complex oxides, heterostructures, and hybrids for energy, electronics, and photonics.
Learn more →Computational tools for texture analysis, diffraction simulation, and orientation relationships from atomistic data.
Learn more →Peer-reviewed publications, open datasets, and software contributions for AI-driven materials science
Showing curated papers from CV. Google Scholar is linked directly; live browser fetching from Scholar is not available.
A. Mishra, J. S. Carpenter, and S. J. Fensin
Physical Review Materials 10, 063602
B. Murgas, A. Mishra, N. Mathew, and A. Hunter
Journal of Applied Physics 139, 155101
P. Tsurkan, M. J. Echeverria, A. Mishra, and A. M. Dongare
Journal of Applied Physics 138, 225902
A. Mishra, B. W. Hamilton, M. S. Nitol, N. Mathew, T. C. Germann, and S. J. Fensin
Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 3593–3602
M. S. Nitol, A. Mishra, S. Xu, and S. J. Fensin
Physical Review Materials 9, 063601
K. Dang, S. Suresh, A. Mishra, I. Chesser, N. Mathew, E. M. Kober, and S. J. Fensin
Scientific Data 12, 955
R. Kumar, A. K. Shringi, H. J. Wood, S. Oturak, T. S. K. Sharma, R. Chaurasiya, A. Mishra, W. M. Choi, N. Y. Doumon, I. Daboc, M. Terrones, and F. Yan
Materials Science and Engineering: R: Reports 163, 100946
A. Mishra, S. Suresh, S. J. Fensin, E. M. Kober, and N. Mathew
Physical Review Materials 8, 123605
I. Chesser, P. M. Derlet, A. Mishra, S. Paguaga, N. Mathew, K. Q. Dang, B. P. Uberuaga, A. Hunter, and S. Fensin
Physical Review Materials 8, 093606
A. Bärtschi, F. Caravelli, C. Coffrin, J. Colina, S. Eidenbenz, A. Jayakumar, S. Lawrence, M. Lee, A. Y. Lokhov, A. Mishra, S. Misra, Z. Morrell, Z. Mughal, D. Neill, A. Piryatinski, A. Scheie, M. Vuffray, and Y. Zhang
arXiv preprint
T. M. Kucinski, R. Dhall, B. Savitzky, C. L. Ophus, R. Karkee, A. Mishra, E. Dervishi, J. H. Kang, C. Lee, J. Yoo, and M. T. Pettes
ACS Nano 18, 27
K. Dang, A. Mishra, S. Suresh, E. M. Kober, N. Mathew, and S. J. Fensin
Physical Review Materials 8, 063604
K. Choudhary et al.
npj Computational Materials 10, 93
S. Parida, A. Mishra, Q. Yang, A. Dobley, C. B. Carter, and A. M. Dongare
Journal of Materials Science 59, 932
A. Mishra, K. Dang, E. M. Kober, S. J. Fensin, and N. Mathew
Materials Research Letters 11, 872
R. Kumar, J. Chen, A. Mishra, and A. M. Dongare
Scientific Reports 13, 11365
S. Galitskiy, A. Mishra, and A. M. Dongare
International Journal of Plasticity 164, 103596
C. Ching, S. Galitskiy, A. Mishra, and A. M. Dongare
Journal of Applied Physics 133, 105901
A. Mishra, K. Ma, and A. M. Dongare
Journal of Materials Science 57, 12782
A. Mishra, M. J. Echeverria, K. Ma, S. Parida, C. Chen, S. Galitskiy, and A. M. Dongare
Journal of Materials Science 57, 10549
S. Satsangi, A. Mishra, and A. K. Singh
ACS Physical Chemistry Au 2, 16
A. Mishra, J. Lind, M. Kumar, and A. M. Dongare
Journal of Applied Physics 130, 215902
M. J. Echeverria, S. Galitskiy, A. Mishra, and A. M. Dongare
Computational Materials Science 198, 110668
A. Mishra, C. Kunka, M. J. Echeverria, R. Dingreville, and A. M. Dongare
Scientific Reports 11, 9872
P. Xu, W. Rheinheimer, A. Mishra, S. N. Shuvo, Z. Qi, H. Wang, A. M. Dongare, and L. A. Stanciu
ChemElectroChem 8, 1847
S. N. Shuvo, A. M. U. Gomez, A. Mishra, W. Y. Chen, A. M. Dongare, and L. A. Stanciu
ACS Sensors 5, 2915
S. Parida, A. Mishra, J. Chen, J. Wang, C. B. Carter, and A. M. Dongare
Journal of the American Ceramic Society 103, 6603
T. Pandey, A. Nissimagoudar, A. Mishra, and A. K. Singh
Journal of Materials Chemistry A 8, 13812
N. Lertcumfu, F. N. Sayed, S. N. Shirodkar, S. Radhakrishnana, A. Mishra, G. Rujijanagul, A. K. Singh, B. I. Yakobson, C. S. Tiwary, and P. M. Ajayan
Physica Status Solidi A 1801004
N. Sethulakshmi, A. Mishra, P. M. Ajayan, Y. Kawazoe, A. Roy, A. K. Singh, and C. S. Tiwary
Materials Today 27, 107
M. Khazaei, A. Mishra, N. S. Venkataramanan, A. K. Singh, and S. Yunoki
Current Opinion in Solid State and Materials Science 23, 164
A. Mishra, S. Satsangi, A. C. Rajan, H. Mizuseki, K.-R. Lee, and A. K. Singh
Journal of Physical Chemistry Letters 10, 780
A. C. Rajan, A. Mishra, S. Satsangi, R. Vaish, H. Mizuseki, K.-R. Lee, and A. K. Singh
Chemistry of Materials 30, 4031
R. Koizumi, S. Ozden, A. Samanta, A. P. P. Alves, A. Mishra, G. Ye, G. G. Silva, R. Vajtai, A. K. Singh, C. S. Tiwary, and P. M. Ajayan
Advanced Materials Interfaces 5, 1701113
A. Chandrasekaran, A. Mishra, and A. K. Singh
Nano Letters 17, 3290
A. Mishra, P. Srivastava, A. Carreras, I. Tanaka, H. Mizuseki, K.-R. Lee, and A. K. Singh
Journal of Physical Chemistry C 121, 18947
P. Srivastava, A. Mishra, H. Mizuseki, K.-R. Lee, and A. K. Singh
ACS Applied Materials & Interfaces 8, 24256
A. Mishra, P. Srivastava, H. Mizuseki, K.-R. Lee, and A. K. Singh
Physical Chemistry Chemical Physics 18, 11073
Codes, databases, and AI-powered tools I've developed and contributed to
India's first computational materials database — 23,000+ MXenes with optimized structures and electronic properties for ML training.
Database · ML-Ready · Ph.D.Python package for virtual texture analysis from atomistic microstructures: orientation relationships, misorientations, Schmid factors.
Python · AnalysisOfficial contributor to NIST's benchmarking platform for materials science ML methods and AI models.
NIST · AI BenchmarkingContributor to LANL's Universal Research and Scientific Agent — an AI-powered research assistant for scientific discovery.
LANL · LLM AgentComing soon.
We are actively recruiting motivated researchers at all levels who are passionate about AI, quantum computing, and materials science
Opportunities for researchers interested in AI for materials, quantum computing workflows, multiscale simulation, and virtual characterization.
Apply through the official LANL jobs posting. Interested candidates may also contact the group to discuss research fit.
Apply on LANL JobsSend your CV and a brief statement of research interests. Include 2-3 representative papers if available.
Contact about postdoc rolesGraduate research opportunities are available through LANL student programs, fellowships, and collaborative university projects.
Interested graduate students should send a short research-interest note, CV, and expected availability.
Contact about graduate rolesUndergraduate internships are suitable for students interested in computational materials science, Python-based scientific software, AI/ML, and simulation analysis.
Please include your CV/resume, coursework or coding experience, and preferred research topics.
Contact about internshipsWork at the forefront of AI + materials science at one of the world's premier research laboratories. Our group offers a unique combination of cutting-edge science, world-class computing resources, and a collaborative, mentorship-driven environment.
Access to LANL supercomputers & quantum hardware
AI, quantum computing, physics & chemistry
Mentorship & pathways for future growth
Located in beautiful Northern New Mexico
Open to Non-sensitive Foreign Nationals, Permanent Residents, and US Citizens.
Interested in collaboration or open positions?