arXiv subjects

Samiha Sharlin

Publications and source records attributed to Samiha Sharlin.

4 recordsLinked to original sources

arXiv2026

A reaction volume bias Monte Carlo trial for sampling chemisorption in confinement

Molecular modeling of chemisorption with Monte Carlo requires the development of new trial moves to efficiently sample complex fluids such as water in Bronsted acid zeolites. Here, we develop a reaction volume bias (RxVB) Monte Carlo trial for modeling chemisorption by combining identity-switch and aggregation-volume-bias (AVB) moves. This method aims to promote the sampling of reactions by choosing reactive pairs that are within an arbitrarily specified reaction volume. The RxVB move achieves up to a 90-fold increase in accepted reaction events over unbiased moves in a single-site slit-pore model, corresponding to a 70-fold gain in statistical efficiency after accounting for computational overhead. But when the same move is applied to water in a Bronsted acid zeolite without orientational bias, there is no measurable speedup for a single MFI unit cell. We demonstrate a simple expression that predicts the maximum efficiency increase in the simplest case where selecting reactants that are near each other is the major sampling bottleneck. Dense water systems may require additional configuration-bias or orientational bias to improve sampling of the hydrogen bond network in order to increase acceptance. This new RxVB trial was made available with examples in the open-source Free Energy and Advanced Sampling Simulation Toolkit (FEASST) simulation package.

physics.chem-ph↗

arXiv2024

In Context Learning and Reasoning for Symbolic Regression with Large Language Models

Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not explicitly trained. Here, we explore the potential of LLMs to perform symbolic regression -- a machine-learning method for finding simple and accurate equations from datasets. We prompt GPT-4 and GPT-4o models to suggest expressions from data, which are then optimized and evaluated using external Python tools. These results are fed back to the LLMs, which propose improved expressions while optimizing for complexity and loss. Using chain-of-thought prompting, we instruct the models to analyze data, prior expressions, and the scientific context (expressed in natural language) for each problem before generating new expressions. We evaluated the workflow in rediscovery of Langmuir and dual-site Langmuir's model for adsorption, along with Nikuradse's dataset on flow in rough pipes, which does not have a known target model equation. Both the GPT-4 and GPT-4o models successfully rediscovered equations, with better performance when using a scratchpad and considering scientific context. GPT-4o model demonstrated improved reasoning with data patterns, particularly evident in the dual-site Langmuir and Nikuradse dataset. We demonstrate how strategic prompting improves the model's performance and how the natural language interface simplifies integrating theory with data. We also applied symbolic mathematical constraints based on the background knowledge of data via prompts and found that LLMs generate meaningful equations more frequently. Although this approach does not outperform established SR programs where target equations are more complex, LLMs can nonetheless iterate toward improved solutions while following instructions and incorporating scientific context in natural language.

cs.CL↗

arXiv2023

Formalizing Chemical Physics using the Lean Theorem Prover

Chemical theory can be made more rigorous using the Lean theorem prover, an interactive theorem prover for complex mathematics. We formalize the Langmuir and BET theories of adsorption, making each scientific premise clear and every step of the derivations explicit. Lean's math library, mathlib, provides formally verified theorems for infinite geometries series, which are central to BET theory. While writing these proofs, Lean prompts us to include mathematical constraints that were not originally reported. We also illustrate how Lean flexibly enables the reuse of proofs that build on more complex theories through the use of functions, definitions, and structures. Finally, we construct scientific frameworks for interoperable proofs, by creating structures for classical thermodynamics and kinematics, using them to formalize gas law relationships like Boyle's Law and equations of motion underlying Newtonian mechanics, respectively. This approach can be extended to other fields, enabling the formalization of rich and complex theories in science and engineering.

cs.LO↗

arXiv2023

Incorporating Background Knowledge in Symbolic Regression using a Computer Algebra System

Symbolic Regression (SR) can generate interpretable, concise expressions that fit a given dataset, allowing for more human understanding of the structure than black-box approaches. The addition of background knowledge (in the form of symbolic mathematical constraints) allows for the generation of expressions that are meaningful with respect to theory while also being consistent with data. We specifically examine the addition of constraints to traditional genetic algorithm (GA) based SR (PySR) as well as a Markov-chain Monte Carlo (MCMC) based Bayesian SR architecture (Bayesian Machine Scientist), and apply these to rediscovering adsorption equations from experimental, historical datasets. We find that, while hard constraints prevent GA and MCMC SR from searching, soft constraints can lead to improved performance both in terms of search effectiveness and model meaningfulness, with computational costs increasing by about an order-of-magnitude. If the constraints do not correlate well with the dataset or expected models, they can hinder the search of expressions. We find Bayesian SR is better these constraints (as the Bayesian prior) than by modifying the fitness function in the GA

cs.LG↗