The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …

Large language models (LLMs) have demonstrated tremendous capabilities in solving
complex tasks, from quantitative reasoning to understanding natural language. However …

PySR is an open-source library for practical symbolic regression, a type of machine learning
which aims to discover human-interpretable symbolic models. PySR was developed to …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Symbolic regression (SR) is a challenging task in machine learning that involves finding a
mathematical expression for a function based on its values. Recent advancements in SR …

This paper is dedicated to the quantum chemical package Jaguar, which is commercial
software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific …

Symbolic regression (SR) is the problem of learning a symbolic expression from numerical
data. Recently, deep neural models trained on procedurally-generated synthetic datasets …

Approximate density functional theory has become indispensable owing to its balanced cost–
accuracy trade-off, including in large-scale screening. To date, however, no density …

Identification of nonlinear dynamical systems using data-driven frameworks facilitates the
prediction and control of systems in a range of applications. Identification of a single system …

Anisotropic patchy particles have become an archetypical statistical model system for
associating fluids. Here, we formulate an approach to the Kern–Frenkel model via the …