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  • Selected Preprints
  • Selected Publications
    • 2D-ProteinRAG: Mitigating Intent-Context Misalignment in Homology-Based Protein Question Answering
    • Adaptive multigrid strategy for geometry optimization of large-scale three dimensional molecular mechanics
    • An AI-Ready Fine-Tuning Framework for Accurate Machine-Learning Interatomic Potentials in Solid–Solid Battery Interfaces
    • Autonomous Knowledge Distillation for an AI-Ready Knowledge Layer
    • Configuration Selection and Matrix-Aware Optimization for Data-Efficient Atomistic Foundation Models
    • Construction and a Priori Analysis of a Linear Elasticity Enhanced Multiscale Coupling Method for a Defective Atomistic Chain
    • Evaluating Universal Interatomic Potentials for Metallic Systems: From Elastic Response to Defect Energetics and Ideal Strength
    • Flexible Boundary Sequential Coupling for Atomistic Simulation of Crystal Defects
    • Geometry-conditioned basin initialization for variational quantum eigensolvers in strongly correlated molecules
    • Higher-Order Boundary Conditions for Atomistic Dislocation Simulations
    • NanoTitan: An AI-Driven Memory-Efficient Platform for High-Performance Molecular Dynamics Simulation
    • TensorSketch-Compressed High-Order Atomic Cluster Expansion Potentials: Accuracy, Mechanics, and Vibrational Thermodynamics
    • A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
    • A Discrete-Time Random Feature Method for Nonlinear Evolution Equations with Implicit-Explicit Runge–Kutta Time Stepping
    • A Foundation Model for Atomistic Materials Chemistry
    • A Framework for a Generalisation Analysis of Machine-learned Interatomic Potentials
    • A Posteriori Analysis and Adaptive Algorithms for Blended Type Atomistic-to-Continuum Coupling with Higher-Order Finite Elements
    • A Posteriori Error Estimate and Adaptivity for QM/MM Models of Crystalline Defects
    • A Posteriori Error Estimates for Adaptive QM/MM Coupling Methods
    • A Priori Analysis of a Higher Order Nonlinear Elasticity Model for an Atomistic Chain with Periodic Boundary Condition
    • A Structure-Adaptive Random Feature Method for High-Dimensional Elliptic PDEs
    • A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs)
    • A Theoretical Case Study of the Generalisation of Machine-learned Potentials
    • Adaptive QM/MM Coupling for Crystalline Defects
    • Amplitude Expansion Phase Field Crystal (APFC) Modeling based Efficient Dislocation Simulations using Fourier Pseudospectral Method
    • An Atomic Cluster Expansion Potential for Twisted Multilayer Graphene
    • Analysis of Hessian Variance Scaling for Local and Global Cost Functions in Variational Quantum Algorithms
    • Efficient a Posteriori Error Control of a Consistent Atomistic/Continuum Coupling Method for Two Dimensional Crystalline Defects
    • Elastic Far-field Decay from Dislocations in Multilattices
    • Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
    • Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
    • Formulation and Analysis of Blended Atomistic to Higher-Order Continuum Coupling Methods for Crystalline Defects
    • From Energy-Force Weighting to Primal-Dual Optimization of Machine-Learned Interatomic Potentials
    • Geometry-Preserving Nudged Elastic Band and Dimer Methods under Anisotropic Force Uncertainty
    • Higher Order Far-Field Boundary Conditions for Crystalline Defects
    • IADR: Interface-Augmented Neural Operator for Phase-Field Mean-Curvature Flow
    • LDD-RFM: Learnable Domain Decomposition for Random Feature Models via Variable Projection
    • Liquid Random Feature Methods for Time-Dependent Partial Differential Equations
    • Local Surrogates for Harmonic Vibrational Entropy in Multilattices
    • Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion
    • Mathematical Modeling of Materials at the Atomic Scale
    • MeshAC: A 3D Mesh Generation and Adaptation Package for Multiscale Coupling Methods
    • MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction
    • Moment-Informed Force Rescaling for Random-Batch Langevin Dynamics
    • Optimal Sobolev Approximation by Deterministic and Random Shallow Sigmoidal Networks
    • QM/MM Methods for Crystalline Defects. Part 3: Machine-learned MM Models
    • Random Feature Kalman Filtering for Linear PDE Data Assimilation
    • Residual-Christoffel Sampling for Random Feature Collocation of Linear PDEs
    • Some Progress on Multi-scale Methods in Materials Modeling
    • Surrogate models for vibrational entropy based on a spatial decomposition
    • Trainability-Oriented Hybrid Quantum Regression via Geometric Preconditioning and Curriculum Optimization
    • Trainable Photonic Measurement for Physics-Informed PDE Learning
  • Talks & Conferences
    • Workshop on Mathematical Theory, Methods, and Applications in Materials Simulations (Tianyuan Mathematical Center in Science and Technology, TMCST)
    • Computational Multiscale Methods
    • 22nd Annual Conference of China Society for Industrial and Applied Mathematics (CSIAM 2024)
    • International Workshop on Data-driven Computational and Theoretical Materials Design (DCTMD)
    • Numerical Methods, Mathematical Modeling and Analysis in Materials Science
    • Machine Learning in Multiscale and Reduced Order Methods for the Simulation of Physical Systems (International Conference on Scientific Computation and Differential Equations, SciCADE 2024)
    • Advancing Molecular Simulations with Machine-Learned Interatomic Potentials
    • Advancing Molecular Simulations with Machine-Learned Interatomic Potentials
    • Using Uncertainty Quantification to Improve Learning in Atomistic Modeling (SIAM Conference on Mathematical Aspects of Materials Science)
    • Machine Learning Force Fields, Data-Driven Materials Informatics (Institute for Mathematical and Statistical Innovation)
    • Advancing Machine-Learned Interatomic Potentials: Enhancing Accuracy and Robustness in Materials Science Applications (Data Science Seminar)
    • Enhancing Machine-Learned Interatomic Potentials in Materials Science: Progressing from Accuracy to Robustness (Hot Topics in AI for Sciences)
    • Application of machine-learned interatomic potentials in atomic-scale simulations and beyond (Model Reduction and Simulation at Atomic Scale)
    • Analysis, Methods and Applications in Complex Materials
    • Mathematical Modeling, Analysis and Applications of Machine-Learned Interatomic Potentials
    • Atomic Cluster Expansion with and without Atoms
    • A Framework for a Generalisation Analysis of Machine-Learned Interatomic Potentials (Atomic Cluster Expansion (ACE) Seminar)
    • A Framework for a Generalisation Analysis of Machine-Learned Interatomic Potentials (online) (Computational Methods in Applied Mathematics, CMAM 2022)
    • Error Propagation of Machine-Learned Interatomic Potentials (MLIPs) (Machine-learned Interatomic Potentials Mini-workshop)
    • A Posteriori Error Estimates for Adaptive QM/MM Coupling Methods (Workshop on Computational Materials Science)
    • The Applications of Data-Driven Interatomic Potentials to QM/MM Coupling Methods (International Workshop on Mathematical Theory, Methods and Application in Materials Simulation)
    • Adaptive QM/MM Coupling Methods (Workshop on Materials Modeling, University of Warwick-Shanghai Jiao Tong University-University of British Columbia)
    • Some Recent Progress on Multiscale Coupling Methods (Top-notch Doctoral Seminar in Computational and Applied Mathematics)
  • Teaching

Advancing Molecular Simulations with Machine-Learned Interatomic Potentials

Jun 13, 2024·
Yangshuai Wang
Yangshuai Wang
conferences
Last updated on Jun 13, 2024
Yangshuai Wang
Authors
Yangshuai Wang
Peng Tsu Ann Assistant Professor (Visiting Fellow)
← Advancing Molecular Simulations with Machine-Learned Interatomic Potentials Jul 3, 2024
Using Uncertainty Quantification to Improve Learning in Atomistic Modeling (SIAM Conference on Mathematical Aspects of Materials Science) May 19, 2024 →

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