Can chemistry be derived from quantum mechanics? Chemical dynamics and structure

Ryan Michael Miller

Abstract The question of whether chemical structure is reducible to Everettian Quantum Mechanics (EQM) should be of interest to philosophers of chemistry and philosophers of physics alike. Among the three realist interpretations of quantum mechanics, EQM resolves the measurement problem by claiming that measurements (now interpreted as instances of decoherence) have indeterminate outcomes absolutely speaking, but determinate outcomes relative to emergent worlds—Maudlin (Topoi, 14:7-15, 1995). Philosophers who wish to be sensitive to the practice of quantum chemistry e.g. Scerri (The changing views of a philosopher of chemistry on the question of reduction, 2016) should be interested in EQM because Franklin and Seifert (J. Philos. Sci, 2020) claim that resolving the measurement problem also resolves the reducibility of chemical structure, and EQM is the interpretation which involves no mathematical structure beyond that used by practicing scientists. Philosophers interested in the quantum interpretation debate should be interested in the reducibility of chemistry because chemical structure is precisely the kind of determinate three-dimensional fact which EQM should be able to ground if it is to be empirically coherent—see Allori (Quantum Rep, 5:80-101, 2023). The prospects for reduction of chemical structure are poor if it cannot succeed in EQM; the prospects for EQM as a guide to ontology are poor if it cannot reduce chemical structure. Unfortunately for proponents of chemical reduction and EQM, there are three serious barriers to the reduction of chemistry to EQM. The first concern is that quantum treatments of chemical structure rely on the Born-Oppenheimer approximation, which holds nuclear locations fixed while minimizing the energy of the electronic configuration—Hendry (Philosophical Perspectives in Quantum Chemistry, 147-172, 2022), but this approximation is not licensed by EQM. The Born-Oppenheimer approximation relies on nuclei and molecular orbitals being simultaneously present, but in the three-dimensional ontology following from the Everett interpretation these only emerge at different energy scales and are not simultaneously present—Miller (Found. Chem, 25:405-417, 2023). The second concern is that the emergent worlds of EQM are supposed to be decoherent at the macro-scale—Wilson (The Nature of Contingency: Quantum Physics as Modal Realism 2020), but the recent development of superchemistry suggests that chemical reactions can occur in coherent states—Zhang et al. (Nat. Phys, 1-5, 2023). The third concern is that emergent worlds are only pragmatic pseudo-processes—Wallace (The Emergent Multiverse: Quantum Theory According to the Everett Interpretation, 2012b), but this means EQM trades realist physics for mere instrumentalism about chemistry. Absent a commitment to chemical realism, reduction is an empty promise. The prospects for reduction of chemical structure to EQM are therefore poor.

Anton Amann

Sadia Rafi, Rana Muhammad Ali Maisam, Anum Zahid +3 more

Machine learning has made significant contributions to the fields of chemistry and materials science, enabling the exploration of vast chemical space through large-scale quantum chemical calculations. These models provide fast and accurate predictions of atomistic chemical properties, but they have limitations when it comes to capturing the electronic degrees of freedom of a molecule. This restricts their application in reactive chemistry and chemical analysis. To address this limitation, we introduce a deep learning framework that predicts the quantum mechanical wavefunction of a molecule in a local basis of atomic orbitals. The wavefunction serves as a foundational representation from which all other ground-state properties can be derived. Our approach maintains complete access to the electronic structure through the wavefunction, while achieving computational efficiency comparable to force-field methods. Moreover, the framework captures quantum mechanics in a form that can be analytically differentiated, allowing for efficient optimization and exploration of chemical systems. We demonstrate the potential of our approach through several examples. By leveraging the predicted wavefunction, we showcase the ability to perform inverse design of molecular structures to target specific electronic property optimizations. This opens exciting avenues for tailoring molecular structures to achieve desired electronic characteristics. Additionally, our framework paves the way for enhanced synergy between machine learning and quantum chemistry, enabling more comprehensive investigations into complex chemical systems.

Thomas Freeman Babson

Seogjoo Jang

Jincheng Zhang

Quantum Chemistry Simulation Adaptive Optimization is a novel approach to molecular structure optimization and chemical reaction simulation, leveraging the unique capabilities of quantum chemistry simulations to achieve significantly improved accuracy and efficiency. This paper presents a comprehensive investigation of the algorithm's design, implementation, and performance evaluation, demonstrating its potential to surpass conventional methods. The core mechanism centers around a dynamically adjusted optimization strategy, guided by quantum chemical calculations, to iteratively refine molecular structures and accelerate simulation cycles. The algorithm addresses limitations of traditional methods by offering a more intuitive and automated process.