Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification

Swinburne, Thomas D.; Pérez, Danny

doi:10.1103/physrevmaterials.2.053802

Cited by 29 publications

(53 citation statements)

References 39 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…7a-e). The minimum mismatch supercell commensurate with the initial and final structures consists of 16 Li atoms and was constructed by transforming the FCC structure in BCC settings (i.e., 1 × 1 × 2 replication of a x ′ = ½(a x + a y ) and a y ′ = ½(a x − a y ), see inset of Fig. 8).…”

Section: Applications On Unsolved Systemsmentioning

confidence: 99%

“…The cI16 structure is very complex, and the selection of a suitable path for the transformation a priori is not obvious. It is noteworthy that, in principle, the number of possible transition paths is~3 × 10 16 .…”

Section: Applications On Unsolved Systemsmentioning

confidence: 99%

“…The search strategy generally consists of two parts: saddle point searching and transition path searching. For the first part, the string method 4 , nudged elastic band (NEB) method 5 , doubly NEB method [6][7][8] , climbing image-NEB method 9 , and solid-state NEB method 10 have been used to locate the saddle point on the potential energy surface (PES) of a given transition path [14][15][16][17][18][19][20][21][22][23] . The second part consists of a search for the lowest-energy path connecting the initial and final structures.…”

Section: Introductionmentioning

confidence: 99%

“…npj Computational Materials (2020)16 Published in partnership with the Shanghai Institute of Ceramics of the Chinese Academy of Sciences…”

mentioning

confidence: 99%

See 3 more Smart Citations

An automated predictor for identifying transition states in solids

et al. 2020

View full text Add to dashboard Cite

The minimum energy path (MEP) and transition state are two key parameters in the investigation of the mechanisms of chemical reactions and structural phase transformations. However, determination of transition paths in solids is challenging. Here, we present an evolutionary method to search for the lowest energy path and the transition state for pressure-induced structural transformations in solids without any user input or prior knowledge of possible paths. Instead, the initial paths are chosen stochastically by connecting randomly selected atoms from the initial to final structure. The MEP of these trials paths were computed and ranked in order of their energies. The matrix particle swarm optimization algorithm is then used to generate improved transition paths. The procedure is repeated until the lowest energy MEP is found. This method is validated by reproducing results of several known systems. The new method also successfully located the MEP for the direct low-temperature pressure induced transformation of face centered-cubic (FCC) silicon to the simple hexagonal(sh) phase and FCC lithium to a complex body centered-cubic cI16 high-pressure phase. The proposed method provides a convenient, robust, and reliable approach to identify the MEP of phase transformations. The method is general and applicable to a variety of problems requiring the location of the transition state.

show abstract

Section: Applications On Unsolved Systemsmentioning

confidence: 99%

Section: Applications On Unsolved Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…npj Computational Materials (2020)16 Published in partnership with the Shanghai Institute of Ceramics of the Chinese Academy of Sciences…”

mentioning

confidence: 99%

See 2 more Smart Citations

An automated predictor for identifying transition states in solids

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The setting of Parsplice [46,47,54] is slightly different from the above. In Par-Splice, a splicer tells a producer to generate fragments among several metastable sets.…”

Section: Trajectory Fragmentsmentioning

confidence: 99%

Generalizing Parallel Replica Dynamics: Trajectory Fragments, Asynchronous Computing, and PDMPs

Aristoff¹

2019

SIAM/ASA J. Uncertainty Quantification

View full text Add to dashboard Cite

We study the Parallel Replica Dynamics in a general setting. We introduce a trajectory fragment framework that can be used to design and prove consistency of Parallel Replica algorithms for generic Markov processes. We use our framework to formulate a novel condition that guarantees an asynchronous algorithm is consistent. Exploiting this condition and our trajectory fragment framework, we present new synchronous and asynchronous Parallel Replica algorithms for piecewise deterministic Markov processes. ) 2. Notation. Throughout, X(t) t≥0 is a time homogeneous Markov process, either discrete or continuous in time, with values in a standard Borel state space; U is a subset of state space; and g is a real-valued function defined on state space. Without explicit mention we assume all sets are measurable and all functions are bounded and measurable. We write X(t) to refer to the process X(t) t≥0 at time t. We denote various expectations and probabilities by E and P, with the precise meaning being clear from context. We write L for the probability law of a random object, with L above an equals sign indicating equality in law. We say a random object is a copy of another random object if it has the same law as that object. When we say a collection of random objects is independent we mean these objects are mutually independent unless otherwise specified. We define a ∧ b = min{a, b} and a ∨ b = max{a, b}, and write ⌊s⌋ for the greatest integer less than or equal to s. Metastability.Informally, U is a metastable set for X(t) t≥0 if X(t) t≥0 tends to reach a local equilibrium in U much faster than it escapes from U . Local equilibrium can be understood in terms of quasistationary distributions (QSDs):Definition 3.1. Fix a subset U of state space, and considerfor every t ≥ 0 and A ⊆ U .Note that ρ is supported in U . Equation (3.1) states that if X(0) is distributed as ρ and X(t) t≥0 does not escape from U by time t, then X(t) is distributed as ρ. Throughout, we will assume the QSD of X(t) t≥0 in U exists, is unique, and is the long time distribution of X(t) conditioned to never escape U . That is, we assume that for any initial distribution of X(0) supported in U , (3.2) ρ(A) = lim t→∞ P(X(t) ∈ A|X(s) ∈ U for s ∈ [0, t]) ∀ A ⊆ U.The QSD ρ can then be sampled as follows: choose a time T ρ corr (U ) for relaxation to ρ. Start X(t) t≥0 in U , and if it escapes from U before time t = T ρ corr (U ), restart it

show abstract

Recent advances in Accelerated Molecular Dynamics Methods: Theory and Applications

Pérez

Lelièvre

2024

Comprehensive Computational Chemistry

View full text Add to dashboard Cite

Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification

Cited by 29 publications

References 39 publications

An automated predictor for identifying transition states in solids

An automated predictor for identifying transition states in solids

Generalizing Parallel Replica Dynamics: Trajectory Fragments, Asynchronous Computing, and PDMPs

Recent advances in Accelerated Molecular Dynamics Methods: Theory and Applications

Contact Info

Product

Resources

About