_Website not available. Sign in: https://www.twine.net/signup_ Dr. Muhammad Risyad Hasyim is a computational scientist and Simons Postdoctoral Fellow at New York University's Department of Chemistry, specializing in the intersection of machine learning and molecular simulations. He earned his Ph.D. in Chemical Engineering from UC Berkeley in 2023, where his research focused on understanding glassy dynamics in supercooled liquids using advanced computational methods. Currently, Dr. Hasyim leads electrolyte dataset development for the Open Molecules 2025 project, a collaboration with Meta's FAIR Chemistry team aimed at creating foundational AI models for chemistry. His work involves coordinating researchers across multiple institutions to generate massive datasets of molecular calculations for training next-generation AI systems. He has authored eleven peer-reviewed publications, including work published in PNAS, and serves as a peer reviewer for several academic journals. Beyond his academic research, Dr. Hasyim runs a successful scientific consulting practice, generating over $23,000 in revenue by developing AI-powered solutions for energy and pharmaceutical sectors. His expertise spans computational drug discovery, battery optimization, and materials modeling using tools like PyTorch, COMSOL, and various molecular simulation packages. He is also passionate about education, currently tutoring high school students in computational chemistry and developing educational curricula to make advanced scientific computing more accessible to younger learners.…

Muhammad Risyad Hasyim

_Website not available. Sign in: https://www.twine.net/signup_ Dr. Muhammad Risyad Hasyim is a computational scientist and Simons Postdoctoral Fellow at New York University's Department of Chemistry, specializing in the intersection of machine learning and molecular simulations. He earned his Ph.D. in Chemical Engineering from UC Berkeley in 2023, where his research focused on understanding glassy dynamics in supercooled liquids using advanced computational methods. Currently, Dr. Hasyim leads electrolyte dataset development for the Open Molecules 2025 project, a collaboration with Meta's FAIR Chemistry team aimed at creating foundational AI models for chemistry. His work involves coordinating researchers across multiple institutions to generate massive datasets of molecular calculations for training next-generation AI systems. He has authored eleven peer-reviewed publications, including work published in PNAS, and serves as a peer reviewer for several academic journals. Beyond his academic research, Dr. Hasyim runs a successful scientific consulting practice, generating over $23,000 in revenue by developing AI-powered solutions for energy and pharmaceutical sectors. His expertise spans computational drug discovery, battery optimization, and materials modeling using tools like PyTorch, COMSOL, and various molecular simulation packages. He is also passionate about education, currently tutoring high school students in computational chemistry and developing educational curricula to make advanced scientific computing more accessible to younger learners.…

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Dr. Muhammad Risyad Hasyim is a computational scientist and Simons Postdoctoral Fellow at New York University’s Department of Chemistry, specializing in the intersection of machine learning and molecular simulations. He earned his Ph.D. in Chemical Engineering from UC Berkeley in 2023, where his research focused on understanding glassy dynamics in supercooled liquids using advanced computational methods.

Currently, Dr. Hasyim leads electrolyte dataset development for the Open Molecules 2025 project, a collaboration with Meta’s FAIR Chemistry team aimed at creating foundational AI models for chemistry. His work involves coordinating researchers across multiple institutions to generate massive datasets of molecular calculations for training next-generation AI systems. He has authored eleven peer-reviewed publications, including work published in PNAS, and serves as a peer reviewer for several academic journals.

Beyond his academic research, Dr. Hasyim runs a successful scientific consulting practice, generating over $23,000 in revenue by developing AI-powered solutions for energy and pharmaceutical sectors. His expertise spans computational drug discovery, battery optimization, and materials modeling using tools like PyTorch, COMSOL, and various molecular simulation packages. He is also passionate about education, currently tutoring high school students in computational chemistry and developing educational curricula to make advanced scientific computing more accessible to younger learners.

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Skills

Experience Level

C++
Expert
Python
Expert

Language

English
Fluent
Indonesian
Fluent

Work Experience

Postdoctoral Fellow at New York University, Department of Chemistry
August 1, 2023 - Present
Currently, I am obtaining the Simons Postdoctoral Fellowship while conducting independent research in machine learning for molecular simulations and managing large datasets for foundational ML models.
Graduate Student Research Assistant at UC - Berkeley, Department of Chemical and Biomolecular Engineering
July 31, 2017 - July 31, 2023
◦ Developed GPU-accelerated scientific computing infrastructure (C++/CUDA) for large-scale Monte Carlo and molecular dynamics, achieving 10-100× speedup and enabling million-particle systems simulations. ◦ Engineered a distributed parallel sampling algorithm (MPI-based), achieving 10× acceleration for rare event sampling, scaling across multi-node HPC clusters. ◦ Trained deep neural networks (PyTorch) to learn transition probabilities from simulation trajectories, achieving 90%+ accuracy and enabling 100-1000× speedup for rare event prediction. ◦ Built pyglasstools, an open-source Python package for eigenvalue computations, interfacing with PETSc/SLEPc for distributed calculations
Scientific Consultant at MRH Scientific
February 1, 2025 - Present
◦ Generated $23,000+ in one year providing ML and scientific computing consulting across AI, energy, and pharmaceutical sectors. ◦ Built end-to-end computational drug screening pipeline orchestrating multiple AI models (AlphaFold, DiffDock, ADMET-AI) with molecular simulation tools (RDKit, OpenMM). ◦ Developed physics-informed ML models for battery optimization and electrolyzer design using differentiable simulation frameworks (PyBAMM/PyBOP) and finite element methods (COMSOL). ◦ Prototyped agentic AI system for materials discovery using LangChain with Model Context Protocol (MCP) for multi-tool orchestration and autonomous workflows.

Education

Ph.D. in Chemical Engineering at The University of California, Berkeley
August 1, 2017 - August 1, 2023
B.Sc.(Hons) in Chemical Engineering, Minor in Mathematics at The Pennsylvania State University
August 1, 2013 - May 1, 2017

Qualifications

Add your qualifications or awards here.

Industry Experience

Education, Life Sciences, Computers & Electronics, Professional Services
    paper Open Molecules 2025

    I collaborate with Meta FAIR to revolutionize atomic-scale design through machine learning models that predict molecular behavior, dramatically accelerating discovery processes that traditionally take decades. We released Open Molecules 2025 (OMol25), a groundbreaking dataset the largest, most diverse of its kind that contains a collection of high-accuracy quantum chemistry calculations for biomolecules, metal complexes, and electrolytes, enabling unprecedented accuracy in atomic-scale design for healthcare and energy storage technologies.

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