For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and in an automated fashion. These approaches differ in their application range, the level of completeness of the exploration, and the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.

Exploration of Reaction Pathways and Chemical Transformation Networks

To date, the program for the development of methods and models for atomistic and continuum simulation directed toward chemicals and materials has reached an incredible degree of sophistication and maturity. Currently, one can witness an increasingly rapid emergence of advances in computing, artificial intelligence, and robotics. This drives us to consider the future of computer simulation of matter from the molecular to the human length and time scales in a radical way that deliberately dares to go beyond the foreseeable next steps in any given discipline. This perspective article presents a view on this future development that we believe is likely to become a reality during our lifetime.

The Matter Simulation (R)evolution

While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.

Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials.

Molecular dynamics (MD) has experienced a significant growth in the recent decades. Simulating systems consisting of hundreds of thousands of atoms is a routine task of computational chemistry researchers nowadays. Thanks to the straightforwardly parallelizable structure of the algorithms, the most promising method to speed‐up MD calculations is exploiting the large‐scale processing power offered by the parallel hardware architecture of graphics processing units or GPUs. Programming GPUs is becoming easier with general‐purpose GPU computing frameworks and higher levels of abstraction. In the recent years, implementing MD simulations on graphics processors has gained a large interest, with multiple popular software packages including some form of GPU‐acceleration support. Different approaches have been developed regarding various aspects of the algorithms, with important differences in the specific solutions. Focusing on published works in the field of classical MD, we describe the chosen implementation methods and algorithmic techniques used for porting to GPU, as well as how recent advances of GPU architectures will provide even more optimization possibilities in the future.

Classical molecular dynamics on graphics processing unit architectures

For most applications, taking full advantage of the memory system is key to achieving good performance on GPUs. In this paper, we introduce register caching, a novel idea where registers of multiple threads are combined and used as a shared, last level, manually managed cache for the contributing threads. This method is enabled by the shuffle instruction recently introduced in Nvidia's Kepler GPU architecture, which allows threads in the same warp to exchange data directly, previously only possible by going through shared memory. We evaluate our proposal with a stencil computation benchmark, achieving speedups of up to 2.04, compared to using shared memory on a GTX680 GPU. Stencil computations form the core of many scientific applications, which can therefore benefit from our proposal. Furthermore, our method is not limited to stencil computations, but is applicable to any application with a predictable memory access pattern suitable for manual caching.

Register Caching for Stencil Computations on GPUs

In this article, we describe an improved cell-list approach designed to match the Kepler architecture of General-purpose graphics processing units (GPGPU). We explain how our approach improves load balancing for the above algorithm and how warp intrinsics are used to implement Newton's third law for the nonbonded force calculations. We also talk through our approach to exclusions handling together with a method to calculate bonded forces and 1–4 electrostatic scaling using a single Cuda kernel. Performance benchmarks are included in the last sections to show the linear scaling of our implementation using a step minimization method. In addition, multiple performance benchmarks demonstrate the contribution of various optimizations we used for our implementations.

GPU-accelerated molecular mechanics computations.

Drug design is a creative process that combines different scientific expertise. With the development of increasingly powerful computers, disciplines such as molecular modeling and, in particular, drug design, are becoming an important component of drug discovery. However, modern software often limits the user interaction with the computer calculation, reducing the potential for researchers to use their knowledge in the design process. For this reason, interactive methodologies have been investigated in recent years. In particular, haptic-driven simulators offer the possibility for users to drive and control the modeling simulations, efficiently combining human knowledge and computational power. In this article, we will discuss the state-of-the-art and future perspectives of such methodologies.

Haptic-driven applications to molecular modeling: state-of-the-art and perspectives

In this paper, we describe a hybrid meta-heuristics method of energy minimization and conformational sampling and its application into our haptic-driven molecular modelling simulator. The proposed method has been designed to suit real-time molecular docking simulations, where the time-lapse between two successive ligand poses is relatively small. In these situations, the energy minimization problem becomes increasingly complex and chaotic. The algorithm is tuned to take advantage of recent advances in GPU computing with asynchronous kernel execution, which has allowed us to include full protein flexibility in the real-time interactive, haptic-driven simulations. Finally, in this paper, we will also discuss the implementation of such high-performance computing approaches in our software, discussing the results of our initial validation studies, highlighting the advantages and limitations of such interactive methodology.

/pdf/haptic-driven-interactive-drug-design-implementing-a-gpu-bmfru08q5t.pdf

Haptic-driven, interactive drug design: implementing a GPU-based approach to evaluate the induced fit effect.

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GPU-accelerated molecular mechanics computations.

Haptic-driven applications to molecular modeling: state-of-the-art and perspectives

Haptic-driven, interactive drug design: implementing a GPU-based approach to evaluate the induced fit effect.