Selected work

Representative Papers

Learning consistent molecular mechanics force fields from first principles

Oct 06, 2026

Traditional molecular force fields rely on empirical non-bonded parameters, limiting their generalizability to complex configurations. This work proposes grappa-fullFF, a model that jointly learns bonded and non-bonded interaction parameters from first-principles data. By incorporating electrostatic potential-supervised regularization and a charge-balancing architecture, the approach ensures parameter consistency and physical interpretability. The proposed method achieves state-of-the-art accuracy on geometry optimization benchmarks and faithfully reproduces molecular conformational sampling without relying on external non-bonded parameters. Ultimately, this study establishes a new paradigm for constructing highly accurate, fully parameterized machine learning force fields.

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A strategic roadmap for an atomistic machine-learning ecosystem

Sep 30, 2026

This work addresses the challenges of modeling choices, software interoperability, and hardware acceleration in deploying machine learning ensembles for atomistic simulations. Building upon outcomes from a CECAM workshop, it presents the first systematic integration of physical modeling frameworks with modern deep learning techniques. By synergizing first-principles calculations, molecular dynamics, and statistical sampling with contemporary hardware accelerators, this project establishes interdisciplinary collaborative mechanisms to balance accuracy, efficiency, and scalability. The primary deliverable is a strategic roadmap encompassing both long-term objectives and concrete actions, designed to drive the co-evolution of algorithms, models, and software-hardware infrastructure. Ultimately, this initiative fosters unified community planning and efficient collaboration, setting foundational standards for establishing a robust and sustainable machine learning ecosystem at the atomic scale.

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Recent publications

Latest Papers

Learning consistent molecular mechanics force fields from first principles

Oct 06, 2026

Traditional molecular force fields rely on empirical non-bonded parameters, limiting their generalizability to complex configurations. This work proposes grappa-fullFF, a model that jointly learns bonded and non-bonded interaction parameters from first-principles data. By incorporating electrostatic potential-supervised regularization and a charge-balancing architecture, the approach ensures parameter consistency and physical interpretability. The proposed method achieves state-of-the-art accuracy on geometry optimization benchmarks and faithfully reproduces molecular conformational sampling without relying on external non-bonded parameters. Ultimately, this study establishes a new paradigm for constructing highly accurate, fully parameterized machine learning force fields.

0 citationsRead paper

A strategic roadmap for an atomistic machine-learning ecosystem

Sep 30, 2026

This work addresses the challenges of modeling choices, software interoperability, and hardware acceleration in deploying machine learning ensembles for atomistic simulations. Building upon outcomes from a CECAM workshop, it presents the first systematic integration of physical modeling frameworks with modern deep learning techniques. By synergizing first-principles calculations, molecular dynamics, and statistical sampling with contemporary hardware accelerators, this project establishes interdisciplinary collaborative mechanisms to balance accuracy, efficiency, and scalability. The primary deliverable is a strategic roadmap encompassing both long-term objectives and concrete actions, designed to drive the co-evolution of algorithms, models, and software-hardware infrastructure. Ultimately, this initiative fosters unified community planning and efficient collaboration, setting foundational standards for establishing a robust and sustainable machine learning ecosystem at the atomic scale.

0 citationsRead paper