Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials.

Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experimental methods. In this review, we highlight some of the important gaps between computational simulations and experiments, discuss popular state-of-the-art computational techniques such as the Activation Relaxation Technique (ARTn) and Reverse Monte Carlo (RMC), and introduce more recent advances: machine learning interatomic potentials (MLIPs) and generative machine learning for simulations of amorphous matter (e.

Path-filtering in path-integral simulations of open quantum systems using GFlowNets.

An important class of methods for modeling dynamics in open quantum systems is based on the well-known influence functional (IF) approach to solving path-integral equations of motion. Within this paradigm, path-filtering schemes based on the removal of IF elements that fall below a certain threshold aim to reduce the effort needed to calculate and store the influence functional, making very challenging simulations possible. A filtering protocol of this type is considered acceptable as long as the simulation remains mathematically stable.

Truncations and in silico docking to enhance the analytical response of aptamer-based biosensors.

Aptamers are short oligonucleotides capable of binding specifically to various targets (i.e., small molecules, proteins, and whole cells) which have been introduced in biosensors such as in the electrochemical aptamer-based (E-AB) sensing platform.

DeltaGzip: Computing Biopolymer-Ligand Binding Affinity via Kolmogorov Complexity and Lossless Compression.

The design of biosequences for biosensing and therapeutics is a challenging multistep search and optimization task. In principle, computational modeling may speed up the design process by virtual screening of sequences based on their binding affinities to target molecules. However, in practice, existing machine-learned models trained to predict binding affinities lack the flexibility with respect to reaction conditions, and molecular dynamics simulations that can incorporate reaction conditions suffer from high computational costs.

Amodiaquine Nonspecifically Binds Double Stranded and Three-Way Junction DNA Structures.

The 4-aminoquinoline class of compounds includes the important antimalarial compounds amodiaquine and chloroquine. Despite their medicinal importance, the mode of action of these compounds is poorly understood. In a previous study we observed these compounds, as well as quinine and mefloquine, tightly bind the DNA cocaine-binding aptamer.

Simulations of disordered matter in 3D with the morphological autoregressive protocol (MAP) and convolutional neural networks.

Disordered molecular systems, such as amorphous catalysts, organic thin films, electrolyte solutions, and water, are at the cutting edge of computational exploration at present. Traditional simulations of such systems at length scales relevant to experiments in practice require a compromise between model accuracy and quality of sampling. To address this problem, we have developed an approach based on generative machine learning called the Morphological Autoregressive Protocol (MAP), which provides computational access to mesoscale disordered molecular configurations at linear cost at generation for materials in which structural correlations decay sufficiently rapidly.

Solving the Wigner equation for chemically relevant scenarios: Dynamics in 2D.

The signed particle Monte Carlo (SPMC) approach has been used in the past to model steady-state and transient dynamics of the Wigner quasi-distribution for electrons in low-dimensional semiconductors. Here, we make a step toward high-dimensional quantum phase-space simulation in chemically relevant scenarios by improving the stability and memory demands of SPMC in 2D. We do so by using an unbiased propagator for SPMC to improve trajectory stability and applying machine learning to reduce memory demands for storage and manipulation of the Wigner potential.

Diversifying Design of Nucleic Acid Aptamers Using Unsupervised Machine Learning.

Inverse design of short single-stranded RNA and DNA sequences (aptamers) is the task of finding sequences that satisfy a set of desired criteria. Relevant criteria may be, for example, the presence of specific folding motifs, binding to molecular ligands, sensing properties, and so on. Most practical approaches to aptamer design identify a small set of promising candidate sequences using high-throughput experiments (e.

In situ optical spectroscopy of crystallization: One crystal nucleation at a time.

While crystallization is a ubiquitous and an important process, the microscopic picture of crystal nucleation is yet to be established. Recent studies suggest that the nucleation process can be more complex than the view offered by the classical nucleation theory. Here, we implement single crystal nucleation spectroscopy (SCNS) by combining Raman microspectroscopy and optical trapping induced crystallization to spectroscopically investigate one crystal nucleation at a time.

Optoelectronic Current through Unbiased Monolayer Amorphous Carbon Nanojunctions.

Monolayer amorphous carbon (MAC) is a recently synthesized disordered 2D carbon material. An ensemble of MAC nanofragments contains diverse manifestations of lattice disorder, and because of disorder the key unifying characteristic of this ensemble is poor electronic conductance. Curiously, our computational analysis of the electronic properties of MAC nanofragments revealed an additional commonality: a robust presence of charge-transfer character for electronic transitions from the occupied to virtual orbitals.

Electronic Conduction through Monolayer Amorphous Carbon Nanojunctions.

In molecular electronic conduction, exotic lattice morphologies often give rise to exotic behaviors. Among 2D systems, graphene is a notable example. Recently, a stable amorphous version of graphene called monolayer amorphous carbon (MAC) was synthesized.

E2EDNA: Simulation Protocol for DNA Aptamers with Ligands.

We present E2EDNA, a simulation protocol and accompanying code for the molecular biophysics and materials science communities. This protocol is both easy to use and sufficiently efficient to simulate single-stranded (ss)DNA and small analyte systems that are important to cellular processes and nanotechnologies such as DNA aptamer-based sensors. Existing computational tools used for aptamer design focus on cost-effective secondary structure prediction and motif analysis in the large data sets produced by SELEX experiments.

Solving the Wigner equation with signed particle Monte Carlo for chemically relevant potentials.

Expanding the set of stable, accurate, and scalable methods for simulating molecular quantum dynamics is important for accelerating the computational exploration of molecular processes. In this paper, we adapt the signed particles Monte Carlo algorithm for solving the transient Wigner equation to scenarios of chemical interest. This approach was used in the past to study electronic processes in semi-conductors, but to the best of our knowledge, it had never been applied to molecular modeling.

Generating Multiscale Amorphous Molecular Structures Using Deep Learning: A Study in 2D.

Amorphous molecular assemblies appear in a vast array of systems: from living cells to chemical plants and from everyday items to new devices. The absence of long-range order in amorphous materials implies that precise knowledge of their underlying structures throughout is needed to rationalize and control their properties at the mesoscale. Standard computational simulations suffer from exponentially unfavorable scaling of the required compute with system size.

Predicting optical spectra for optoelectronic polymers using coarse-grained models and recurrent neural networks.

Coarse-grained modeling of conjugated polymers has become an increasingly popular route to investigate the physics of organic optoelectronic materials. While ultraviolet (UV)-vis spectroscopy remains one of the key experimental methods for the interrogation of these materials, a rigorous bridge between simulated coarse-grained structures and spectroscopy has not been established. Here, we address this challenge by developing a method that can predict spectra of conjugated polymers directly from coarse-grained representations while avoiding repetitive procedures such as back-mapping from coarse-grained to atomistic representations followed by spectral computation using quantum chemistry.

Electronic noise due to temperature differences in atomic-scale junctions.

Since the discovery a century ago of electronic thermal noise and shot noise, these forms of fundamental noise have had an enormous impact on science and technology research and applications. They can be used to probe quantum effects and thermodynamic quantities, but they are also regarded as undesirable in electronic devices because they obscure the target signal. Electronic thermal noise is generated at equilibrium at finite (non-zero) temperature, whereas electronic shot noise is a non-equilibrium current noise that is generated by partial transmission and reflection (partition) of the incoming electrons.

Direct observation of backbone planarization via side-chain alignment in single bulky-substituted polythiophenes.

The backbone conformation of conjugated polymers affects, to a large extent, their optical and electronic properties. The usually flexible substituents provide solubility and influence the packing behavior of conjugated polymers in films or in bad solvents. However, the role of the side chains in determining and potentially controlling the backbone conformation, and thus the optical and electronic properties on the single polymer level, is currently under debate.

Fluorescent Proteins Detect Host Structural Rearrangements via Electrostatic Mechanism.

The rational design of genetically encoded fluorescent biosensors, which can detect rearrangements of target proteins via interdomain allostery, is hindered by the absence of mechanistic understanding of the underlying photophysics. Here, we focus on the modulation of fluorescence by mechanical perturbation in a popular biological probe: enhanced Green Fluorescent Protein (eGFP). Using a combination of molecular dynamics (MD) simulations and quantum chemistry, and a set of physically motivated assumptions, we construct a map of fluorescence quantum yield as a function of a 2D electric field imposed by the protein environment on the fluorophore.

Relating Chromophoric and Structural Disorder in Conjugated Polymers.

The optoelectronic properties of amorphous conjugated polymers are sensitive to the details of the conformational disorder, and spectroscopy provides the means for structural characterization of the fragments of the chain that interact with light-"chromophores". A faithful interpretation of spectroscopic conformational signatures, however, presents a theoretical challenge. Here we investigate the relationship between the ground-state optical gaps, the properties of the excited states, and the structural features of chromophores of a single molecule poly(3-hexyl)-thiophene (P3HT) using quantum-classical atomistic simulations.

Electron transport in nanoscale junctions with local anharmonic modes.

We study electron transport in nanojunctions in which an electron on a quantum dot or a molecule is interacting with an N-state local impurity, a harmonic ("Holstein") mode, or a two-state system ("spin"). These two models, the Anderson-Holstein model and the spin-fermion model, can be conveniently transformed by a shift transformation into a form suitable for a perturbative expansion in the tunneling matrix element. We explore the current-voltage characteristics of the two models in the limit of high temperature and weak electron-metal coupling using a kinetic rate equation formalism, considering both the case of an equilibrated impurity, and the unequilibrated case.

Landau-Zener transitions mediated by an environment: population transfer and energy dissipation.

We study Landau-Zener transitions between two states with the addition of a shared discretized continuum. The continuum allows for population decay from the initial state as well as indirect transitions between the two states. The probability of nonadiabatic transition in this multichannel model preserves the standard Landau-Zener functional form except for a shift in the usual exponential factor, reflecting population transfer into the continuum.

Path-integral simulations with fermionic and bosonic reservoirs: transport and dissipation in molecular electronic junctions.

We expand iterative numerically exact influence functional path-integral tools and present a method capable of following the nonequilibrium time evolution of subsystems coupled to multiple bosonic and fermionic reservoirs simultaneously. Using this method, we study the real-time dynamics of charge transfer and vibrational mode excitation in an electron conducting molecular junction. We focus on nonequilibrium vibrational effects, particularly, the development of vibrational instability in a current-rectifying junction.

Vibrational cooling, heating, and instability in molecular conducting junctions: full counting statistics analysis.

We study current-induced vibrational cooling, heating, and instability in a donor-acceptor rectifying molecular junction using a full counting statistics approach. In our model, electron-hole pair excitations are coupled to a given molecular vibrational mode which is either harmonic or highly anharmonic. This mode may be further coupled to a dissipative thermal environment.